- Otago University Research Archive

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AKT1 AND THE NEGATIVE CORRELATION BETWEEN
RHEUMATOID ARTHRITIS AND SCHIZOPHRENIA
Natasha Anne Austin
A thesis submitted for the degree of Masters of Science in Genetics
at the University of Otago, Dunedin, New Zealand
Date: Wednesday 28th July 2010
ACKNOWLEDGEMENTS
I am most grateful to Associate Professor Tony Merriman, my fantastic supervisor for giving
me the chance to work on a topic close to my heart. His academic and moral support in all
areas has been quite amazing and I consider myself very lucky to work under a supervisor
who has so much time for his students. In particular, I am most appreciative for the time he
put in to providing in depth feedback and comments on the draft chapters of this thesis. Under
Tony’s supervision I felt encouraged and motivated every step of the way.
I would also like to give special mention to Jade and Fathin. Without these two wonderful
ladies I would have been lost in my first months in the lab. I am very grateful that they were
there to point me in the right direction with everything from bench work to statistics. Their
continued support has made my time in the Merriman lab as stress free as possible.
To Mandy and Marylyn, I am thankful for their help through the troubles with my mouse
primers and RFLP work. To Ruth, I would like to offer thanks for her time and patience in
assisting me with my SNPmax work. To all other members in the Merriman laboratory,
Mohan, Sara, Ange, Ani, Cushla, Xin, Rebecca D and Morgan I thank you for all your
contributions. I am grateful to everyone who gave up their time to guide me and help me.
Most of all thank you all for the friendly environment you provided.
To Reece, the loving support you have provided throughout this journey has been second to
none. Without you to encourage me to stick at it when things were not going according to
plan I may not have made it to the end.
I dedicate this thesis to my fantastic Whanau who have given me nothing but support
throughout my time at university. To mum, for encouraging me to be the best that I can be
and to Dad, who is unwavering in his love. To all my brothers and sisters, especially Darcy
whose daily struggle with rheumatoid arthritis inspired me try and do something about the
disease. Without my family I would not be who I am today and I am thankful for all they have
done for me.
I
ABSTRACT
Nearly 3% of the world’s population is affected by some form of autoimmune disease, with
current estimates of rheumatoid arthritis (RA) prevalence at 0.7-1% of Caucasians. This
degenerative disorder is characterized by chronic inflammation of the joints resulting in
irreversible joint damage and disability.
RA has a complex aetiology with both
environmental and genetic factors having a role. Numerous studies have identified genes that
predispose individuals to RA and a large proportion of these have been focused on genes
playing a role in the immune system. Mental illness is a common form of neurological
disease, with estimates of 1 in 4 people experiencing some form of the disease during their
lifetime. Schizophrenia (SZ) is one form of mental illness that affects 0.5-1% of the
population. An inverse relationship between RA and SZ is well documented. Recent evidence
suggests that there may be a common disease pathway for these two disorders. Recently,
genes traditionally associated with RA have been implicated in SZ disease pathways and vice
versa. AKT1 belongs to a pathway of genes involved in cell survival and function. A number
of genes in this pathway have been implicated in autoimmune diseases. DISC1 is known to
play a role in the development of SZ. Recent findings suggest this gene is implicated in the
same pathway as AKT1.
The first aim of this study was to investigate AKT1 and genes involved in the same pathway
for association with RA. Three candidate genes, DISC1, NFATC1 and NFATC2 were
identified from RA genome wide association scan study results. Meta-analysis of genotyping
results with Wellcome Trust Case Control Consortium (WTCCC RA data for AKT1 SNPs
resulted significant associations for AKT1_rs2494731 (p = 0.033, OR = 1.08[1.0-1.2]) and
AKT1_rs4983386 (p = 0.006, OR = 1.10[1.0-1.2]). Haplotype analysis of these two SNPs was
significant (haplotype 21 p = 0.0009, OR = 1.473[1.2-1.8]) for a combined genotyping dataset
(NZRA and UKRA). Conditional analysis provided evidence for independent effects at these
loci (p = 0.415) It was therefore concluded that there is evidence for two independent effects
of AKT1 in development of RA. Meta-analysis of genotyping and WTCCC RA data for
NFATC2_rs8119787 was significant for association (p = 0.032, OR = 0.92[0.86-0.99])
whereas separate analyses for each sample set was not (WTCCC p = 0.057, NZRA p = .0.314
Three SNPs within DISC1 (DISC1_rs4658966p = 0.001 OR = 0.81[0.71-0.91], rs321577 p =
II
0.018 OR = 0.90[0.83-0.98] and DISC1_rs872624p = 0.017 OR = 0.85[0.71-097]) were
significantly associated with RA in a meta-analysis of genotyping and WTCCC RA data.
Haplotype analysis for three DISC1 SNPs (DISC1_rs872625, DISC1_rs4658966, and
DISC1_rs872624) provided evidence of association in three haplotypes (111 p = 0.013 OR =
1.30[1.06-1.61], 211 p = 0.002 OR = 0.60[0.44-0.83] and 21 p = 0.005 OR = 0.65[0.490.88]).
The SNPs were investigated for association with SZ and a broader sample for mental illness
(two SZ cohorts and bipolar disease, BD). None of the SNPs within AKT1, NFATC1,
NFATC2 or DISC1 were significant for this analysis. All SNPs were also investigated for sex
bias within each sample set. No significant associations were found when analyzing the sexes
separately for AKT1 or NFATC2. For NFATC1_rs2002311 the results although significant
were inconclusive. DISC1 SNP rs9431714 was significant for females in the WTCCC RA (p
= 0.004) and Genetic Association Information Network (GAIN) SZ (p = 0.006) sample sets.
For this SNP the OR of WTCCC conveyed susceptibility to RA (OR = 1.26[1.07-1.47]) and
the OR of GAIN conveyed protection against SZ (OR = 0.88[0.72-1.08]). A similar, although
borderline, effect was seen for the WTCCC RA and psychosis datasets for DISC1_rs872624
in females (WTCCC p = 0.056 OR = 0.82[0.67-1.01], psychosis p = 0.041 OR = 1.18[0.921.52]). This analysis provides evidence for a shared disease pathway for RA and SZ in
females at least.
This thesis also investigated SNPs from within the genes of interest (AKT1, NFAT and
DISC1) in the SZ literature. A meta-analysis was carried out for the common SNPs in the
literature for AKT1 and DISC1. For ATK1 none of SNPs examined were significant for
association with SZ. This was the result when all information was analysed together and when
it was separated for ethnicity. In DISC1 one SNP, rs821597 was significantly associated with
SZ (p = 0.025) when the two studies from the literature were analysed in combination with
GAIN SZ. This analysis provides additional support for the involvement of this DISC1 SNP
in the development of SZ.
The last section of this thesis was focused on the involvement of the GRIK1 and GRIK2
genes in the development of inflammatory arthritis (IA) in a mouse model of the human
disease. Recent data suggests a common pathway involving GRIK2 may link RA and SZ. A
III
microsatellite assay, containing 57 microsatellite markers, was prepared to identify breeding
animals for involvement in the serum transfer model of IA induction. Following the failure of
this method the assay was modified for use in identifying animals for the spontaneous method
of IA induction. The spontaneous model was yet to be tested at completion of this thesis.
IV
TABLEOFCONTENTS
ACKNOWLEDGEMENTS ............................................................ I
ABSTRACT .............................................................................II
TABLE OF CONTENTS............................................................. V
LIST OF TABLES .................................................................... X
LIST OF FIGURES ............................................................... XIII
LIST OF ABBREVIATIONS ....................................................XVI
I N T R O D U C T I O N ................................................................................................................... 19
1.1.
Autoimmune Disease......................................................................................................... 20
1.1.1.
Rheumatoid Arthritis ................................................................................................. 20
1.1.1.1. Trigger ................................................................................................................... 21
1.1.1.2. Mechanism ............................................................................................................ 22
1.1.1.3. Diagnosis ............................................................................................................... 23
1.1.1.4. Genetic factors ....................................................................................................... 24
1.1.1.5. Environmental factors ........................................................................................... 29
1.1.1.6. Treatments ............................................................................................................. 32
1.1.1.7. Related conditions ................................................................................................. 33
1.2.
Neuropsychiatric Disease .................................................................................................. 34
1.2.1.
Schizophrenia ............................................................................................................ 34
1.2.1.1. Trigger ................................................................................................................... 35
1.2.1.2. Mechanism ............................................................................................................ 35
1.2.1.3. Genetic Factors ...................................................................................................... 37
1.2.1.1. Environmental Factors........................................................................................... 41
1.2.1.2. Treatment ............................................................................................................... 46
1.3.
A Genetic Link Between Rheumatoid Arthritis and Schizophrenia.................................. 47
1.3.1.
Experimental Evidence .............................................................................................. 47
1.3.1.
Theories for the Association Between RA and SZ .................................................... 48
1.3.2.
Significance ............................................................................................................... 48
1.4.
Candidate Genes ................................................................................................................ 49
1.4.1.
Genome Wide Association Scanning for Identifying Associated Genes .................. 49
1.4.2.
AKT1 .......................................................................................................................... 51
1.4.1.
GRIK ......................................................................................................................... 53
1.4.2.
AKT1 Pathway Genes ................................................................................................ 53
1.4.2.1. PTEN ..................................................................................................................... 53
1.4.2.2. PDK1 ..................................................................................................................... 54
1.4.2.3. BAD ....................................................................................................................... 54
1.4.2.4. GSK3E ................................................................................................................... 54
1.4.2.5. CCND1 .................................................................................................................. 55
1.4.2.6. Beta-CTN (E-CTN) ............................................................................................... 55
1.4.3.
DISC1 ........................................................................................................................ 55
1.4.4.
NFAT ......................................................................................................................... 56
1.5.
Meta-Analysis of Published Literature .............................................................................. 58
1.5.1.
History of Meta-analysis ........................................................................................... 58
1.5.2.
Conducting a meta-analysis ....................................................................................... 59
1.5.3.
Advantages of Meta-Analysis ................................................................................... 60
V
Disadvantages of Meta-analysis ................................................................................ 61
1.5.4.
1.5.5.
Future directions of Meta-analysis ............................................................................ 62
1.6.
Animal Models for Rheumatoid Arthritis ......................................................................... 64
1.6.1.
Knockout Mouse Approach ....................................................................................... 65
1.6.2.
Mouse Models for Rheumatoid Arthritis .................................................................. 65
1.6.3.
KRN model of Inflammatory Arthritis ...................................................................... 66
1.6.3.1. Spontaneous arthritis model .................................................................................. 66
1.6.3.2. Serum induced arthritis model............................................................................... 66
1.6.3.3. Similarities and Differences with human Rheumatoid Arthritis ........................... 66
1.7.
Project Aims ...................................................................................................................... 68
M A T E R I A L S A N D M E T H O D S ...................................................................................... 69
2.1.
Human Genotyping Sample Sets ....................................................................................... 70
2.1.1.
Ethics Approval ......................................................................................................... 70
2.1.2.
Participant recruiting ................................................................................................. 70
2.1.3.
DNA extraction ......................................................................................................... 71
2.1.4.
Sample storage........................................................................................................... 72
2.2.
GWAS Datasets ................................................................................................................. 73
2.2.1.
Primary Sample sets .................................................................................................. 73
2.2.1.1. WTCCC ................................................................................................................. 73
2.2.1.2. GAIN SZ ............................................................................................................... 73
2.2.2.
Additional samples sets ............................................................................................. 74
2.2.2.1. Non-GAIN SZ ....................................................................................................... 74
2.2.2.2. GAIN Bipolar Disease ........................................................................................... 75
2.2.3.
Datasets for the Detection of Sex Bias ...................................................................... 75
2.3.
Selection of SNPs .............................................................................................................. 76
2.3.1.
Literature search ........................................................................................................ 76
2.3.2.
BC|SNPmax ............................................................................................................... 76
2.3.2.1. Imputing SNPs in WTCCC and GAIN.................................................................. 78
2.3.3.
Linkage Disequilibrium analysis ............................................................................... 80
2.4.
Human Sample Genotyping .............................................................................................. 82
2.4.1.
RFLP primer design for human genotyping .............................................................. 82
2.4.2.
Polymerase chain reaction ......................................................................................... 84
2.4.2.1. Optimisation of marker assays .............................................................................. 84
2.4.2.2. PCR amplification ................................................................................................. 85
2.4.3.
Restriction enzyme digest.......................................................................................... 86
2.4.4.
Gel electrophoresis .................................................................................................... 87
2.4.5.
TaqMan...................................................................................................................... 88
2.4.6.
Quality Control .......................................................................................................... 90
2.4.7.
Data Analysis............................................................................................................. 91
2.4.7.1. Association Analysis ............................................................................................. 91
2.4.7.2. Hardy-Weinberg Testing ....................................................................................... 93
2.4.7.3. Haplotype association analysis .............................................................................. 93
2.4.7.4. Conditional analysis .............................................................................................. 95
2.5.
Meta-Analysis of Genotyping and Published Literature ................................................... 97
2.5.1.
Meta-Analysis of Genotyping ................................................................................... 97
2.5.2.
Meta-Analysis of Published Literature ...................................................................... 98
2.6.
Mouse Sample Preparation ................................................................................................ 99
2.6.1.
Ethical Approval ........................................................................................................ 99
2.6.2.
Breeding the Mouse strains ....................................................................................... 99
2.6.2.1. KRN ....................................................................................................................... 99
VI
2.6.2.2. GRIK ................................................................................................................... 100
2.6.3.
The serum transfer model ........................................................................................ 100
2.6.4.
Induction of IA via the Spontaneous Model............................................................ 101
2.6.5.
Animal Handling ..................................................................................................... 103
2.6.5.1. Housing Conditions ............................................................................................. 103
2.6.5.2. Food and Water ................................................................................................... 103
2.6.5.3. Identification........................................................................................................ 103
2.6.5.4. Weaning ............................................................................................................... 103
2.6.5.5. Tail Sample Collection ........................................................................................ 104
2.6.5.6. Testing for Inflammatory Arthritis ...................................................................... 104
2.6.5.7. Serum transfer ..................................................................................................... 104
2.6.5.8. Animal Euthanasia............................................................................................... 105
2.6.6.
Mouse Genomic DNA extraction ............................................................................ 105
2.6.6.1. DNA Extraction from Tail-tips ........................................................................... 105
2.6.6.2. DNA Extraction from Mouse Liver .................................................................... 106
2.6.6.3. Phenol Chloroform extraction ............................................................................. 106
2.6.6.4. NanoDrop spectrophotometery ....................................................................... 107
2.7.
Mouse Sample Genotyping ............................................................................................. 108
2.7.1.
Gel electrophoresis .................................................................................................. 108
2.7.2.
KRN +/+ .................................................................................................................. 108
2.7.3.
GRIK -/-................................................................................................................... 109
2.8.
Mouse Microsatellite Assay ............................................................................................ 112
2.8.1.
Selection of microsatellites...................................................................................... 112
2.8.1.1. Microsatellites for the serum transfer model ....................................................... 112
2.8.1.2. Microsatellites for the spontaneous IA model ..................................................... 113
2.8.2.
Preparing the assay ................................................................................................. 113
2.8.2.1. Gel electrophoresis .............................................................................................. 113
2.8.2.2. Non-informative microsatellites .......................................................................... 114
2.9.
Materials and reagents ..................................................................................................... 115
2.9.1.
Solutions .................................................................................................................. 115
2.9.2.
Enzyme buffers ........................................................................................................ 116
2.9.3.
Restriction enzymes................................................................................................. 118
2.9.4.
Website addresses .................................................................................................... 119
R E S U L T S .................................................................................................................................. 121
3.1.
Genotyping of RA Candidate Genes from the AKT1 Pathways ...................................... 122
3.1.1.
SNP selection........................................................................................................... 122
3.1.1.1. AKT1 ................................................................................................................... 122
3.1.1.2. NFAT ................................................................................................................... 123
3.1.1.3. DISC1 .................................................................................................................. 125
3.1.1.4. BAD ..................................................................................................................... 126
3.1.1.5. CCND1 ................................................................................................................ 126
3.1.1.6. GSK3B ................................................................................................................ 127
3.1.1.7. PDK1 ................................................................................................................... 128
3.1.1.8. PTEN ................................................................................................................... 128
3.1.1.9. Beta-CTN (E-CTN) ............................................................................................. 129
3.1.2.
Linkage Disequilibrium Analysis ............................................................................ 129
3.1.2.1. AKT1 ................................................................................................................... 129
3.1.2.2. NFAT ................................................................................................................... 130
3.1.2.3. DISC1 .................................................................................................................. 130
3.1.3.
SNPs within and Surrounding AKT1 ....................................................................... 132
VII
3.1.3.1. Case-Control Association Analysis ..................................................................... 132
3.1.3.2. Meta-analysis ....................................................................................................... 134
3.1.3.3. Haplotype Analysis ............................................................................................. 139
3.1.3.4. Effect in Psychosis .............................................................................................. 140
3.1.3.5. Sex Differences.................................................................................................... 143
3.1.4.
SNPs within NFAT.................................................................................................. 145
3.1.4.2. Meta-analysis ....................................................................................................... 145
3.1.4.5. Sex Differences.................................................................................................... 150
3.1.5.
SNPs Within DISC1 ................................................................................................ 152
3.1.5.1. Meta-analysis ....................................................................................................... 154
3.1.5.4. Sex Differences.................................................................................................... 164
3.2.
Meta-Analysis of SNPs from SZ Candidate Genes In the Literature .............................. 167
3.2.1.
AKT1 ........................................................................................................................ 167
3.2.1.1. SNP selection....................................................................................................... 167
3.2.1.2. AKT1 Meta-analysis – part 1 .............................................................................. 169
3.2.1.3. AKT1 Meta-analysis – Part 2 .............................................................................. 173
3.2.2.
DISC1 ...................................................................................................................... 176
3.2.2.1. DISC1 Meta-analysis – part 1 ............................................................................. 178
3.2.2.2. DISC1 Meta-analysis – Part 2 ............................................................................. 184
3.3.
Genotyping mice for KO status ....................................................................................... 188
3.3.1.
KRN ......................................................................................................................... 188
3.3.2.
GRIK1 ..................................................................................................................... 189
3.3.3.
GRIK2 ..................................................................................................................... 191
3.4.
Induction of Inflammatory Arthritis in a Mouse Knockout Model ................................. 192
3.4.1.
Serum Transfer Model ............................................................................................. 192
3.4.2.
Spontaneous Model ................................................................................................. 192
3.5.
Microsatellite Assay for Differentation Between Genomic DNA From Mouse
Strains B6 and 129....................................................................................................................... 193
3.5.1.
B6/129 Assay........................................................................................................... 193
3.5.2.
B6/NOD Assay ........................................................................................................ 197
3.5.2.1. Testing the Assay ................................................................................................ 197
3.5.2.2. Backcross One ..................................................................................................... 199
3.5.2.3. Selecting breeding animals from the assay.......................................................... 201
D I S C U S S I O N A N D C O N C L U S I O N S ....................................................................... 202
4.1.
Genotyping of RA Candidate Genes from the AKT1 Pathways ...................................... 203
4.1.1.
AKT1 ........................................................................................................................ 203
4.1.1.1. Association with RA............................................................................................ 203
4.1.1.1. Association with psychosis.................................................................................. 204
4.1.1.2. Sex bias ................................................................................................................ 204
4.1.1.3. Summary.............................................................................................................. 205
4.1.1.
Nuclear Factor of Activated T cells (NFAT) .......................................................... 205
4.1.1.3. Sex bias ................................................................................................................ 206
VIII
4.1.1.4. Summary.............................................................................................................. 207
4.1.2.
Disrupted in Schizophrenia 1 (DISC1) .................................................................... 207
4.1.2.3. Sex bias ................................................................................................................ 209
4.1.2.4. Summary.............................................................................................................. 209
4.1.3.
Limitations of this Study ......................................................................................... 210
4.2.
Two Hypotheses for the Negative Corrolation Between RA and SZ .............................. 211
4.2.1.
Hypothesis 1 ............................................................................................................ 211
4.2.1.1. Support for hypothesis 1 ...................................................................................... 212
4.2.2.
Hypothesis 2 ............................................................................................................ 213
4.2.2.1. Support for hypothesis 2 ...................................................................................... 213
4.3.
Meta-Analysis of SNPs from SZ Candidate Genes In the Literature .............................. 215
4.3.1.
AKT1 ........................................................................................................................ 215
4.3.2.
DISC1 ...................................................................................................................... 215
4.3.3.
4.4.
Induction of Inflammatory Arthritis in Mice................................................................... 218
4.4.1.
4.5.
Microsatellite Assay for Differentation Between Genomic DNA From Mouse
Strains B6 and 129....................................................................................................................... 220
4.5.1.
4.6.
Summary and Future Research ........................................................................................ 221
4.6.1.
Human ..................................................................................................................... 221
4.6.2.
Mouse ...................................................................................................................... 221
4.6.3.
Future Research ....................................................................................................... 221
REFERENCES .............................................................................................................................. 223
A P P E N D I C E S ......................................................................................................................... 260
IX
LISTOFTABLES
Table 1.1: The 1987 revised criteria for the classification of RA. .................................................... 24
Table 1.2: Morbid Risk of RA for Relatives of RA Patients............................................................. 25
Table 1.3: Phenotype frequencies of HLA DRB1 ............................................................................. 26
Table 1.4:Confirmed Rheumatoid Arthritis susceptibility loci . ....................................................... 28
Table 1.5: Morbid Risk of Schizophrenia for Relatives of Schizophrenic Patients. ......................... 37
Table 1.6: Potential Schizophrenia susceptibility loci....................................................................... 40
Table 1.7: The incidence of rheumatoid arthritis in schizophrenic patients and controls. ................ 47
Table 1.8: Trends in numbers of published articles on human genome epidemiology, metaanalysis and genome-wide association studies .................................................................................. 51
Table 2.9: The number of case and control samples for each sample set used in this study............. 71
Table 2.10: DNA plate nomenclature for RA cases and controls. .................................................... 72
Table 2.11: Example of data output for PLINK case-control analysis in SNPmax .......................... 78
Table 2.12: PCR reaction mix for a single 10 µL reaction ................................................................ 85
Table 2.13: Thermocycler settings for PCR amplification ................................................................ 86
Table 2.14: Restriction digest reaction mix per reaction for enzymes not requiring BSA................ 86
Table 2.15: Restriction digest reaction mix per reaction for enzymes not requiring BSA................ 86
Table 2.16: 3.5% agarose gel ............................................................................................................ 87
Table 2.17: TaqMan reagents for a single reaction. .......................................................................... 88
Table 2.18: Example of STATA input file layout ............................................................................. 97
Table 2.19: Tail lysis buffer for DNA extraction ............................................................................ 105
Table 2.20: 3.5% agarose gel .......................................................................................................... 108
Table 2.21: PCR reaction mix for a single 25 µL reaction .............................................................. 109
Table 2.22: Thermocycler settings for PCR amplification .............................................................. 109
Table 2.23: PCR reaction mix for a single 10 µL reaction .............................................................. 110
Table 2.24: Thermocycler settings for PCR amplification .............................................................. 110
Table 2.25: 4% agarose gel.............................................................................................................. 113
Table 2.26: Restriction enzymes utilised in this study and their working conditions. .................... 118
Table 3.27: PLINK genotyping information for all AKT1 SNPs in the WTCCC rheumatoid
arthritis and GAIN schizophrenia sample sets.. .............................................................................. 123
Table 3.28: A comparison of genotyping data for AKT1 SNPs of interest in the WTCCC and
GAIN sample sets. ........................................................................................................................... 123
Table 3.29: PLINK genotyping information for all NFATC1 SNPs in the WTCCC and GAIN
sample sets.. ..................................................................................................................................... 124
Table 3.30: PLINK genotyping information for the NFATC2 SNPs with the most significant
p-values in the WTCCC and GAIN sample sets.. ........................................................................... 124
Table 3.31: A comparison of genotyping data for NFATC1and NFACTC2 SNPs of interest in
the WTCCC and GAIN sample sets. ............................................................................................... 124
Table 3.32: PLINK genotyping information for DISC1SNPs with the most significant pvalues in the WTCCC and GAIN sample sets................................................................................. 125
Table 3.33: A comparison of genotyping data for DISC1SNPs of interest in the WTCCC and
GAIN sample sets. ........................................................................................................................... 126
Table 3.34: PLINK genotyping information for all BAD SNPs in the WTCCC and GAIN
sample sets. ...................................................................................................................................... 126
Table 3.35: PLINK genotyping information for all CCND1 SNPs in the WTCCC and GAIN
sample sets. ...................................................................................................................................... 126
Table 3.36: PLINK genotyping information for all GSK3B SNPs in the WTCCC and GAIN
sample sets. ...................................................................................................................................... 127
X
Table 3.37: PLINK genotyping information for all PDK1 SNPs in the WTCCC and GAIN
sample sets. ...................................................................................................................................... 128
Table 3.38: PLINK genotyping information for all PTEN SNPs in the WTCCC and GAIN
sample sets. ...................................................................................................................................... 128
Table 3.39: PLINK genotyping information for all E-CTN SNPs in the WTCCC and GAIN
sample sets. ...................................................................................................................................... 129
Table 3.40: Case-control analysis for the AKT1SNPs. .................................................................... 133
Table 3.41: Meta-analysis for the AKT1SNPs over all sample sets genotyped in this study and
WTCCC.. ......................................................................................................................................... 135
Table 3.42: Haplotype analysis for the two AKT1 SNPs in LD, AKT1_rs2494731 and
AKT1_rs4983386, shown for the WTCCC sample set. ................................................................... 139
Table 3.43: Haplotype analysis for the two AKT1 SNPs in LD, AKT1_rs2494731 and
AKT1_rs4983386 over the GAIN SZ dataset.. ................................................................................ 139
Table 3.44: Haplotype analysis for the two AKT1SNPs in LD, AKT1_rs2494731 and
AKT1_rs4983386, over the combined genotyping sample set (NZRA, ARA and UKRA).. .......... 140
Table 3.45: Analysis for AKT1 SNPs, in three mental illness datasets. .......................................... 141
Table 3.46: Analysis of males versus females for the four AKT1 SNPs. ........................................ 143
Table 3.47: AKT1_rs7146661 association results over NZRA, WTCCC RA, GAIN SZ and
psychosis for female cases versus controls and males cases versus controls. ................................. 144
Table 3.48: Case-control analysis for the SNPs NFATC1_rs2002311 and
NFATC2_rs8119787. ....................................................................................................................... 145
Table 3.49: Meta-analysis for the NFAT SNPs over NZRA, WTCCC.. ......................................... 146
Table 3.50: Association analysis for NFAT SNPs, NFATC1_rs2002311 and
NFATC2_rs8119787, in three mental illness datasets. .................................................................... 149
Table 3.51: Analysis of males versus females for NFATC1_rs2002311 and
NFATC2_rs8119787.. ...................................................................................................................... 151
Table 3.52: NFATC1_rs2002311 association results over NZRA, WTCCC RA, GAIN SZ and
Psychosis for female cases versus controls and males cases versus controls.................................. 151
Table 3.53: Case-control analysis for the DISC1 SNPs .................................................................. 153
Table 3.54: Meta-analysis for the DISC1 SNPs over the sample sets genotyped in this study
and WTCCC. ................................................................................................................................... 154
Table 3.55: The Haplotype analysis for three DISC1 SNPs, DISC1_rs872625,
DISC1_rs4658966, and DISC1_rs872624 are shown for the WTCCC RA sample set .................. 159
Table 3.57: The Haplotype analysis for three DISC1 SNPs, DISC1_rs872625,
DISC1_rs4658966, and DISC1_rs872624 are shown for the NZRA sample set. ........................... 160
Table 3.58: Analysis for DISC1 SNPs, DISC1_rs9431714, DISC1_rs872625,
DISC1_rs4658966, DISC1_rs821577 and DISC1_rs872624, in three mental illness datasets ....... 162
Table 3.59: Analysis of males versus females for the SNPs DISC1_rs9431714,
DISC1_rs872625, DISC1_rs4658966, DISC1_rs821577 and DISC1_rs872624 ............................ 165
Table 3.60: Sex specific association results for DISC1. .................................................................. 166
Table 3.61: Table of SNPs occurring more than once in the literature. .......................................... 167
Table 3.62: Literature information for AKT1 SNPs investigated in a range of neurological
disorders.. ........................................................................................................................................ 168
Table 3.63: Genotyping information for AKT1 SNPs of interest extracted from the literature. ..... 169
Table 3.64: Meta-analysis for the AKT1SNPs investigated in the schizophrenia literature.. .......... 170
Table 3.65: SNPs for inclusion in the ethnicity based meta-analysis.............................................. 173
Table 3.66: Meta-analysis for the AKT1 SNPs investigated in the schizophrenia literature. .......... 173
Table 3.67: Table of DISC1 SNPs occurring more than once in the literature ............................... 176
Table 3.68: Literature information for DISC1 SNPs investigated in a range of neurological
disorders.. ........................................................................................................................................ 177
Table 3.69: Genotyping information for DISC1 SNPs of interest extracted from the literature. .... 178
XI
Table 3.70: Meta-analysis for the DISC1 SNPs investigated in the schizophrenia literature.. ....... 179!
Table 3.71: SNPs for inclusion in the ethnicity based meta-analysis. ............................................. 184!
Table 3.72: Meta-analysis for the DISC1 SNPs investigated in the schizophrenia literature. ........ 185!
Table 3.73: Results from the genotyping of KRN mice. All mice tested were males. .................... 189!
Table 3.74: Results from the genotyping of GRIK1 mice. M=Male, F=Female. ........................... 190!
Table 3.75: Results from the genotyping of GRIK2 mice. M=Male, F=Female. ........................... 191!
Table 3.76: Microsatellites investigated in this thesis and whether they were included in the
B6/129 assay.. .................................................................................................................................. 194!
Table 3.77: Successful informative microsatellites with their exact position on the
chromosome..................................................................................................................................... 196!
Table 3.78: Microsatellites investigated in this thesis and whether they were included in the
B6/NOD assay. ................................................................................................................................ 198!
Table 3.79: DNA concentrations of Mouse DNA samples used in the backcross one assay.. ........ 199!
Table 3.80: DNA concentrations of Mouse DNA test samples used in the backcross one
assay................................................................................................................................................. 199!
Table 3.81: B6/NOD assay results. 2 = test sample 2, 5 = test sample 5. ....................................... 200!
Table 5.82: Supporting evidence for hypothesis one. ..................................................................... 213!
Table 5.83: Supporting evidence for hypothesis two. ..................................................................... 214!
Table A.84: Primer sequences for mouse microsatellite markers ................................................... 261!
Table A.85: Primer sequences for SNP markers ............................................................................. 265!
Table A.86: Assay conditions for mouse microsatellite markers contained in the assay. ............... 266!
Table A.87: Assay conditions for human SNP markers .................................................................. 267!
Table B.88: BC|SNPmax genotyping information for DISC1 SNPs in the WTCCC and GAIN
cohorts. ............................................................................................................................................ 268!
Table B.89: BC|SNPmax genotyping information for NFATC2 SNPs in the WTCCC and
GAIN cohorts. ................................................................................................................................. 275!
XII
LISTOFFIGURES
Figure 1.1: Summary of the pathophysiology of rheumatoid arthritis. ............................................. 23
Figure 1.2: Haematoxylin and eosin stain of joint tissues from patients with rheumatoid
arthritis. .............................................................................................................................................. 23
Figure 1.3: Relative risk of developing rheumatoid arthritis in subjects exposed to different
combinations of smoking and HLA-DR shared epitope (SE) genes ................................................. 30
Figure 1.4: The influence of risk factors over time on the development of schizophrenia. .............. 41
Figure 1.6: Risk of schizophrenia in non-white ethnic minorities according to their proportion
in their local area. .............................................................................................................................. 45
Figure 1.7: A summary of the interaction between candidate genes of interest with RA and
SZ. ..................................................................................................................................................... 52
Figure 1.8: Activation and inhibition of AKT1 through a multiple signalling pathway. ................... 52
Figure 1.9: Involvement of DISC1 in the AKT1 pathway. ................................................................ 56
Figure 1.10: The activation of Ca2+ dependant NFATC through..................................................... 57 Figure 1.11: A typical graphical representation of meta-analysis results.......................................... 60
Figure 1.13: Initiation of arthritis in K/BxN mice ............................................................................. 67
Figure 2.14: Example of SNPmax setup for PLINK case-control analysis....................................... 78
Figure 2.15: Example of SNPmax setup for IMPUTE of SNPs in AKT1. ........................................ 79
Figure 2.16:Haploview LD plot (r2) generated from HapMap genotyping of one block of
DISC1 SNPs. ..................................................................................................................................... 80
Figure 2.17: Haploview LD plot (r2) generated from HapMap genotyping illustrating a region
of complete LD between SNPs.......................................................................................................... 81
Figure 2.18: The process of primer design for forced and natural cut sites.. .................................... 83
Figure 2.20: An example of a RFLP assay. ....................................................................................... 87
Figure 2.23: The two forms of TaqMan analysis software output.. .................................................. 90 Figure 2.24: Data input for single site analysis in SHEsis.. .............................................................. 92 Figure 2.25: Data input for haplotype analysis in SHEsis................................................................. 94
Figure 2.26: Run variables for a haplotype association analysis for selected DISC1 SNPs in
the WTCCC RA dataset. ................................................................................................................... 95
Figure 3.27: Example layout for Map and Ped files used in conditional analysis. ........................... 96
Figure 2.28: The breeding program for induction of inflammatory arthritis in GRIK knockout
mice. ................................................................................................................................................ 101
Figure 2.29: The breeding program for induction of spontaneous inflammatory arthritis in
GRIK knockout mice.. ..................................................................................................................... 102
Figure 2.30: The binding pattern of GRIK1 and Neo primers within the GRIK1 gene.. ................ 111
Figure 3.31: A Haploview LD plot showing the SNPs of interest in and around the gene
AKT1.. .............................................................................................................................................. 130
Figure 3.32: Haploview LD plot (r2) showing haplotype block 21of DISC1 ................................. 131
Figure 3.33: Odds ratio meta-analysis plot for AKT1_rs2494731 in AKT1. ................................... 136
Figure 3.34: Odds ratio meta-analysis plot for AKT1_rs1130214 in AKT1.. .................................. 136
Figure 3.35: Odds ratio meta-analysis plot for AKT1_rs4983386 near AKT1. ............................... 137
Figure 3.36: Odds ratio meta-analysis plot for AKT1_rs7146661 near AKT1.. .............................. 137
Figure 3.39: Odds ratio meta-analysis plot for AKT1_rs4983386 near AKT1. ............................... 138
Figure 3.40: Odds ratio meta-analysis plot for AKT1_rs7146661 near AKT1.. .............................. 138
Figure 3.41: Odds ratio meta-analysis plot for AKT1_rs2494731 over the mental illness
datasets.. .......................................................................................................................................... 141
XIII
datasets.. .......................................................................................................................................... 142
datasets.. .......................................................................................................................................... 142
datasets. ........................................................................................................................................... 142
Figure 3.45: Odds ratio meta-analysis plot for rs2002311 in NFATC1. ......................................... 147
Figure 3.46: Odds ratio meta-analysis plot for rs8119787 in NFATC2.. ........................................ 147
Figure 3.49: Odds ratio meta-analysis plot for NFATC1_rs2002311.............................................. 150
Figure 3.50: Odds ratio meta-analysis plot for NFATC2_rs8119787.............................................. 150
Figure 3.51: Odds ratio meta-analysis plot for DISC1_rs9431714 in DISC1. ................................ 155
Figure 3.52: Odds ratio meta-analysis plot for DISC1_rs872625 in DISC1. .................................. 156
Figure 3.53: Odds ratio meta-analysis plot for DISC1_rs4658966 in DISC1.. ............................... 156
Figure 3.54: Odds ratio meta-analysis plot for DISC1_rs821577 in DISC1.. ................................. 156
Figure 3.65: Odds ratio meta-analysis plot for DISC1_rs872624 in DISC1.. ................................. 164
Figure 3.69: Odds ratio meta-analysis plot for Caucasian ancestry AKT1_rs1130214 ................... 174
Figure 3.70: Odds ratio meta-analysis plot for Asian ancestry AKT1_rs1130214. ......................... 174
Figure 3.71: Odds ratio meta-analysis plot for Asian ancestry AKT1_rs2498804 .......................... 175
Figure 3.72: Odds ratio meta-analysis plot for Caucasian ancestry AKT1_rs3803304 ................... 175
Figure 3.73: Random effects meta-analysis plot for DISC1_rs821616. .......................................... 180
Figure 3.74: Random effects meta-analysis plot for DISC1_rs3738401. ........................................ 181
Figure 3.75: Odds ratio meta-analysis plot for DISC1_rs1000731 ................................................. 181
Figure 3.76: Odds ratio meta-analysis plot for DISC1_rs3738398. ................................................ 182
Figure 3.77: Odds ratio meta-analysis plot for DISC1_rs821597.. ................................................. 182
Figure 3.78: Odds ratio meta-analysis plot for DISC1_rs843979. .................................................. 183
Figure 3.79: Random effects meta-analysis plot for DISC1_rs999710. .......................................... 183
Figure 3.80: Odds ratio meta-analysis plot for Caucasian ancestry DISC1_rs821616.. ................. 185
Figure 3.81: Random effects meta-analysis plot for Asian ancestry DISC1_rs821616. ................. 186
Figure 3.82: Random effects meta-analysis plot for Caucasian ancestry DISC1_rs3738401. ........ 186
Figure 3.83: Odds ratio meta-analysis plot for Asian ancestry DISC1_rs843979. ......................... 187
Figure 3.84: Gel electrophoresis visualisation of nine microsatellites over B6 and 129 DNA....... 193
Figure 3.85: Visual representation of the approximate placement of microsatellite markers
included in the B6/129 assay. .......................................................................................................... 195
Figure 3.86: Gel electrophoresis visualisation of five microsatellites over B6 and NOD DNA..... 197
Figure 4.87: A schematic of a proposed theoretical model for the negative association
between rheumatoid arthritis and schizophrenia. ............................................................................ 212
XIV
Figure C.88: Haploview LD plot (r2) generated from HapMap genotyping of all validated
AKT1 SNPs.. .................................................................................................................................... 278!
Figure D.89: Haploview LD plot (r2) generated from HapMap genotyping of all validated
DISC1 SNPs.. .................................................................................................................................. 279!
Figure D.90: Haploview LD plot (r2) generated from HapMap genotyping of one block of
DISC1 SNPs .................................................................................................................................... 279!
Figure E.91: Haploview LD plot (r2) generated from HapMap genotyping of all validated
NFAT SNPs...................................................................................................................................... 280!
XV
LISTOFABBREVIATIONS
ADHD
ATK1
BAD
BD
B-D
bp
CBT
CCND1
CCP
CD64
CI
CI
CIA
CMA
COMT
COX
CTNNB1
D1
DISC1
DMARD
DNA
dNTP
DSM
DTNBP1
EA
EBV
EDTA
ES
FEZ1
GAIN
GI
GPI
GRIK
GSK3ȕ
GWAS
HHV-6
HLA
HW
HWD
HWE
IA
Attention Deficit Hyperactive Disorder
v-AKT murine thymoma viral oncogene homolog 1
Bcl-2-associated Agonist of cell Death
Bipolar Disease
Breslow-Day
base pairs
Cognitive Behaviour Therapy
Cyclin D1
anti-Cyclic Citrullinated Peptide
Cluster of Differentiation 64
Confidence Intervals
Confidence Interval
Collagen-Induced Arthritis
Comprehensive Meta Analysis
Catechol-O-Methyl-Transferase
Cyclooxygenase
Catenin (cadherin associated protein), beta 1
Dopamine Receptor D1
Disrupted In Schizophrenia 1
Disease-Modifying Antirheumatic Drug
deoxyribose nucleic acid
dinucleoside triphosphate
Diagnostic and Statistical Manual
Dysbindin
European-American (ancestry)
Epstein Barr Virus
ethylenediaminetetraacetic acid
Embryonic Stem (cells)
Fasciculation and Elongation protein Zeta 1
Genetic Association Information Network
Gastro-Intestinal
Glucose-6-Phosphate Isomerise
Glutamate Receptor, Ionotropic, Kainate
Glycogen Synthase Kinase 3 Beta
Genome Wide Association Scan
Human Herpes Virus 6
Human Leuocyte Antigen
Hardy Weinberg
Hardy Weinberg Disequilibrium
Hardy Weinberg Equilibrium
Inflammatory Arthritis
XVI
ICD
IGBMC
IgG
IL -1
IL-2
IL23R
IP
LD
LTD4
MAF
Mb
MGS
M-H
MHC
mL
MS
NDE1
NFAT
ng
NGR1
NOD
NSAID
NZ
OC
OR
OXRA
PCR
PDE4ȕ
PDK1
PFC
PPL
PTEN
PTPN22
RA
RF
RFLP
rpm
SPF
STAT4
SZ
Taq
TCR
TE
TNF
International statistical Classification of Diseases and related health problems
Institut de Genetique et de Biologie Moleculaire et Cellulaire
Immunoglobin G
Interleukin 1
Interleukin 2
Interleukin 23 Receptor
Intraperitoneal Injection
Linkage Disequilibrium
Leukotriene D4
minor allele frequency
Megabase
Molecular Genetics of Schizophrenia
Mantel-Haenszel
Major Histocompatibility Complex
Millilitre
Multiple Sclerosis
nudE Nuclear Distribution gene E homolog 1
Ca2+ dependant Nuclear Factor of Activated T cells, Cytoplasmic
Nanogram
Neuroregulin 1
Non-Obese Diabetic (mouse)
Non-Steroidal Anti-Inflammatory Drug
New Zealand
Obstetric Complications
Odds Ratio
Oxford Rheumatoid Arthritis
Polymerase Chain Reaction
Phosphodiesterase 4 Beta
Pyruvate Dehydrogenase Kinase isozyme 1
Pre-Frontal Cortex
Periplakin
Phosphate and tensin homolog deleted from chromosome 10
Protein Tyrosine Phosphatase, Non-receptor type 22
Rheumatoid Arthritis
Rheumatoid Factor
Restriction Fragment Length Polymorphism
revolutions per minute
Specific Pathogen-Free (environment)
Signal Transducer and Activator of Transcription factor 4
Schizophrenia
Thermus aquaticus polymerase enzyme
T-Cell receptor
Tris -EDTA
Tumour Necrosis Factor
XVII
TNFAIP3
TNFR
TNF-Į
TRAF1
UKRA
UV
VitD
WTCCC
ȕ-CTN
ȝL
Tumour Necrosis Factor, Alpha-Induced Protein 3
Tumour Necrosis Factor Receptor
Tumour Necrosis Factor Alpha
TNFR associated Factor 1
United Kingdom Rheumatoid Arthritis (also known as London samples)
Ultra Violet
Vitamin D
Welcome Trust Case Control Consortium
Cadherin-associated protein Beta
micro litre
XVIII
Chapter 1: Introduction
CHAPTERONE
INTRODUCTION 1.1. Autoimmune Disease
1.2. Neuropsychiatric Disease
1.3. A Genetic Link Between Rheumatoid Arthritis and
Schizophrenia
1.4. Candidate Genes
1.5. Meta-Analysis of Published Literature
1.6. Animal Models for Rheumatoid Arthritis
1.7. Project Aims
19
1.1. AUTOIMMUNEDISEASE
Autoimmune disease is defined as an immune system attack on ‘self’ tissues and cells
(Cooper & Stroehla, 2003). Nearly 3% of the world’s population is affected by some form of
autoimmune disease (Reviewed by Cooper & Stroehla, 2003). Autoimmune diseases such as
rheumatoid arthritis (RA) and dermatomyositis are described as systemic. This is when many
organs or tissue types are affected and prognosis is generally poor. Many individuals have a
reduced life expectancy and experience a number of secondary conditions, although the
introduction of new drugs has helped vastly in this area (discussed later in this review). The
other major type of autoimmune disease is organ specific and this class includes conditions
such as type 1 diabetes mellitus (affects the islet cells of the pancreas) and multiple sclerosis
(MS, immune attack on the myelin sheath). Autoimmune diseases generally have a complex
aetiology with both environmental and genetic factors having a role.
The main aim of this study was to identify candidate genes involved in the negative
association between RA and SZ. This was achieved by utilising two major strategies: casecontrol analysis, and meta-analysis of candidate gene single nucleotide polymorphisms
(SNPs) in the literature. A review of the research that prompted this study is included in the
introduction. Topics of interest include the immunology of RA, the factors contributing to the
development of RA and SZ and the genetic link between these two diseases.
1.1.1. RHEUMATOID ARTHRITIS
Arthritic disease comes in many forms including: osteoarthritis, crystal induced arthritis or
gout, septic/viral arthritis and the autoimmune forms rheumatoid arthritis (RA) and psoriatic
arthritis. Recent studies estimate that RA affects around 0.7-1% of Caucasians, with the
highest prevalence in developed countries (Helmick et al., 2008; Myasoedova et al., 2010).
This degenerative disorder results in irreversible joint damage and disability (Sherrer et al.,
2005). Primarily characterised by chronic inflammation of the joints, this disease has a
symmetrical pattern attacking mainly the wrists, hands, elbows, shoulders, knees and ankles.
Individuals experience varying degrees of disability caused by pain and swelling in the joints
but may also be affected by fatigue, malaise and weight loss (Gabriel, 2001). Rheumatoid
nodules, a protruding mass of subcutaneous cells, may also be present and are generally
20
associated with a poorer prognosis (Kaye et al., 1984). Also of concern is that patients with
inflammatory arthritis have increased morbidity from a range of disorders, including
cardiovascular disease, infection and lymphoma (Maradit-Kremers et al., 2005; Turesson et
al., 2004).
1.1.1.1. Trigger
It is hypothesised that individuals with an underlying genetic susceptibility to RA (see section
1.1.1.4) who have been exposed to the appropriate environmental risk factors (see section
1.1.1.5) still may not develop this disease unless exposed to a trigger. Viral infection has been
suggested as the trigger of some autoimmune diseases by recent epidemiological studies
(Balandraud et al., 2004; James et al., 1997; Newkirk et al., 1994). Epstein-Barr virus (EBV
or Human Herpes Virus 4) and Human Herpes Virus 6 (HHV-6) were both implicated in RA
due to the high levels of anti-EBV and anti-HHV6 antibodies detected in patients with RA
versus healthy controls (Balandraud et al., 2004). Until further studies are done to confirm
this association, EBV and HHV-6 remain only candidates for a trigger.
One infectious agent that has been implicated in RA is the microorganism Sheiphali
"Toxoplasma" gondii (T.gondii). A species of parasitic protozoa in the genus Toxoplasma,
T.gondii, causes the disease toxoplasmosis. In humans this condition has been linked to
schizophrenic-like behaviours such as hallucinations (Torrey & Yolken, 2003). A study has
shown that T.gondii infection was elevated in 75 individuals with RA compared with controls
(Tomairek et al., 1982). Interestingly, the exposure to cats, the definitive host of T.Gondii, in
childhood has also been linked to RA (Torrey & Yolken, 2001). Odds ratios of 3.04-3.60
were found in two independent studies of cat ownership before puberty for individuals with
RA, indicating a significant association with the disease (Bond & Cleland, 1996; Torrey &
Yolken, 2001). Here T. Gondii is being proposed as a trigger for both diseases. An individual
would require an underlying genetic susceptibility to either RA or SZ for it to develop.
21
1.1.1.2. Mechanism
The immune system basis of RA has been studied in detail (Arend & Dayer, 1995;
Balandraud et al., 2004; Liu & Pope, 2003; Wordsworth & Bell, 1992). The normal function
of the immune system is a process tightly regulated by two sets of mediators, one for the
initiation of the immune response and one involved in turning this response off at the
appropriate time (Choy & Panayi, 2001). The damage to cartilage and bone seen in RA is due
to an imbalance between these two sets of mediators causing chronic inflammation (Choy &
Panayi, 2001).
A local immune reaction is established through interaction between antigen presenting cells
(APCs, i.e. macrophages) and T cells (Arend, 1997). Major histocompatibility complex
(MHC) class II molecules are present on the cell surface of APCs and these present antigens
which bind to specific receptors on CD4+ T cells (Arend, 1997). It has been shown that a
specific set of peptides are presented by APCs in RA development(Wordsworth et al., 1989).
The binding of these molecules may be influenced by alleles in the gene coding the human
leukocyte antigen (HLA, see section 1.1.1.4). The interaction between T cells and APCs
causes activation of macrophages and B cells (Figure 1.1). Immunoglobins, IgG and IgM, are
released into the synovium tissues by activated B lymphocytes (Arend, 1997). Both
immunoglobins and macrophages signal the upregulation of proinflammatory cytokines (i.e.
IL-1, IL-8, IL-17, TNFĮ and INF-Ȗ) (Arend, 1997). These cytokines cause the serum
complement cascade to become activated, leading to inflammation, oedema, vasodilation and
infiltration of activated T-cells and cytokines (Lee & Weinblatt, 2001). In the final stages of
RA development, a pannus (granulated tissue) forms (Figure 1.2) surrounding the synovial
lining (Lee & Weinblatt, 2001). This structure initiates angiogenesis (construction of new
blood vessel structures) and the production of tissue damaging enzymes (Koch, 1998). Late
stage RA is characterised by joint destruction. This destruction is due to thickening of the
synovium and disintegration of underlying bone and cartilage (Lee & Weinblatt, 2001).
22
Figure 1.1: Summary of the pathophysiology of rheumatoid
arthritis. Adapted from Treatment of Rheumatoid Arthritis: New
Therapeutic
Approaches
with
Biological
Agents
(http://altmed.creighton.edu/FishOil/rheumatoidoverview.htm).
Figure 1.2: Haematoxylin and eosin stain of joint
tissues from patients with rheumatoid arthritis. A
synovial pannus invading bone and cartilage
(original magnification x100). (Taken from Lee &
Weinblatt, 2001).
1.1.1.3. Diagnosis
Diagnosis of RA is confirmed using a variety of techniques; the most common include joint
x-rays, rheumatoid factor (RF) and anti-cyclic citrullinated peptide testing (CCP). RF is
present in approximately 65% of people presenting with RA symptoms (Aho et al., 1985).
The autoantibody RF forms a complex with the immunoglobin IgG which can activate the
immune complement cascade in rheumatoid synovium (Aho et al., 1985). The presence of
these autoantibodies may precede development of organ specific autoimmune diseases by
many years, however onset of RA is generally more rapid (Aho et al., 1985). Elevated antiCCP levels are strongly associated with early onset RA, with 79% of patients testing positive
(Vasishta, 2002). Localised induction of anti-CCP by citrullinated proteins in the inflamed
synovium is responsible for these elevated levels (Masson-Bessiere et al., 2001). Although
numerous studies have been involved with determining the effect of anti-CCP on disease
pathology, little is known about the exact mechanism. It has been shown that these
autoantibodies are involved in the early stages of the disease and increased levels are related
to an increased severity of symptoms (Dubucquoi et al., 2004; Grootenboer-Mignot et al.,
2004; Kastbom et al., 2004; Söderlin et al., 2004). Anti-CCP testing has the additional
advantage of distinguishing between erosive and non-erosive RA with patients that test antiCCP positive developing more joint damage that those without the antibodies (Vasishta,
2002).
23
The 1987 American College of Rheumatology (ACR) classification system is commonly used
for diagnosis of RA (Arnett et al., 1988).Under this classification, patients are said to have
RA if they satisfy at least 4 of the 7 criteria outlined in Table 1.1 below. Patients must be
afflicted by these 4 criteria for at least 6 weeks.
Table 1.1: The 1987 revised criteria for the classification of RA (Taken from Arnett et al., 1988).
Criterion
Definition
1.
Morning stiffness
Morning stiffness in and around the joints, lasting at least 1 hour before
maximal improvement
2.
Arthritis of three or more
joint areas
At least three joint areas simultaneously have had soft tissue swelling or fluid
(not bony overgrowth alone) observed by a physician. The fourteen possible
areas are right or left PIP, MCP, wrist, elbow, knee, ankle, and MTP joints
3.
Arthritis of hand joints
At least one area swollen (as defined above) in a wrist, MCP, or PIP joint
4.
Symmetric arthritis
Simultaneous involvement of the same joint areas (as defined in 2) on both
sides of the body (bilateral involvement of PIPS, MCPs, or MTPs is
acceptable without absolute symmetry)
5.
Rheumatoid nodules
Subcutaneous nodules, over bony prominences, or extensor surfaces, or in
juxta-articular regions, observed by a physician
6.
Serum rheumatoid factor
Demonstration of abnormal amounts of serum rheumatoid factor by any
method for which the result has been positive in 4% of normal control
subjects
7.
Radiographic changes
Radiographic changes typical of rheumatoid arthritis on postero-anterior hand
and wrist radiographs, which must include erosions or unequivocal bony
decalcification localized in or most marked adjacent to the involved joints
(osteoarthritis changes alone do not qualify)
1.1.1.4. Genetic factors
Both environmental and genetic factors play a role in the susceptibility to and development of
RA (Firestein, 2003). Twin studies have played a key role in highlighting the effect size of the
genetic component of the disease. It has been estimated that RA has a heritability of around
60% in Caucasians (MacGregor et al., 2001a). An individual’s risk will increase ~32 fold if
they have a monozygotic twin (see Table 1.2) with this disorder, but only ~9 fold if their
dizygotic twin is affected (Kwoh et al., 1996).
24
Table 1.2: Morbid Risk of RA for Relatives of RA Patients. (Adapted from Kwoh
et al., 1996) and (Lawrence, 1970)
Relationship
General population
Spouses of patients
Parents
Siblings
Children
Dizygotic twin
Monozygotic twin
% Shared genes
NA
NA
50
50
50
50
100
Risk (%)
1
1
4.2
4.6
1.3
9
32
RA = Rheumatoid Arthritis
Gender is a major factor in the risk of RA onset. A recent study found women are affected by
the disease at a disproportionately higher rate (3-5 times more likely) than males (Alamanos
et al., 2006). Although females are more likely to be affected by the disease, there is no
evidence that they are more likely to be adversely affected in disease outcome(Harrison &
Symmons, 2000).
For over 30 years it has been known that the MHC on chromosome 6p21.3 is associated with
RA (Stastny, 1976). This gene rich region (~220 genes) covers over 3.6Mb and consists of
three distinct regions, classes I, II and III (Newton et al., 2004). The class I region contains
HLA class 1 genes (HLA-A, -B and –C), class II contains the HLA-DR, -DP and DQ loci and
class III falls between these two regions (Newton et al., 2004). Various studies since the late
1970s have shown that different alleles of the HLA-DRB1 gene confer different levels of
association with RA. For example, the phenotype HLA-DRB1*0404 is positively associated
with RA (see Table 1.3) conferring susceptibility (Thomson et al., 2001)whereas DRB1*0103
is negatively associated with RA providing protection (Milicic et al., 2002). The shared
epitope (SE) hypothesis was proposed in the late 1980s to account for the differences in
association between allele types (Gregersen et al., 1987). It was suggested that the
susceptibility alleles (see Table 1.3) encode a conserved sequence of amino acids which shape
one side of the antigen binding site and therefore effects the presentation of antigen.
25
Table 1.3: Phenotype frequencies of HLA DRB1
HLA DBR1
phenotype
*0101/2
*04
*0401
*0404
*0405/8/9
*1001
SE+
Controls
(n=286)
62
100
63
10
5
3
127
Cases
(n=680)
155
306
204
77
19
17
400
Odds Ratio
(95% CI)
1.0 (0.8-1.5)
1.5 (1.1-2.0)
1.5 (1.1-2.1)
3.5 (1.8-6.8)
1.6 (0.6-4.3)
2.4 (0.8-7.8)
1.8 (1.4-2.4)
SE+ = shared epitope positive. CI = confidence interval. (Taken
from Silman & Pearson, 2002)
The variability of HLA alleles is not limited to within a population, with a high degree of
variability seen between ethnicities. The predominant alleles in each population are: *0401
and *0404 in Caucasians, *0405 in Japanese, *0101 in Israeli Jews and *09 in Chileans
(Newton et al., 2004). HLA alleles have also been associated with a number of different
disorders besides RA. Examples include DRB1*0101 in Lyme disease (Steere et al., 2006)
and DRB1*0103 with Crohn's disease (Fernandez et al., 2004) and ulcerative colitis
(Puzanowska et al., 2003). DR1 has also been linked with schizophrenia in Japanese (Narita
et al., 2000).
The association of the HLA alleles with RA is robust with supporting evidence over many
different populations (Balsa et al., 2000; Citera et al., 2001; Del Rincón & Escalante, 2001;
Pascual et al., 2001; Wakitani et al., 1997; Zanelli et al., 2000). However the penetrance of
these genotypes is relatively low. Approximately 30% of the normal population have the
HLA-DRB1*04 allele and ~30% of cases with no SE-encoding alleles at all (Newton et al.,
2004). Twin studies have indicated that only ~50% of the genetic contribution to RA is due to
the HLA alleles (MacGregor et al., 2001b). This finding has lead to the investigation of other
genes in the region of MHC, for example the proinflammatory cytokine tumour necrosis
factor (TNF). TNF is located within the MHC class III region, 1000kb from HLA-DRB1
(Newton et al., 2004). There have been many studies investigating the association of this gene
with RA (Orozco et al., 2009b; Plenge et al., 2007b; Thomson et al., 2007). However, due to a
lack of LD studies in the MHC region, few recognise this association may be due to linkage
between markers in TNF and HLA (Newton et al., 2004).
26
Techniques and resources such as Genome Wide Association Scanning (GWAS) and the
International HapMap (a catalogue of common human variants) has led to a staggering
increase in the publication of genes associated with RA (Choi et al., 2006; Gregersen, 2005;
Steer et al., 2007; Xavier & Rioux, 2008). The Wellcome Trust Case Control Consortium
(WTCCC) took advantage of this technology to run one of the biggest genome wide research
projects. This group genotyped 2,000 cases and 3000 shared controls to identify variants in
each of 7 different diseases (RA, hypertension, bipolar disorder, coronary artery disease,
Crohn's disease, type 1 diabetes and type 2 diabetes) (WTCCC, 2007). Thus far the GWAS
approach has confirmed the contribution of previously identified RA associated genes such as
the antigen-presenting genes of the chromosome 6 HLA complex, STAT4 (signal transducer
and activator of transcription 4) and the T-cell activation regulator PTPN22 (Gregersen et al.,
2006; Wordsworth et al., 1989; Xavier & Rioux, 2008). PTPN22 has also been implicated in
systemic lupus erythematosus, Grave’s disease and type 1 diabetes, indicating a general
autoimmune role (Xavier & Rioux, 2008). GWAS studies have also identified a number of
novel RA associated genes. These include the inflammatory mediator TNFAIP3 on
chromosome 6 and TNFR–associated factor 1 (TRAF1) on chromosome 9 (Xavier & Rioux,
2008). TRAF1 is known to bind TNFAIP3, therefore TNF-mediated responses and TNF-Į
inhibitor drugs have become an important focus for understanding and treating this disease
(Xavier & Rioux, 2008). A list of Confirmed RA susceptibility loci is given in Table 1.4.
27
Table 1.4:Confirmed Rheumatoid Arthritis susceptibility loci with their chromosome positions, function, supporting literature and power (updated from Barton & Worthington,
2009).
Loci
HLA-DRB1
Position
6p21
Function
Immune system – presents peptides of
extracellular proteins.
Regulation of the T cell receptor
signalling pathway via CBL.
PTPN22
1p13
TNFD
6p21
Activation of immune response and antiapoptosis pathways
STAT4
2q32
Transcription factor
TRAF1/C5
9q33
IL2RB
PRKCQ
KIF5A
AFF3
CD40
CTLA4
IL2-IL21
MMEL1
PADI4
22q13
10p15
12q13
2q11
20q12
2q33
4q27
1p36
1p36
FCRL3
1q21
Involved in regulation of TNFD
signalling.
Endocytosis and mitogenic signals
T-cell activation
Transport of neurofilaments
Transcription factor
Activation of antigen presenting cells
T cell regulation (inhibitory)
Regulation of immune cells
Involved in homeostasis
Regulation of granulocyte and
macrophage development
Immune system regulation
CD244
TNFAIP3
1q23
6p23
Regulation of leukocyte activation
Inflammatory mediator
References
(Brinkman et al., 1997; Mulcahy et al., 1996; Tuokko et al., 1998; Udalova et al.,
2002; WTCCC, 2007)
(Begovich et al., 2004; Farago et al., 2009; Harrison et al., 2006; Hinks et al.,
2005; Karlson et al., 2008; Kokkonen et al., 2007; Majorczyk & Jasek, 2007;
Michou et al., 2007; Naseem et al., 2008; Orozco et al., 2005; Stahl et al., 2010)
(Brinkman et al., 1997; Mulcahy et al., 1996; Orozco et al., 2009b; Plenge et al.,
2007a; Thomson et al., 2007; Udalova et al., 2002; Waldron-Lynch et al., 2001)
(Okamoto et al., 2003)
(Barton et al., 2008a; Martinez et al., 2008; Orozco et al., 2008; Remmers et al.,
2007; Stahl et al., 2010; Zervou et al., 2008)
(Kobayashi et al., 2008)
(Barton et al., 2008a; Chang et al., 2008; Kurreeman et al., 2007; Plenge et al.,
2007b; Stahl et al., 2010; Zervou et al., 2008)
(Barton et al., 2008b; Stahl et al., 2010; WTCCC, 2007)
(Barton et al., 2008b; Raychaudhuri et al., 2008; WTCCC, 2007)
(Barton et al., 2008b; Raychaudhuri et al., 2008; Stahl et al., 2010)
(Barton et al., 2009; Stahl et al., 2010)
(Orozco et al., 2009a; Raychaudhuri et al., 2008; Stahl et al., 2010)
(Barton et al., 2009; Plenge et al., 2005; Raychaudhuri et al., 2008)
(Barton et al., 2009; Raychaudhuri et al., 2008; Zhernakova et al., 2007)
(Raychaudhuri et al., 2008; WTCCC, 2007)
(Fan et al., 2008; Ikari et al., 2005; Kang et al., 2006; Plenge et al., 2005; Suzuki
et al., 2003; Takata et al., 2008)
(Begovich et al., 2007b; Owen et al., 2007)
(Begovich et al., 2007b; Davis, 2007)
(Suzuki et al., 2008)
(Plenge et al., 2007a; Stahl et al., 2010)
*= Effect size as calculated as an average odds ratio, +=1-1.5, ++=1.5-2, +++=>2
28
Ethnicity
European
Effect size*
+++
European
++
European
++
Asian
European
+++
+
Asian
European
+
+
European
European
European
European
European
European
European
European
Asian
+
+
+
+
+
+
+
+
++
European
Asian
Asian
European
+
++
+
+
1.1.1.5. Environmental factors
RA is a complex disease with both genetic and environmental factors playing a role in the
manifestation and outcome of the disease. There are a range of environmental factors which
may influence RA. These include: demographics, socio-economics, smoking, diet,
psychological, hormonal and climate. These all have an effect to varying degrees.
Epidemiological studies provide the best support for the involvement of different
environmental elements. Epidemiology has addressed the common perception among lay
people that weather can influence RA disease activity (Symmons, 2003). Studies have shown
that the weather has no influence over disease progression (Symmons, 2003) but may
influence subjective pain levels (Gorin et al., 1999; Strusberg et al., 2002).
Socioeconomic factors have been widely investigated for evidence of impact on disease
outcome. Lower education levels were linked to increased rates of morbidity and mortality in
RA in three independent studies (ERAS Study Group, 2000; McEntegart et al., 1997; Pincus
& Callahan, 1985; Pincus & Callahan, 1988). It was suggested that lower socioeconomic
status is directly correlated with reduced access to medical care (Symmons, 2003). However,
Vlieland et al., conducted their research in Scotland where medical care is publically funded
and freely available and found low socioeconomic status was still significantly linked to RA
incidence (1997). An alternative hypothesis was that the prescription charges may have been a
negative factor in compliance (Symmons, 2003).
Smoking is now widely accepted as a negative contributing factor towards onset of RA
(Wilson & Goldsmith, 1999). It has been shown that smokers are 4 times more likely than
non-smokers to be affected (Stolt et al., 2003). Smokers who are homozygous for the HLADR SE (see section 1.1.1.4) are 21 times more likely to be affected than non-smokers with no
SE genes (Klareskog et al., 2006).As shown in Figure 1.3 this effect is only seen in
individuals positive for anti-CCP antibodies. This may be due to stimulation of the pathway
responsible for the production of anti-CCP (see section 1.1.1.3) initially in the lungs of all
individuals but then in as part of a wider autoimmune response in those carrying HLA-DR SE
genes.
29
Figure 1.3: Relative risk of developing rheumatoid arthritis in subjects exposed to different combinations of
smoking and HLA-DR shared epitope (SE) genes. A) RA patients who are positive for anti-CCP antibodies. B)
Results in RA patients who are negative for anti-CCP antibodies. Error bars represent 95% confidence intervals
(CI). In A) the value of 40.2 represents the upper boundary of the 95% CI for smokers with double copies of SE
genes. (Taken from Klareskog et al., 2006)
Interestingly smoking is known to increase the production of another diagnostic maker, RF
(see section 1.1.1.3). This is true in both RA patients and the wider population (Saag et al.,
1997; Tuomi et al., 1990; Wolfe, 2000). RA patients that smoke are far more prone to the
development of extra-articular features, such as nodules and rheumatoid lung disease, than
non-smokers (Banks et al., 1992; Harrison et al., 2001; Masdottir et al., 2000; Saag et al.,
1996; Struthers et al., 1981). Yet it has been shown that smoking has a positive effect in a
number of inflammatory diseases (Baron, 1996; Harrison, 2002). An RA study which found
this association differed from previous studies by including patients in the early stages of the
disease (Harrison et al., 2001). It was hypothesised that smoking may slow disease
progression by suppressing immune cell function, and thus joint inflammation, in the initial
stages of RA (Spector & Blake, 1988). Due to a lack of follow up studies, this effect remains
unconfirmed.
For many years it has been known that pregnancy can reduce RA symptoms in woman.
However, only recently have epidemiological studies shed light on the size of this effect.
Most pregnant RA patients find a 50-75% reduction in the activity of the disease (Symmons,
2003) and that 16% entered total remission (Barrett et al., 1999). Immune tolerance during
pregnancy may play a role (Symmons, 2003). This hypothesis was supported in a study by
Nelson et al., where patients with the greatest reduction in symptoms were those with the
most HLA class II differences between mother and child (Nelson et al., 1992). Both reduced
disease activity and remission very rarely last post-pregnancy (Oka & Vainio, 1966;
30
Symmons, 2003). Breastfeeding is also inversely correlated with risk for RA, with two
independent studies finding that breastfeeding for a period of 12 months or longer was
significantly protective (Pikwer et al., 2009; SE & Grodstein, 2004). There has also been
some evidence of reduced disease activity in RA patients receiving hormone replacement
therapy (HRT, (Bijlsma et al., 1987)).
Diet is one area where there has been extensive research. Omega-3 is an essential fatty acid
found in nuts, vegetable and oily fish (e.g. salmon, mackerel and eels). It has been shown that
increased dietary intake of these food groups may benefit RA patients (Shapiro et al., 1996;
Symmons, 2003). A meta-analysis of randomized controlled trials found that Omega-3 fatty
acids significantly reduce pain and stiffness in RA patients (Ariza-Ariza et al., 1998). Another
strain of supporting evidence is in the form of epidemiological studies in Southern Europe
(Italy and Greece) and Northern Europe (Cimmino et al., 1998; Drosos et al., 1992; Salvarani
et al., 1992). Southern European countries have high fish content diets and a reduced
frequency and severity of RA. Due to confounding variables between these two populations
(i.e. vitamin D intake, genetics) more robust investigation is required.
The behaviours that RA patients use to manage symptoms which induced stress (i.e. pain) can
affect the manifestation of the disease. It has been shown that those with negative coping
strategies (i.e. pessimism, restricting activity) are more likely to have reduced functioning and
depression than those with positive coping strategies (Brenner et al., 1994; Fournier et al.,
2002; Newman & Revenson, 1993).
Vitamin D (vitD) intake has been tentatively linked with RA (Hillman et al., 1994). The main
source of vitD is via irradiation of the skin. However, it is also available in the diet through
fish oils (Cantorna, 2000). VitD is traditionally known for its involvement in bone formation
and resorption through calcium homeostasis (Cantorna, 2000). However, recent studies have
provided evidence for a role of active vitD (1,25-(OH)2D3) in the immune response, with
supplementation preventing development of arthritis (Cantorna et al., 1998). VitD therapy
may assist in the formation of stronger bones and reduce autoimmunity (Cantorna, 2000).
Current research indicates that there may be a role for vitD receptor polymorphisms in
susceptibility of RA (Ranganathan, 2009) but this falls outside the scope of this review.
31
1.1.1.6. Treatments
There is currently no cure for RA but there are a range of medical and alternative therapies.
The five main classes of medical treatments are: non-steroidal anti-inflammatory drugs
(NSAIDs), glucocorticoids, disease modifying anti-rheumatic drugs (also known as slow
acting anti-rheumatic drugs, DMARDs), immunosuppressants and biological agents. NSAIDs
control pain, stiffness and inflammation but have no effect on disease progression (Rang et
al., 2003). They are normally used as “bridge” therapy to provide relief during the initial
stages of DMARD treatment. Some common NSAIDs include low dose aspirin and
ibuprofen. Glucocorticoids, such as prednisone, are also used as “bridge” therapy for
DMARDs and can decrease joint pain and swelling (Rang et al., 2003). These drugs have
immunosuppressant as well as anti-inflammatory properties and therefore can have a dosedependent effect on disease progression (this is limited by toxicity at high doses) (Rang et al.,
2003). There are a number of DMARDs in circulation, however the most popular is
methotrexate due to its low cost and effectiveness (Rang et al., 2003). These drugs are
generally utilised by patients with severe forms of the disease. DMARDs work through
immunosuppressive mechanisms and can decrease disease progression or even trigger a
remission of symptoms (Cash & Klippel, 1994). Immunosuppressant drugs have a wide
variety of actions, for example, azathioprine stops proliferation of the pannus tissue,
cyclosporine A inhibits cytotoxic T cell and IL-2 production and leflunomide prevents
proliferation of rapidly dividing cells (i.e. lymphocytes) (Rang et al., 2003). Biological agents
are the newest drug class for RA treatment. They are different to all the other classes of drugs
in that they block specific components of the inflammatory cascade. The development of new
technologies (discussed in section 1.1.1.4) has aided in identifying mediators in the RA
disease pathway and thus specific components can be investigated as therapeutic targets.
These drugs have limited availability in New Zealand and can be expensive. IL-1 inhibitors,
TNF-Į inhibitors, such as infliximab (a receptor fusion protein), and proteins blocking the
activation of T or B cells all fall in this class. TNF-Į inhibitors are currently the most
effective drug treatments for RA. Interestingly, the anti-nausea drug thalidomide, which
caused a great deal of public panic in the 1950s and 60s, is being utilised as a biological agent
in RA for its anti-angiogenic properties (Gutiérrez-Rodríguez, 2005). Alternative medicines
are also widely used by RA patients. Over 65% of patients who tried the following treatments
found them beneficial: chiropractic treatments, special diets (including high Omega-3 intake),
32
acupuncture and spiritual healing (Rao et al., 1999). The main reasons people tried an
alternative treatment approach was due to insufficient pain relief from prescribed drugs (Rao
et al., 1999). Increasing vitD levels is thought to decrease the risk of RA due to its roles in
bone metabolism and immune system modulation (Merlino et al., 2004). However,
preliminary studies of the influence of vitD in RA risk indicate an inverse association only in
older women (Merlino et al., 2004).
1.1.1.7. Related conditions
Many of the medical therapies for RA also have debilitating adverse effects and in some cases
can result in life threatening conditions (Kirwan, 1995). Some of these can be controlled in a
dose dependent manner. For example, strict dose control is used to prevent Cushing’s
syndrome, weight gain and infections (due to immunosuppression) following the use of
glucocorticoids (Chrousos, 1995). Long term use of NSAIDs (even at low doses) can cause
renal toxicity and increased incidence of cardiovascular events due to blockage of COX
enzymes (Emery et al., 1999). Even the widely used DMARD treatment, methotrexate, must
be closely monitored due to the severity of the adverse effects. Incidence of hepatic toxicity
(including liver cirrhosis) is common (>15%) with this drug, and gastrointestinal (GI) effects
are also an issue (Kremer et al., 2005; Tilling et al., 2006). Combination therapy is one way
that the incidence of side effects can be decreased. Immunosuppressants are generally given
in combination with DMARDs and this means that smaller doses of both drugs can be given,
whilst still achieving the desired effect (Katchamart et al., 2008; Smolen et al., 2007).
However, immunosuppressant drugs can be dangerous in the long term. This is because
suppression of the immune system prevents the body from efficiently fighting infections
(D'Haens et al., 2008).
33
1.2. NEUROPSYCHIATRICDISEASE
The most common form of neurological disease is mental illness. It is estimated that 1 in 4
people will experience some form of mental illness in their lifetime (Kessler et al., 2008).
There are many different types of mental illness, the most common including: schizophrenia
(see section 1.2.1), bipolar disorder, manic depression and psycho-affective disorder. A
negative stigma surrounds mental illness and as a result many affected individuals and their
families can be victims of discrimination (Sartorius, 2007). This can affect the quality of care
they receive and the access to appropriate treatments (Sartorius, 2007). As in RA, the onset of
mental disorders has been attributed to environmental stressors in the presence of an
underlying genetic predisposition.
1.2.1. SCHIZOPHRENIA
Schizophrenia (SZ) is a relatively common disorder, affecting 0.5-1% of the population and
frequently co-morbid with substance abuse, depression and suicide (Lewis & GonzalezBurgos, 2006). SZ is made up of two types of symptoms, positive and negative.
Hallucinations, paranoid delusions and thought disorder make up the positive symptoms and
these are often accompanied by negative catatonic behaviours such as avolition (lack of desire
or motivation), alogia (lack of additional, unprompted content seen in normal speech) and
affective flattening (lack of emotional reactivity) (Harrison, 1999). Catatonic behaviour is
associated in some context with many different disorders including psychotic episodes (SZ,
bipolar, depression and post-traumatic stress syndrome), infections (i.e. encephalitis), stroke
and discontinuation of prescription medications (i.e. benzodiazepines) making diagnosis of
SZ difficult. Diagnosis of this disorder is based on the current Diagnostic and Statistical
Manual (DSM) of Mental Disorders, this resource states that patients must have two or more
of these symptoms for a “significant proportion of time” over a one month period (American
Psychiatric Association, 2000). The International Statistical Classification of Diseases and
Related Health Problems (ICD) is another important reference for classification of medical
conditions (World Health Organisation, 1992). Both the ICD and the DSMIV use the same
classification code for SZ.
34
1.2.1.1. Trigger
A host of literature is available offering insight into the trigger of the first psychotic episode
associated with SZ. However, as with RA it appears that underlying genetic components
predispose for SZ but cumulative interaction with the environmental factors mentioned here is
needed for development of this disorder.
One environmental factor which may be playing a role in the initiation of SZ is exposure to
the infectious agent T. Gondii. This parasitic protozoa causes the disease toxoplasmosis which
has been linked to schizophrenic-like behaviours in humans (Torrey & Yolken, 2003). Four
independent studies have shown that T. gondii infection is elevated in individuals with SZ
(Delgado & GarcÌa, 1979; Gilmore et al., 2000; Qiuying et al., 1999; Yolken et al., 2001).
Ownership of cats (the definitive host of T.Gondii), in childhood has also been linked to SZ
(Torrey & Yolken, 2001). In SZ patients, cat ownership in childhood was over 50% compared
to 38% of controls (Torrey et al., 2000; Torrey & Yolken, 1995). Exposure to T. Gondii has
also been implicated in development of RA (see section 1.1.1.1) indicating that an immune
basis for SZ may be a possibility.
The onset of schizophrenia is also varied between individuals. Messias et al., found in a longterm follow-up study that 50% of patients had an acute onset while the remainder had an
extended prodrome (2007). The appearance of negative symptoms often starts around five
years before the initial psychotic episode (Hafner et al., 1999).
1.2.1.2. Mechanism
In the hundred years since Kraeplin first described this disorder, little mechanistic information
has been obtained (Harrison, 1999). In fact, only 20 years ago the challenge for
neuropathologists was to confirm that SZ is in fact a disease of the brain (Harrison, 1999).
Various studies have indicated different biological factors in the initiation and progression of
this disease, with lack of consistency being a major issue (Harrison, 1999). A number of
different hypotheses have been proposed to explain the neuropathology of SZ. The
neurodevelopmental and neurochemical are discussed in more detail here.
35
The neurodevelopmental hypothesis implicates an interaction between multiple susceptibility
genes and environmental factors early in life results in altered brain development leading to
the appearance of psychosis in adulthood (Jarskog et al., 2007). This hypothesis favours an
effect caused in early development which remains static until its ‘unmasking’ during
maturation (i.e. puberty). However, a longitudinal neuroimaging study has shown that if the
effect is neurodevelopmental, it is also progressive (Lieberman et al., 2001). This study and
others like it illustrate a progressive loss of gray matter in several areas of the brain, including
the hippocampus and the prefrontal cortex, in individuals who later develop psychosis (Gur et
al., 1998; Lieberman et al., 2001; Nelson et al., 1998; Pantelis et al., 2003).
Animal studies have shown that schizophrenic-like behaviours are seen when there is
increased dopamine signalling to the striatum (striatal hyperdopaminergia) (Wiedholz et al.,
2007). A recent case control analysis has replicated this finding in humans (effect size =1.25)
(Huang et al., 2009). They also found that individuals with prodromal SZ symptoms had an
intermediate level (effect size =0.75) of hyperdopaminergic activity in the striatum (Howes et
al., 2009). This indicates that dopamine over-activity may precede SZ onset, however the
sample size was small (n=43) and requires replication. Interestingly, brain imaging studies
have shown an opposite effect in the prefrontal cortex (PFC) of SZ patients. Here dopamine
levels are reduced compared with control subjects (Okubo et al., 1997). The authors suggest
that the lower levels are seen due to decreased binding to dopamine (D1) receptors. They also
show that low levels of dopamine in the PFC are related to increased severity of negative SZ
symptoms (Okubo et al., 1997). Future research in this area has found that activity in the PFC
is inversely correlated with dopamine levels in the striatum (Meyer-Lindenberg et al., 2002).
Additionally, blocking transmission of glutamate from the ventral tegmental area* increases
dopamine release in the striatum and decreases its release in the PFC (Takahata &
Moghaddam, 2002). This is effectively a replication of the pathology seen in SZ patients.
The neurochemical hypothesis states that abnormal dopamine levels in the PFC and striatum
of SZ patients may be the result of glutamate transmitter hyperactivity in the primary cortex
(Reviewed by Laruelle et al., 2003). This hypothesis provides a better explanation for
symptoms of SZ than the neurodevelopment hypothesis. This is because both negative and
*
The Ventral tegmental area (VTA) is a cluster of neurons found in the midbrain. This area is the origin of
dopaminergic cells that feed into the dopamine system (Reviewed by Bannon & Roth, 1983)
Alamanos Y, Voulgari PV, Drosos AA (2006). Incidence and Prevalence of Rheumatoid Arthritis,
Based on the 1987 American College of Rheumatology Criteria: A Systematic Review. Seminars in Arthritis and Rheumatism 36: 182-188.
36
positive symptoms can be accounted for by the biological pathways (i.e. dopamine reward
pathway) affected by dysfunction of dopamine signalling (Jarskog et al., 2007).
The discovery of the involvement of dopamine in the pathophysiology of SZ has lead to an
understanding of the effectiveness of anti-psychotic drugs. It is now known that atypical antipsychotics (i.e. Clozapine) improve the symptoms of SZ by partially blocking DA receptors
to prevent over-activity of dopamine in the striatum. Some (i.e. Clozapine) also increase the
levels of dopamine in the prefrontal cortex (Rollema et al., 1997). Should additional
supporting evidence for this neurochemical hypothesis become available, there is also the
potential for the development of novel treatments outside the dopamine pathway.
1.2.1.3. Genetic Factors
Family and twin studies have shown that genetics play an important role in SZ (McDonald &
Murray, 2000). An individual’s risk will increase ~10 fold if they have a first degree relative
(see Table 1.5) with this disorder, over 40 fold if a mono-zygotic twin is affected (Cardno et
al., 1999) and nearly 50 fold if both parents suffer from SZ (Gottesman). A meta-analysis of
twin studies has estimated the genetic contribution of SZ to be over 80% (Sullivan et al.,
2003).
Table 1.5: Morbid Risk of Schizophrenia for Relatives of Schizophrenic Patients.
(Taken from Tsuang, 2000)
Relationship
General population
Spouses of patients
First cousins
Aunts/Uncles
Nieces/nephews
Grandchildren
Half-siblings
Parents
Siblings
Children
Siblings with 1 SZ parent
Dizygotic twin
Monozygotic twin
Children with 2 SZ parents
% Shared genes
NA
NA
12.5
25
25
25
25
50
50
50
50
50
100
100
SZ = Schizophrenic
37
Risk (%)
1
2
2
2
4
5
6
6
9
13
17
17
48
46
Numerous studies have been involved in pinpointing the genetic components responsible for
susceptibility to SZ (Middleton et al., 2002; Rubinstein, 1997; Schwab et al., 2005) (see
Table 1.6). However, SZ is a complex disease and therefore the result of multiple gene
interactions, each with small to moderate effect sizes. Similar to recent RA studies (WTCCC,
2007) SZ research groups are turning towards GWAS for identification of susceptibility
genes. 2007 saw the publication of the Genetic Association Information Network (GAIN),
another GWAS utilising large cohort sizes for the study of numerous disorders (including
Attention Deficit Hyperactive Disorder, ADHD, diabetic neuropathy, major depressive
disorder, psoriasis,
and bipolar I disorder) (As described in The GAIN Collaborative
Research Group, 2007). The study of SZ used two sample sets, one of European ancestry
(1440 cases and 1469 controls) and the other with African ancestry (1280 cases and 1000
controls) (GAIN, 2007) Many allelic markers reached genome wide significance for
association with SZ in this study, for example SNPs within the genes disrupted in
schizophrenia 1 (DISC1) and v-akt murine thymoma viral oncogene homolog 1 (AKT1).
Genes implicated in neurological and neurodevelopmental pathways hold special interest for
researchers. One such gene is the neurotransmitter, Neuroregulin-1 (NGR1). In 2006 a metaanalysis of 14 studies found no association between single NGR1 variants and SZ but did find
a significant association (p = 0.016) for combined NGR1 haplotypes (Munafo et al., 2006).
There is also evidence to sugggest involvement of immune system genes in the development
of SZ. A number of studies have indicated genes in the immunoregulatory rich 6p22.1 region
are likely associated with SZ suspectibility (Purcell et al., 2009; Shi et al., 2009b; Stefansson
et al., 2009). Histone protein production genes (DNA transcription/repair) and the human
leucocyte antigen complex (reulation of the immune system) are both included in this region
(Muller & Dursun, 2010; Rogers & Goldsmith, 2009). In addition, the transducer of ERBB2
number 1 (TOB1) pathway, which regulates anti-proliferative reached significance in a recent
SZ GWAS analysis (p = 0.018) (Jia et al., 2010).
Other genes with biological plausibility (functions shown in Table 1.6) include DISC1 (see
section 1.4.3), regulator of G-protein signalling 4 (RGS-4), catechol-O-methyl-transferase
(COMT) and dysbindin (DTNBP1). To illustrate the importance of DISC1 function in
neurodevelopment, a study has shown that interruption of this gene illustrates many of the
same aspects of neuropathology of SZ (Kamiya et al., 2005). The dopamine reward pathway
has been linked to SZ and genes that play a role have already shown association in the
38
literature. For example, the vesicular monoamine transporter protein accumulates dopamine
among others into vesicles, an activity that is elevated in SZ patients (Howes & Kapur, 2009).
Research has shown that a SNP (rs2270641) in this gene is associated with SZ (OR=1.63)
(Howes & Kapur, 2009). AKT1 is another dopamine pathway influencing gene associated
with SZ. A 2008 study identified a coding variant (AKT1_rs1130233) which affected brain
measures relating to dopaminergic function (Tan et al.). Further research is required to
provide evidence implicating whole pathways in the development of SZ. AKT1 and GRIK2
are currently among the regions of interest for SZ researchers (see section 1.4, (Steer et al.,
2007)).
39
Table 1.6: Potential Schizophrenia susceptibility loci with their chromosome position, function, supporting literature and power.
Loci
COMT
Position
22q11
Function
Metabolism
NRG1
8p12-21
Neurotransmission, Myelination
DTNBP1
6p22
Organelle biogenesis
RGS4
DISC1
1q21-23
1q42
GTPase activating protein
Neuronal migration, cAMP signal
transduction
ZNF
10q23-33
Regulation of transcription
ADAMTSL3
GRIK3
15q25
1p33-34
Neurotransmitter receptors
GRIK4
PTPN21
11q22
14q31
NOTCH4
DAOA
AKT1
6p21
13q34
14q32
Neurotransmitter receptors
Regulation of cell growth and
differentiation
Cell fate
NMDA regulator
Growth factor induced neuronal
survival
References
(Egan et al., 2001; Herken & Erdal, 2001; Shifman et al., 2002)
(Gu et al., 2009; Li et al., 2006; Ohmori et al., 1998; Park et al., 2002)
(Green et al., 2005; Li et al., 2006; Shi et al., 2009a; Stefansson et al., 2003)
(Li et al., 2006; Zhao et al., 2004)
(Bray et al., 2005; Edwards et al., 2008; Funke et al., 2004; Li & He, 2007;
Riley et al., 2009a; Van Den Bogaert et al., 2003; Williams et al., 2004a)
(Chowdari et al., 2002; Talkowski et al., 2006; Williams et al., 2004b)
(Cannon et al., 2005; Hodgkinson et al., 2004; Schumacher et al., 2009;
Tomppo et al., 2009; Zhang et al., 2006a)
(Chen et al., 2007)
(Need et al., 2009; O'Donovan et al., 2008; Riley et al., 2009b; Shi et al.,
2009a)
(Need et al., 2009)
(Begni et al., 2002; Djurovic et al., 2009)
(Ahmad et al., 2009)
(Pickard et al., 2006)
(Shi et al., 2009a)
(Need et al., 2009)
(Li et al., 2006)
(Bajestan et al., 2006)
(Xu et al., 2007)
*= Effect size as calculated as an average odds ratio, +=1-1.5, ++=1.5-2, +++=>2
40
Ethnicity
European
Asian
European
Asian
European
Effect Size*
++
++
+
+
+++
European
European
++
+++
Asian
European
+
+
European
European
Asian
European
European
+
++
++
+
+
European
Asian
European
Asian
+
+
+++
+
1.2.1.1. Environmental Factors
Environmental risk factors also play a role in the development of SZ. In 2000 McDonald et
al., suggested that the environmental influences on SZ occurred in a three step approach: early
effects on neurodevelopment, childhood risk factors and events proximal to the onset of the
disease (
Figure 1.4). All of these effects have been linked to an earlier onset of SZ (McDonald &
Murray, 2000) Early effects on neurodevelopment include genetic factors (discussed in
section 1.2.1.3) and obstetric complications (OCs). Urban living, social isolation and low self
esteem are all childhood risk factors. Events proximal to the onset of the disease encompass
drug use, migration and adverse life events (or social stress). All of these factors (see Figure
1.5) will be discussed in this section in relation to recent meta-analyses, which provide the
best support for association.
!
"#$%&'()*)+)(,!
-&.&$!
!
!
!
732+,!%&2&*23+!
839325$!
!
!
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!
!
/00)123()4.!
!
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"4%)3+!356&2$)(,!
!
!
!
!
!
-./!012/345!6*
!
!
:)(,!+);&!
!
<2#1!3*#$&!
Figure 1.4: The influence of risk factors over time on the development
of schizophrenia. (Taken from McDonald & Murray, 2000)
41
!
Figure 1.5: Environmental factors influencing the development of
SZ and their relative risks. Undated from (Sullivan, 2005)
Early effects focus on OCs; these are broadly grouped as: complications of pregnancy
(bleeding, preeclampsia etc), abnormal growth and development (low birth weight etc), and
complications during labour (asphyxia, emergency caesarean section etc) (Cannon et al.,
2002). Various studies associating the following factors with an increased risk of up to 7-fold
in the general population (see Figure 1.5): maternal influenza during pregnancy (Wright et al.,
1995), premature birth, low birth weight, perinatal brain damage and pregnancy bleeding
(Hultman et al., 1999; Jones et al., 1998; Wright et al., 1995). These effects show the
strongest association with SZ in males (McDonald & Murray, 2000). In addition, research
indicates a role for prenatal malnutrition, extreme prematurity and hypoxia as the leading OCs
implicated in development of SZ (Dalman et al., 1999; Isohanni et al., 2005; Rosso et al.,
2000; Susser & Lin, 1992; Zornberg et al., 2000).
There is robust evidence for maternal illness during pregnancy causing an increased risk for
SZ (Brown et al., 2004; Brown et al., 2002; Mednick et al., 1988; Munk-Jorgensen, 1987). A
study in 2000 found maternal infection with the rubella virus during pregnancy provided a
relative risk for SZ of 5.2 (Brown et al.). There is also support for viral infection during early
development. A relative risk of 5 was also found for toddlers (average age of 26 months)
infected with meningitis during the 1971-1974 epidemic in Brazil (Gattaz et al., 2004). This
evidence also provides support for the neurodevelopmental hypothesis of SZ (see section
1.2.1.2) (Murray & Lewis, 1987; Weinberger, 1987).
42
Season of birth is another factor with strong support (Davies et al., 2003; Messias et al., 2007;
Torrey et al., 1997). Individuals with SZ are more likely to be born during the winter months
(5-8% in meta-analysis), an effect seen in both hemispheres (Torrey et al., 1997). Two
hypotheses for this effect have been proposed: that the mother is in the second trimester at the
height of the flu-season and that risk is increased via the interaction of seasonal effects and
genetic susceptibility (Messias et al., 2007).
Advancing paternal age has been investigated as a risk factor for SZ since the mid-20th
century (Messias et al., 2007). More recently epidemiological studies from Europe and
America have provided increased support (Aleman et al., 2003; Brown et al., 2002; Byrne et
al., 2003; Dalman & Allebeck, 2002; Malaspina et al., 2001; Zammit et al., 2003). It was
found that the relative risk for SZ was elevated with increasing paternal age, with a relative
risk of 2.96 in the 55 years age group compared with the 20-24 years group.
Individuals that go on to develop SZ often differ from their peers early in childhood (Messias
et al., 2007). These children are more inclined to be socially anxious and isolate themselves
from their peers (Howes et al., 2004). They reach developmental milestones later than their
peers (Isohanni et al., 2001; Jones, 1997; Jones et al., 1994), have lower levels of cognitive
functioning (David et al., 1997; Gunnell et al., 2002) and educational achievement is
impaired (Cannon et al., 1999; Done et al., 1994; Isohanni et al., 1998; Jones et al., 1994). It
has been shown that SZ patients had significant neurological and motor skills development
issues in the first decade of life (Messias et al., 2007). A study by Poulton et al., has shown
the best indicator for SZ risk is an interview at age 11 years (2000). The children were
interviewed regarding quasi-psychotic phenomena (such as “have other people ever read your
mind”) and scored based on strength of response. These individuals were then tested at age 26
years for schizophreniform disorder. Risk for SZ was increased 16 fold for individuals with
strong affirmative answers to one or more of the questions.
In addition to these factors, events in adulthood also play a significant role in the risk of
developing SZ. Numerous studies have linked urban living and migration with increased
incidence of SZ in a range of populations (Reviewed by McDonald & Murray, 2000).
Allardyce et al., found a 2-fold higher risk of SZ associated with living in London versus
living in rural Scotland (2000). Many other studies have reported similar findings with the
43
risk of SZ doubled in urban areas compared with rural (Harrison et al., 2003; Lewis et al.,
1992; Mortensen et al., 1999; Pedersen & Mortensen, 2001; Spauwen et al., 2004). Migration
as a social isolator has a negative impact on an individual’s stress levels. A recent metaanalysis examining SZ in first and second generation migrants found an overall relative risk
of 2.9 (Cantor-Graae & Selten, 2005). The relative risk was much higher for second
generation migrants (OR=4.5), and migrants with a different skin colour than the majority of
people in the destination country (OR=4.4, see Figure 1.6). Authors of the meta-analysis
hypothesise that these increased risk ratios are due to the increased stress of living in a new
country and for the second generation migrants the stress of being treated as foreign in their
birth country (Cantor-Graae & Selten, 2005; Selten & Cantor-Graae, 2005).
44
Figure 1.6: Risk of schizophrenia in non-white ethnic minorities according to
their proportion in their local area. (taken from Howes et al., 2004)
RR = relative risk
Individuals with SZ often have mutations in the dopamine reward pathway (see section
1.2.1.3) causing increased sensitisation of receptors to dopamine (Chen et al., 2003). This
effect provides support for the neurochemical hypothesis of SZ development (see section
1.2.1.2). Many recreational drugs are regulated via this system including cannabis and
methamphetamines. Those users who are genetically susceptible to SZ presumably have
increased susceptibility to the psychogenic effects of the drugs (Howes et al., 2004). This
statement is supported by the findings of the New Zealand Birth Cohort Study (Moffitt et al.,
2001). Information on the cannabis consumption of individuals was obtained at 15 and 18
year olds by interview. At age 26, the same individuals were then tested for schizophreniform
psychosis. It was found that consumption of cannabis at 15 years was associated with a
relative risk of SZ of 4. Other papers published on the cannabis consumption issue, report
relative risks between 2-25 (Arseneault et al., 2002; Van Os et al., 2002; Weiser et al., 2003;
Zammit et al., 2002). Howes and colleagues found evidence for a dose dependant relationship
between cannabis use and psychosis (2004). Although most studies agree that drug abuse and
dependency seems to be an influencing factor in triggering episodes, the size of this effect is
not consistently reported. Bersani et al., state that drug or alcohol abuse precedes the first
symptoms of SZ in 67% of patients (2002), Bühler et al., found 62% (2002) and for
Hambrecht et al., this number is only 33% (2000).
45
1.2.1.2. Treatment
Little is known about the pathophysiology (Lewis & Levitt, 2002) and therefore successful
therapeutic treatments are limited. Remission or improvement of symptoms is seen as a more
achievable goal than a cure for this disorder. Most treatments for SZ are aimed at binding
receptors in the dopamine reward pathway (see section 1.1.1.4). Anti-psychotic medications
(typical anti-psychotics and atypical or second generation psychotics) are available for the
management of positive symptoms. Typical anti-psychotics are available as either highpotency (i.e. fluphenazine) or low-potency (i.e. chlorpromazine) depending on the needs of
the patient (Kane, 1999). High-potency drugs can be given as a depot (slow release
subcutaneous injection) to ensure drug compliance with patients who refuse treatment (Kane,
1999). Clozapine is an atypical anti-psychotic with lower incidence of adverse effects than
other prescribed drugs (Kane, 1999). It is primarily used in treatment-resistant SZ
(unresponsive to at least two different anti-psychotics). Cognitive behavioural treatment
(CBT) is a new therapy for SZ patients with promising results (Zimmermann et al., 2005).
CBT is aimed at altering problematic emotions and social behaviours through recognition of
their consequences (Zimmermann et al., 2005).
Many patients with SZ self-medicate with substances such as cannabis (28%), smoking (74%)
and alcohol (42%) for relief from negative symptoms (Bellamy et al., 1992; Hambrecht &
Hafner, 2000). However, schizophrenic drug abusers have a higher incidence of depression
and alcohol abusers are more likely to engage in self-destructive behaviours (Hambrecht &
Hafner, 2000).
46
1.3. AGENETICLINKBETWEENRHEUMATOIDARTHRITISAND
SCHIZOPHRENIA
1.3.1. EXPERIMENTAL EVIDENCE
Experimental evidence suggests that factors which contribute to RA susceptibility are
protective against SZ and vice-versa (Oken & Schulzer, 1999; Spector & Silman, 2004). The
correlation between these two disorders has been highlighted in a number of publications, as
shown in Table 1.7. A meta-analysis of studies published before 1999 described the relative
risk of schizophrenic patients developing RA compared with the general population as less
than 0.1 (Oken & Schulzer, 1999). Recent research on the link between these two disorders
has been centred on a common disease pathway (Lewis & Gonzalez-Burgos, 2006). This
suggests that either the pathway responsible for RA has an active component in the
neurological system or that SZ has an autoimmune basis (Kipnis et al., 2006; Torrey &
Yolken, 2001). Although this hypothesis would neatly facilitate a mechanistic relationship, no
evidence has been produced in its support.
Table 1.7: The incidence of rheumatoid arthritis in schizophrenic patients and controls. All control sets have
another psychiatric illness unless otherwise stated. (Updated from Oken & Schulzer, 1999).
Sample Size
Data Source
Location
Cases
Controls
RA frequency, n
(%)
Cases
Controls
(Ross et al., 1950)
Quebec
800
808
0
4 (0.49)
(Pilkington, 1956)
(Mellsop et al., 2008)
England
Australia
130
301
188
3,157
1 (0.77)
0
(Österberg, 1978)
Sweden
40,426
142,406
(Baldwin, 1980)
(Mohamed et al., 1982)
England
USA
2,314
111
5,404
51
19
(0.047)
2 (0.09)
0
5 (2.66)
234
(7.41)
149
(0.10)
23 (0.43)
3 (5.88)
(Allebeck et al., 1985)
(Oken & Schulzer,
1999)
(Oken & Schulzer,
1999)
(Eaton et al., 2006) *
Sweden
Canada
1,190
27,630
4,599
202,342
2 (0.17)
30 (0.11)
USA
1,323
661
1 (0.08)
Denmark
7,704
192,590
15 (0.19)
*controls were free from psychiatric illness
47
19 (0.41)
900
(0.44)
2 (0.30)
336
(0.17)
Odds ratio
0.11 (0.0062.08)
0.28 (0.03-2.46)
0.02 (0.0010.33)
0.45 (0.28-0.72)
P
value
0.14
0.25
0.006
0.001
0.20 (0.05-0.86)
0.06 (0.0031.23)
0.41 (0.09-1.74)
0.24 (0.17-0.35)
0.03
0.07
0.25 (0.02-2.75)
0.26
1.15 (0.055-2.5)
<0.05
0.22
0.001
1.3.1. THEORIES FOR THE ASSOCIATION BETWEEN RA AND SZ
There have been many proposed explanation for the negative correlation between RA and SZ,
including: a genetic, biochemical or immunological link or an infection which can trigger
either disease (Torrey & Yolken, 2001).The genetic approach has generally been focused on
the influence the HLA system (see section 1.1.1.4) has on the two diseases. Alleles of the
HLA-DRB1 gene have most frequently been reported in association with SZ (reviewed by
Wright et al., 2001). However, there is also evidence to suggest the HLA-D6, HLA-D8 and
HLA-DQB1 genes may also be playing a role (Wright et al., 2001).
Another theory is that SZ has an autoimmune basis and this is the hypothesis adopted by the
current study. Literature published in this area of interest has been focused on antinuclear
antibodies, antibrain antibodies (Heath & Krupp, 1967) and other autoimmune deficits
(reviewed by Ganguli et al., 1993; Strous & Shoenfeld, 2006).The hypothesis of the current
study is that a genetic variant in a shared biochemical pathway is a key factor in the
relationship between RA and SZ. This research will investigate genes that play a role in both
the autoimmune and neurological systems to find a functional variant responsible for the
negative association between RA and SZ. The focus will be centred on genes which contain
an allele that confers susceptibility to one disorder and protection against the other.
Biologically this will be illustrated as tissue-specific opposing signals within a broad
signalling pathway.
1.3.2. SIGNIFICANCE
Not only will identifying genes associated with these disorders increase understanding of
polygenic disease, but the putative pathway responsible for the negative correlation between
RA and SZ has huge potential as a therapeutic target. It may be possible to manipulate the
pathway in such a way that the body is deceived into believing one phenotype is active and
thus protected against the other. It is therefore important to identify any genes which may be
responsible for the link between the two disorders.
48
1.4. CANDIDATEGENES
1.4.1. GENOME WIDE ASSOCIATION
ASSOCIATED GENES
SCANNING
FOR
IDENTIFYING
Many challenges surround the identification of genes involved in complex disease. One of the
most important factors is the ability to get complete coverage of the genome when scanning
for associated genes. The traditional method of gene identification was the candidate gene
approach, seeing one or only a few genes targeted in each study (Khoury et al., 2009).
However the human genome encompasses a great deal of variation between individuals with
12 million SNPs having been described (Pearson & Manolio, 2008). Therefore identifying all
the possible associations using the candidate gene approach is unrealistic. The ability to
undertake GWAS studies is the direct result of the completion of the Human Genome Project
and the HapMap Project, while the commercial availability of dense genotyping chips makes
the studies technically feasible (Huang et al., 2009).
The chips make use of tagging SNPs* to ensure the highest possible proportion of the genome
is represented in the scan. There are a range of commercial chips available which can
currently include up to 1,000,000 tagging SNPs. Large case-control sample sets of individuals
are then scanned for SNP alleles which are significantly over or under-represented in the
disease group compared with the controls (Pearson & Manolio, 2008).
However it is
statistically improbable that the true disease associated variant is one of those included in the
gene chip (Marchini et al., 2007). The information obtained on genotyped SNPs by GWAS
scan can be thought of as predictors for untyped variants (Marchini et al., 2007). The
imputation approach provided a way to identify genotypes for SNPs not directly included in a
commercial chip. This technique involves predicting the genotype of the missing variant by
utilising information obtained from: the observed data (i.e. using the genotype calling
program CHIAMO), the latest HapMap build (for estimation of a fine-scale recombination
map) and a population genetics model (i.e. HAPGEN). This type of data has a greater degree
of uncertainty (accuracy = 98.4%) than that obtained by direct genotyping (Marchini et al.,
*
Tagging SNPs fall in a region of the genome which has a high percentage of linkage disequilibrium. The
genotype of these SNPs is therefore representative of all the variation in that region (Pearson & Manolio, 2008).
TA,ManolioTA(2008).HowtointerpretagenomeͲwideassociationstudy.Jama299(11):1335..
49
Pearson
2007). Yet it provides necessary information when a SNP of interest has not been genotyped
in published GWAS scans.
GWAS have been particularly important in the study of diseases where no one gene is having
a great effect. In SZ, for example, many SNPs studied under the candidate gene approach
were insignificant for association. This can either be due to a lack of power in the studies or
the gene genuinely not being associated. Many of these candidate genes have been reinvestigated under GWAS. Genes which then came up significant usually had a small effect
size, illustrating that the previous studies lacked the power necessary to detect the association
(Need et al., 2009). GWAS has therefore been very successful in identifying new gene
associations, such as IL23R in inflammatory bowel disease (Duerr et al., 2006), and providing
support for genes identified under the candidate gene approach, for example COMT in SZ
(Sullivan et al., 2008) and PTPN22 in RA (WTCCC, 2007). Additionally, the hypothesis-free
approach of GWAS studies has allowed the identification of pathways not previously
considered appealing in disease association, for example the Disrupted in Schizophrenia 1
(DISC1, see section 1.4.3) pathway in SZ (Need et al., 2009) (Stahl et al., 2010).
In the past three years the number of published GWAS studies has increased exponentially
(see Table 1.8) and therefore caused a surge in the number of published genes. However,
many of these studies lack large enough sample sets for replication and therefore their ability
to robustly define associations to disease is limited. This problem has been widely discussed
(Scott et al., 2007; Zeggini et al., 2008) and has facilitated an increase in the popularity of
meta-analysis studies (see Table 1.8, in detail in section 1.5). Yet even with both types of
analysis the portion of genetic varience discovered thus far for complex diseases, including
SZ and RA, is relatively small.
50
Table 1.8: Trends in numbers of published articles on human genome epidemiology, meta-analysis and genomewide association studies and numbers of genes studied, by year, 2001-2009*. (Updated from Khoury et al.,
2009)
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
No. of Genes
644
809
842
1143
1324
1902
2228
3478
4970
No. of
Diseases
Total
691
862
883
1026
1092
1120
1304
1386
1242
2493
3200
3478
4282
5030
5357
7213
7772
8678
No. of Articles Published
GWAS
Metaanalyses
0
29
0
40
3
63
0
77
5
103
11
146
104
195
163
237
274
348
*=Data were obtained through a HuGE Navigator query (http://www.hugenavigator.net) conducted on February
19th 2010.
1.4.2. AKT1
AKT1 is necessary for growth factor-induced neuronal survival in the developing nervous
system (Hemmings, 1997) and has been implicated in SZ (Ikeda et al., 2004; Schwab et al.,
2005). It plays a major role in cell survival and function as well as angiogenesis (Downward,
2004). Of particular importance is that the AKT1 pathway is functionally implicated in RA
(Kim et al., 2002) and supported by genetic data from the WTCCC (2007). Data for SNPs
within AKT1was extracted from the WTCCC. An independent association analysis (via
PLINK see section 2.4.7.1) revealed two SNPs which reach significance (p<0.05) and two
trending towards significance (p<0.06). AKT1 has an effect downstream of GRIK2 (see
section 1.1.1) and has been tentatively implicated in both RA and SZ. Therefore the genes in
this pathway fit the criteria for the common neurological/autoimmune basis needed to link
these two disorders. AKT1 will be investigated in the current study as a candidate gene for
RA.
51
Figure 1.7: A summary of the interaction between the candidate genes
of interest with RA and SZ.
Figure 1.8: Activation and inhibition of AKT1 through a multiple signalling pathway. (Taken from Invitrogen,
2010)
52
1.4.1. GRIK
Recent data suggests a common pathway involving GRIK2 may link RA and SZ (see Figure
1.7). A yeast two hybrid study indicated that GRIK2 interacts with the protein periplakin
(PPL) (Myriad Genetics, unpublished), which is primarily responsible for the structural
integrity of membrane receptor anchors (Beekman et al., 2004). It has been shown that PPL
interacts with both the proto-oncogene AKT1 (van den Heuvel et al., 2002) and the immune
response mediator CD64 (membrane glycoprotein which has a high affinity for IgG-type
antibodies) (Beekman et al., 2004). PPL mediates localisation to the intracellular network by
binding to the PH domain of AKT1 (Song et al., 2007). GRIK2’s potential as a candidate gene
in this thesis is also supported by unpublished data illustrating it is over-expressed in the
rheumatoid synovium (Paul Hessian, Physiology, unpublished data).
1.4.2. AKT1 PATHWAY GENES
1.4.2.1. PTEN
Phosphate and Tensin Homolog deleted from chromosome ten (PTEN) is involved in the
AKT1 pathway through dephosphorylation of PIP2 to PIP3 (see Figure 1.8), preventing
AKT1’s phosphorylation and consequential activation (Tarnawski et al., 2010). PTEN is a
tumour suppressor and mutation of this gene has been associated with a wide range of human
cancers (Goberdhan & Wilson, 2003). PTEN is involved in cell cycle arrest and apoptosis
through AKT1 (Huber et al., 2006) but also regulates cell adhesion, growth and migration
(Goberdhan & Wilson, 2003; Stambolic et al., 1998). PTEN knockout mice have increased
expression of CCND1 (see section 1.4.2.5) and decreased disease latency (Rodriguez et al.,
2009). Clinically, PTEN is only found at very low levels in synovial fibroblasts invading
cartilage in RA patients (Huber et al., 2006). This suggests a role for defective apoptosis in
synovial hyperplasia and may therefore be an interesting candidate for association with RA
(Huber et al., 2006).
53
1.4.2.2. PDK1
Pyruvate Dehydrogenase Kinase, isozyme 1 (PDK1) is involved with the mediation of
homeostasis. PDK1 directly activates AKT1 (see Figure 1.8), which in turn provides the
survival signal for mast cells infiltrating the rheumatoid synovium (Sawamukai et al., 2007).
Cell proliferation and biosynthesis of proteins also occur downstream of PDK1 (Rodriguez et
al., 2009). This protein must be phosphorylated to be active, however it is uncertain how this
is achieved (Sawamukai et al., 2007). Inhibition of this protein may be pivotal in disrupting
cell survival.
1.4.2.3. BAD
Bcl-2-associated agonist of cell death (BAD) is part of the Bcl-2 family responsible for
initiating apoptosis (Lahiry et al., 2010). It forms heterodimers with Bcl-xl and Bcl-2 to
reverse their anti-apoptotic activity (Scatizzi et al., 2010). AKT1 phosphorylates BAD which
inhibits the mitochondrial death cascade and therefore promotes cell survival (see Figure 1.8)
(Lahiry et al., 2010). Upregulation of this pathway could result in inappropriate cell survival
as in the rheumatoid synovium in the form of a pannus.
1.4.2.4. GSK3E
Glycogen synthase kinase 3 beta (GSK3E) is a serine-theronine kinase with regulatory
functions which include energy metabolism, body pattern formation and neuronal cell
development (Plyte et al., 1992). As shown in Figure 1.8 GSK3E is directly regulated by
AKT1 (section 1.4.2) and inhibits CCND1 (section 1.4.2.5), NFAT (section 1.4.4) and E-CTN
(section 1.4.2.6).This gene is of particular interest in this study as it has been implicated in
both neurological and bone disease. Pajak and colleagues investigated the role of GSK3E in
Alzheimer’s disease (2009). It was found that GSK3E activity increased production of
amyloid-E and therefore amyloid-E-dependent pathogenesis (Pajak et al., 2009). This includes
a local plaque-associated inflammatory response and cytological disturbances both typical for
Alzheimer’s (Pajak et al., 2009). Polymorphisms in this gene have been positively associated
with Alzheimer’s (Schaffer et al., 2008) and bone disease in myeloma (Durie et al., 2009).It
has also been shown that active GSK3E may regulate apoptosis of dendritic cells (Escribano
et al., 2009).
54
1.4.2.5. CCND1
Cyclin D1 (CCND1) is involved in the transition of the G1 cell growth phase to the S phase of
DNA replication (Tashiro et al., 2007). Overexpression of this protein in fibroblasts has
resulted in shortening of the G1 phase (Tashiro et al., 2007). It has also been associated with
many cancers, with polymorphisms in CCND1 linked with breast and cervical cancer in
particular (Castro et al., 2009; Lu et al., 2009; Thakur et al., 2009).
1.4.2.6. Beta-CTN (E-CTN)
Cadherin-associated protein beta (E-CTN), is a protein encoded by the gene CTNNB1 located
at 3p21 (Kraus et al., 1994). It is part of the complex of proteins that maintain epithelial cell
layers, in particular anchoring the actin cytoskeleton and signalling the end of cell division. ECTN is part of the Wnt signalling pathway recently implicated in a range of different human
cancers (Park et al., 2009). It has also been shown that after interaction with the proinflammatory mediator LTD4, E-CTN becomes associated with the anti-apoptotic protein,
Bcl-2 (Mezhybovska et al., 2006). As shown in Figure 1.8, E-CTN is directly regulated by
GSK3E inhibition.
1.4.3. DISC1
Disrupted in Schizophrenia 1 (DISC1) was first identified as associated with SZ in a large
Scottish pedigree (Clair et al., 1990) as a balanced translocation occurring between
1q42.1;11q14.3 (Blackwood et al., 2001). Replications of this study have found that DISC1
especially predisposes to SZ in females (Hennah et al., 2003). In addition to being implicated
in SZ (Ekelund et al., 2004; Hennah et al., 2003; Kockelkorn et al., 2004; Thomson et al.,
2005) this gene has recently also been associated with bipolar disorder (Hodgkinson et al.,
2004; Palo et al., 2007), major depression, autism and Asperger’s syndrome (Kilpinen et al.,
2007). Both autism and Asperger’s are early-age neurodevelopmental disorders. This
indicates there may be a neurodevelopmental basis for the DISC1 pathway and also provides
support for the neurodevelopmental hypothesis of SZ causation. DISC1 is part of several
pathways and acts as a multifunctional ‘hub’ (Camargo et al., 2006). A number of genes,
including NDE1, PDE4B and FEZ1, involved in the DISC1 pathway are also associated with
55
SZ (Tomppo et al., 2009), indicating that a disruption of the pathway and not just DISC1 is
responsible for the association. A recent study has shown a place for DISC1 in the AKT1
pathway, through direct inhibition of GSK3E (Mao et al., 2009).
Figure 1.9: Involvement of DISC1 in the AKT1 pathway. (Taken from Ross, 2009)
1.4.4. NFAT
Bone is constantly being remodelled through processes of formation and resorption to
maintain structural integrity (Sitara & Aliprantis, 2009). It is during regulation of these
pathways that the Ca2+ dependant Nuclear Factor of Activated T cells, Cytoplasmic (NFATC)
family plays their role (Sitara & Aliprantis, 2009). Consisting of five factors (C1-C4 Ca2+
dependant, C5) this family is activated by the protein phosphatase calcineurin (see Figure
1.10). Calcineurin has been linked to RA through its increased expression in the rheumatoid
synovium (Yoo et al., 2006). It has been suggested that the observed pro-inflammatory
activity is mediated through the NFATC family (Sitara & Aliprantis, 2009). NFATC1 has
primarily been implicated in the bone resorption pathway. It has been dubbed a “master
transcriptional regulator” due to its bottleneck effect in the multiple signalling pathway of
osteoclastogenesis, the formation of bone resorption cells (Sitara & Aliprantis, 2009). It has
also been shown to produce a severely osteoporotic phenotype in knockout mice (Winslow et
56
al., 2006). The diversity of this family is illustrated via the involvement of NFATC2 in bone
formation processes (Winslow et al., 2006). A recent mouse model study found that mice
deficient in NFATC2 had reduced bone formation rates resulting in low bone mass (Koga et
al., 2005). The importance of the calcineurin/NFAT pathway in RA disease pathogenesis is
highlighted in the clinical efficiency of calcineurin inhibitors (Kitahara & Kawai, 2007). This
is especially true for Cyclosporine A which has been shown to be effective in otherwise
refractory RA (Wells et al., 2001). Additionally, both NFATC1 and NFATC2 have been
shown to trans-activate ADAMTS subtypes (Thirunavukkarasu et al., 2006; Yaykasli et al.,
2009), a gene which has previously been associated in SZ (see Table 1.10).
Figure 1.10: The activation of Ca2+ dependant NFATC through
calcineurin phosphorylation (Taken from West et al., 2002).
57
1.5. METAͲANALYSISOFPUBLISHEDLITERATURE
One of the major issues with data analysis is obtaining a sample set large enough to provide
adequate power. Meta-analysis addresses this issue by providing a statistical basis for
combining sets of data from different studies with similar hypotheses (Egger & Smith, 1997).
Using this methodology quantitative statistics for the pooled results can be obtained. In this
thesis, meta-analysis is utilised to combine the results from studies in the literature review and
samples sets from the genotyping.
1.5.1. HISTORY OF META-ANALYSIS
Karl Pearson performed the first meta-analysis over one hundred years ago (Pearson, 1904).
Pearson realised that by combining the results from a group of studies he could improve the
accuracy of the data analysis. The first large scale study combining data sets from
independent researchers was published in 1940 (Pratt et al., 1940). This group was
investigating extra-sensory perception and combined 145 reports into a meta-analysis. They
were also the first to address the influence unpublished data has on the results of metaanalysis (see section 1.5.3). More sophisticated meta-analysis techniques began to appear in
the 1970s when particularly influential studies came to light. One of these investigated the
effectiveness of aspirin on reducing recurrent heart attack events (Elwood et al., 1974). The
results of this randomised trial were not convincing of a beneficial effect. However the results
of a meta-analysis conducted on this and other randomised trials provided statistical
significance for an advantageous effect of aspirin (Elwood, 2006; Peto, 1980). Also in the
1970s, the term meta-analysis was coined by Gene Glass to describe “the statistical analysis
of a large collection of analysis results from individual studies for the purpose of integrating
the findings” (Glass, 1976).
Since this time, researchers from a broad range of disciplines have undertaken meta-analyses,
with the number of published meta-analysis studies increasing steadily in the last 10 years
(see Table 1.8). Today there are many statistical packages that will meta-analyse samples sets,
including Comprehensive Meta Analysis (CMA) and Stata.
58
1.5.2. CONDUCTING A META-ANALYSIS
Meta-regression is utilised to identify a common measure over all the sample sets. This is
usually effect size measured as an odds ratio (OR) and 95% confidence intervals (CIs). The
result for each group is controlled for differences in study characteristics and combined into
an average for the combined sample set. This average effect size is a more powerful estimate
than the result from any of the single studies.
Identification of appropriate studies in the literature is the first step in conducting a
publication meta-analysis. The quality and specificity of the study can affect the results of a
meta-analysis. Therefore care must be taken to exclude studies which do not meet a
predefined set of criteria (Egger et al., 1997). Study characteristics which may need to be
taken into account include study design, population ethnicity, sample size, diagnostic criteria
for experimental and control groups and the outcome of interest (L'Abbè et al., 1987).
Another issue which needs to be addressed is whether to include unpublished data to avoid
publication bias (see section 1.5.3).
The study characteristics that are controlled for vary depending on the approach used.
Therefore, a model appropriate to the study should be selected for statistical analysis. The
‘inverse variance method’ is one approach common in medical research. This model assigns a
difference weighted mean to the effect size of each study (Egger et al., 1997). The weighting
is based on study size and random variation. Smaller studies and studies with greater random
variation are assigned a smaller weighting than larger studies. There are two main models for
managing this variance, fixed effects and random effects. The fixed effect model follows the
assumption that all variation observed is due to random variation (Yusuf et al., 1985). If this
model was correct, it would be expected that infinitely large studies would provide identical
results (Egger & Smith, 1997). Under this model, generalizations about the population cannot
be made. The random effects model considers underlying effect differences for each study as
an additional source of variation (Berlin et al., 2006). Although this model can produce wider
CIs than the fixed effect model a significant difference between the two models is only seen if
there is heterogeneity* between the sample sets (DerSimonian & Laird, 1986). If the results of
*
Statistical heterogeneity is the result of a higher than chance level of variation between studies (Higgins &
Thompson, 2002)
Arinami T, Ohtsuki T, Ishiguro H, Ujike H, Tanaka Y, Morita Y, Mineta M, Takeichi M, Yamada S, et al. (2005). Genomewide high-density SNP linkage analysis of 236 Japanese families supports the existence of schizophrenia susceptibility loci on chromosomes 1p, 14q, and 20p. The American Journal of Human Genetics 77(6): 937-944.
59
the individual studies vary greatly under a fixed effect meta-analysis then it is appropriate to
redo the analysis using the random effects model (Egger et al., 1997).
The statistical results of meta-analysis are often accompanied by a graphical representation in
the form of a forest plot (see Figure 1.11). These graphs present the OR and 95% CIs of each
individual study together with the results from the meta-analysis. If the 95% CI crosses an OR
of 1 there is no significant (P>0.05) difference between the control and experimental groups
for that study (Egger et al., 1997). When the vertical line of the combined OR (represented as
a diamond) cross the horizontal lines of all the studies the sample sets can be considered
homogenous (Egger et al., 1997). In most meta-analysis statistical packages the heterogeneity
is also expressed as a p value, with values over 0.05 indicating homogeneity.
Figure 1.11: A typical graphical representation of meta-analysis results. Darkened squares represent the
individual studies and their weighting. The centre of the square is the odds ratio and the line represents the 95%
confidence intervals. Odds ratios are marked along the bottom with a vertical line at 1. The diamond represents
the combined study effect size. (Taken from Rohatiner et al., 2005)
1.5.3. ADVANTAGES OF META-ANALYSIS
There are numerous advantages of conducting a meta-analysis over analysing sample sets
separately. The most significant, increasing statistical power of the analysis, has already been
60
alluded to. Single studies may be affected by false positive (produces a positive effect when
there isn’t one) or false negative results (no significant effect when there is one) (Egger &
Smith, 1997). The sample size needed to reach a nominal level of false results is vast. For
example, to detect an effect size of 10% (OR = 1.10) with a nominal level of false errors (i.e.
less than 10%), over 10000 individuals would be needed in each of the case and control
sample sets (Collins et al., 1992). Meta-analyses are a more efficient and cost effective way of
achieving sample sets of this size (Egger & Smith, 1997). They also have increased power
and thus an improved ability to detect smaller effect sizes.
Meta-analysis also provides a way to generate a systematic review of the literature. The more
traditional literature reviews are considered highly subjective with interpretation of individual
studies at the discretion of the author (Dickersin & Berlin, 1992). These narrative reviews are
therefore more prone to bias and error, with authors frequently reaching opposing conclusions
(Mulrow & San Antonio, 2009; Teagarden, 1989). Meta-analyses are more objective and
offer a weighting system for alignment sample sizes, a variable likely to affect the outcome of
the analysis.
1.5.4. DISADVANTAGES OF META-ANALYSIS
Meta-analysis also has some weaknesses. Firstly, it is only a statistical examination and
therefore the quality reflects the scientific studies it is analysing (Smith & Egger, 1998). If
there are underlying issues with the studies, the meta-analysis may not be truly representative
of the data. It has been proposed that only studies with quality methodology should be
included in meta-analysis to prevent bias (Slavin, 1984).
Unpublished data are often excluded from meta-analysis. This exclusion may skew the results
towards significance. Because studies which show insignificant results are less likely to be
published (Easterbrook et al., 1991), there may be any number of appropriate studies which
met the inclusion criteria for the meta-analysis but are not obtainable. The Cochrane
Collaboration* attempts to overcome this issue by collecting both published and unpublished
data from a number of medical disciplines and producing meta-analyses and systematic
*
The Cochrane Collaboration is a network of clinicians, epidemiologists and other health professionals first
established in Oxford in 1992 (Bero & Rennie, 1995).
.
Arnett WD, Rosner JL (1987). Neutrino mass limits from SN1987A. Phys Rev Lett 58(18): 1906-1909.
61
reviews (Bero & Rennie, 1995). Authors may also be unwilling to provide data to others,
especially when the results are less favourable (Smith & Egger, 1998). This means that even
the obtained unpublished literature may be unrepresentative of all the unpublished studies
(Smith & Egger, 1998).
English language bias may also be an issue in the collection of literature for meta-analysis.
Many researchers who publish meta-analyses in English language journals have limited
inclusion criteria to articles originally published in English (Grégoire et al., 1995). It is
possible that research with positive results is more often published in international English
journals and that negative findings are reported in local language journals (Egger & Smith,
1998). This issue was examined by Egger and colleagues by comparing research published in
German language articles with English language articles (1998). They found that the
percentage of publications with significant (P<0.05) results was higher in English (63%) than
German (35%). This indicates that there is potential for bias in meta-analyses which only
include English language literature (Egger & Smith, 1998).
Another disadvantage of meta-analysis is the event of Simpson’s Paradox. This phenomenon
is described as a combination of studies producing an effect opposite to the effect of the
individual studies (Simpson, 1951). This bias was seen in a study on the effectiveness of
nicotine gum (Silagy et al., 2004). Two trials were included and produced similar effect sizes
(6%). However, when these studies were combined in meta-analysis the pooled effect size
was (3%). Simpson’s Paradox is based on unbalanced experimental design (Glass, 1977).
This effect could not occur if the two sample groups are of the same size (Glass, 1977).
Therefore ensuring that the individual samples sets are properly weighted (as described in
1.5.2) can help eliminate this issue.
1.5.5. FUTURE DIRECTIONS OF META-ANALYSIS
The Cochrane Collaboration provides the opportunity to obtain meta-analysis reports that are
more objective and free from bias than can be produced by individual authors (Bero &
Rennie, 1995). The inclusion of unpublished and non-English language material is part of
what makes this project so attractive.
62
It is generally accepted that meta-analysis studies are superior to the narrative review
approach (Smith & Egger, 1998). However, the publication of poorly constructed metaanalysis studies is threatening this reputation. The most robust meta-analyses include well
aligned studies which meet stringent inclusion criteria (Egger & Smith, 1997).
63
1.6. ANIMALMODELSFORRHEUMATOIDARTHRITIS
The main reason for using animal models for this disease are the many limitations associated
with studying RA in humans. Firstly, there is the vast number of individuals needed to reach
the power level necessary for detecting all the variation within a genetic effect (Ahlqvist et
al., 2009). This issue is starting to be addressed by the publication of studies with larger
sample sets, i.e. GWAS. In studies such as GAIN and WTCCC there is collaboration between
many different groups (see section 1.4.1) allowing the combination of recruited patients
around the world into one sample set. However, at present, it is still only the strongest
associations that are detectable. Secondly, the provision for follow up study in humans is
restricted (Ahlqvist et al., 2009). Animal models offer the opportunity to investigate
phenotypic and biological implications in more depth (Bendele, 2001). However, the value of
animal research in RA rests entirely on the availability of suitable disease models (Ahlqvist et
al., 2009). In the case of RA these models need to reflect the environmental and polygenic
interactions seen in humans. Two of the most popular animals for RA research are mice and
rats (Ahlqvist et al., 2009; Bendele, 2001; Kannan et al., 2005). Their usefulness stems from a
quick reproduction cycle, ability to thrive in a laboratory environment and relatively
inexpensive maintenance (Kannan et al., 2005).
There are limitations with animals studies as well, the most controversial of which is the
differences between the animal and human models. However it is argued that the majority of
genes will act in a similar way between the species and that the pathway may be of greater
importance than the individual genes (Ahlqvist et al., 2009). It is also essential to understand
the mechanisms of disease in animal models as it this group which is first used to test
potential therapeutic agents (Bendele, 2001). One way to address this is using the mouse
knockout approach, where individual genes can be inhibited illustrating the effect on the
disease (see section 1.6.1). Another issue with animal models is the difference in exposure to
environmental factors. Generally in animal genetics studies the environment is controlled to
allow a minimal impact of environmental factors on disease variability (Ahlqvist et al., 2009).
Some factors, such as those that are human specific, may never be included in the animal
model (Ahlqvist et al., 2009). In spite of these limitations animal models offer a
complementary insight into human disease genetics to that provided in human studies.
64
1.6.1. KNOCKOUT MOUSE APPROACH
Knockout mouse models utilise methods which inhibit gene function in a developing mouse
embryo. This occurs in one of two ways, either with the mutation of the gene resulting in nonfunction or removal of the gene altogether. The first knockout mice were created in the late
1980s (Capecchi, 1989) with rat models not developed until 2003 (Zan et al., 2003).
Knockouts have proven extremely useful in illustrating the effects a gene and its downstream
pathways have on development of disease. In the study of hereditary hemochromatosis,
knockout models facilitated the discovery that major histocompatibility complex class one
(MHC-C1) may have a role in the disease (Zhou et al., 1998). Knockout mice also offer a
model for testing potential therapeutic agents in diseases that are the result of a gene
interruption. In retinoblastoma, for example, a knockout model has only recently been
developed, hindering the progression of clinical studies (Zhang et al., 2006b).
1.6.2. MOUSE MODELS FOR RHEUMATOID ARTHRITIS
There are many different mouse models of inflammatory arthritis (IA) available to researchers
of RA. Mouse IA has many similarities to human RA and may vary depending on the type of
model used. There are two main categories of mouse models: induced and transgenic. In
induced models the animal is given a substance to bring on the disease and this cannot be
passed on to offspring (Holmdahl et al., 2002). The most popular induced model is Collageninduced arthritis (CIA). This model is MHC complex-dependent and has a chronic and acute
form (Holmdahl et al., 2002). However, the use of transgenics in the CIA model is at present
limited. This is due to the resistance of the most common transgenic background, C57BL/6, to
induction of CIA (Asquith et al., 2009). Improvement of the CIA protocol is one area where
this issue is currently being addressed (Asquith et al., 2009). The use of purely transgenic
mouse models such as the K/BxNmodel is another way to avoid this problem (see section
1.1.1).
65
1.6.3. KRN MODEL OF INFLAMMATORY ARTHRITIS
K/BxN mice spontaneously develop RA as a result of the cross between autoimmune prone
non-obese diabetic (NOD) mice, which have a specific MHC haplotype, and KRN mice,
transgenic for a T cell receptor (TCR) specifically recognising bovine pancreas ribonuclease
(RNase, see fig. 4) (Kouskoff et al., 1996). This was a serendipitous discovery by a research
group in 1996 (Kouskoff et al., 1996). Further study found that the development of RA was
attributable to T cells of the mice recognising a self antigen of Glucose-6-phosphate
isomerase (GPI) peptide (Korganow et al., 1999; Kouskoff et al., 1996; Matsumoto et al.,
1999). It is believed that the combination of the KRN T and B cells with the specific MHC
haplotype of the NOD mouse is responsible for this phenomenon (Kouskoff et al., 1996).
1.6.3.1. Spontaneous arthritis model
In the spontaneous KRN model, it is necessary to breed the candidate gene knockout onto a
NOD background. Therefore when the NOD knockout mice are crossed with KRN mice the
offspring (K/BxN) will develop spontaneous IA.
1.6.3.2. Serum induced arthritis model
It has been found that transferring the serum (or even just purified IgG) of affected K/BxN
mice into naïve animals is enough to induce inflammatory arthritis (Maccioni et al., 2002;
Monach et al.). With this model, the knockouts do not need to be bred to a NOD background
and crossed to KRN transgenics. Instead knockouts can be directly injected with serum of the
affected mice and observed for signs of RA. This method provides researchers with a way to
study the genetics of the RA with lowered impact on ethics and resources.
1.6.3.3. Similarities and Differences with human Rheumatoid Arthritis
The K/BxN model of RA has a number of similarities with human RA. For example, both
present as a chronic progressive disorder with symmetrical polyarthritis characteristics and
66
result in severe joint destruction (Ditzel, 2004). The major differences between the human and
mouse models are the involvement of the MHC haplotype and rheumatoid factor. The IA
transgenic mice are completely dependent on the presence of MHC class II molecule A(g7)
(Ditzel, 2004). Although humans have a range of MHC class II, HLA-DR alleles which
confer susceptibility to RA (i.e. HLA-DBR1 and HLA-DBR4, see section 1.1.1.4) these
genotypes are only indicative of disease in 70% of RA patients. Rheumatoid factor (see
section 1.1.1.3) in humans is generally (but not always) involved in the disease. In the
transgenic mouse model, K/BxN rheumatoid factor is absent (Ditzel, 2004).
One important issue surrounding this mouse model is that there is no illustration of sex bias.
In humans, females are 4 times more likely to develop RA than in men but the K/BxN model
the ratios are equal (Ditzel, 2004). However, until as recently as 2007 when the CIA
DR4.AE° model was developed (Taneja et al.), none of the RA animal models incorporated
this sex bias.
The K/BxN model has many characteristics making it attractive for use in this thesis. It is well
aligned with the human disease, as well as cost effective and allows for the manipulation of
genes (Ditzel, 2004). The serum transfer model has the added benefit of being relatively quick
to perform (i.e. less breeding).
Figure 1.12: Initiation of arthritis in K/BxN mice as the result of dual specificity of a transgenic T-cell receptor
(TCR). The KRN TCR recognises a peptide from bovine pancreatic RNase presented by the mouse major
histocompatibility complex (MHC) class II molecule I-Ak. This TCR also recognises the self-antigen GPI
peptide presented by the MHC class II molecule I-Ag7. T cells expressing the transgenic TCR assist anti-GPI B
cells, which differentiate into the plasma cells that produce arthritogenic anti-GPI IgG. (Taken from Ditzel,
2004)
67
1.7. PROJECTAIMS
x
To establish if genes from the AKT1 pathway are associated with the genetic link between
Rheumatoid Arthritis and Schizophrenia by analysis of genotype data from four different
disease sample sets.
x
To utilise published data to conduct a meta-analysis on genes within the AKT1 pathway
to determine whether these genes are responsible for the genetic link between
Rheumatoid arthritis and Schizophrenia.
x
To identify the microsatellite markers which differ between the B6 and 129 mice strains
to allow formation of a GRIK2 knockout on a B6 background.
x
To determine the effect GRIK2 knockouts have on a mouse model of RA by establishing
a method for Inflammatory Arthritis induction in mice.
68
Chapter 2: Materials and Methods
2
.
CHAPTERTWO
MATERIALS AND METHODS 2.1.
2.2.
2.3.
2.4.
2.5.
2.6.
2.7.
2.8.
2.9.
Human Genotyping Sample Sets
GWAS Datasets
Selection of SNPs
Human Sample Genotyping
Meta-Analysis of Genotyping and Published Literature
Mouse Sample Preparation
Mouse Sample Genotyping
Mouse Microsatellite Assay
Materials and reagents
69
2.1.
HUMANGENOTYPINGSAMPLESETS
2.1.1. ETHICS APPROVAL
New Zealand sample set
x The Lower South Ethics Committee for control sample set (OTA/98/04/024)
x The Multi-Region Ethics Committee for case (rheumatoid arthritis) sample set
(OTA/99/02/007).
Australia case sample set
x The Research and Ethics Committee of the Repatriation General Hospital
(Protocol No. 16/05).
UK Oxford sample set
x Oxford Research Ethics Committee (OxRec No C02.032)
UK London sample set
x Case sample set: The Lewisham Hospital and Guy’s and St. Thomas’
Hospitals local research ethics committee.
x Control sample set: The European Collection of Cell Culture (www.ecacc.org).
2.1.2. PARTICIPANT RECRUITING
Three case-control sample sets, New Zealand, Oxford and London, were used for association
analysis in addition to publically available data (section 2.2). RA patients were diagnosed by a
rheumatologist or a trained nurse and met the criteria for the disease as outlined in the 1987
ACR classification system (see section 1.1.1.3). Patients were said to have RA if they
satisfied at least four of the seven criteria outlined in reference to Table 1.1. Patients must be
afflicted by these four criteria for at least six weeks.
70
The selection criteria of control participants in all sample sets included: no history of
autoimmune disease and of European Caucasian ancestry (self-reported). Written informed
consent was required from all volunteers invited to join the study. Each volunteer donated
blood samples for the extraction of DNA. New Zealand control participants were recruited
primarily from the Auckland and Otago regions and the RA patients from Auckland, Bay of
Plenty, Wellington, Canterbury, Otago and Southland. The median age for controls of
both sexes was 51.1 years. A set of 96 Australian case participants was recruited by
Professor Malcolm Smith from the Repatriation General Hospital, Adelaide, Australia.
These samples were added to the New Zealand case-control sample set for all analysis. RA
patients in the Oxford sample set were recruited from the rheumatology out-patient clinic
at the Nuffield Orthopaedic Centre. Median age of onset for patients was 55 years for
females and 47.5 years for males. Controls were ascertained from the blood donor
register for the Oxford region based on the criteria mentioned above. The control
sample set for the London sample set was purchased from the European Collection of Cell
Cultures (section2.9.4). Collaborator Dr. Sophia Steer recruited RA patients from the
Guy's and St. Thomas's Hospital and Lewisham Hospital (London, UK).
Table 2.9: The number of case and control samples for each sample set used in this study.
Sample Set
Male
RA Cases
Female Total*
RA Controls
Male
Female Total*
34.8%
33.7%
65.2%
66.3%
763
96
40.9%
-
59.1%
-
568
-
Oxford
29%
71%
728
51.2%
48.8%
527
London
22.3%
77.7%
287
22.6%
68.4%
177
New Zealand
Australian
*Note that the numbers presented here may differ slightly to the ones in the
results due to the removal of failures or samples being unavailable.
2.1.3. DNA EXTRACTION
Genomic DNA was extracted from Australasian volunteer blood samples by staff in the
Merriman laboratory following this protocol: 10 mL of whole blood was transferred to 50 mL
Falcon tubes containing 30 mL of RBC lysis buffer. The tubes were centrifuged for 15
minutes at 2500 rpm. The supernatant was removed and 20 mL of RBC lysis buffer (refer to
71
section 2.9.1) added to the tube. The solution was mixed by gentle inversion of the tube and
centrifuged for a further for 15 minutes at 2500 rpm. The supernatant was removed and
discarded. DNA was then extracted from the pellet immediately by adding 3.5 mL of GuHCl
to the tube and vortexing vigorously. A solution containing 250 PL of 7.5 M ammonium
acetate, 50 PL of Proteinase K and 250 PL of sodium sarcosyl was added to the tube and
vortexed briefly. The samples were then incubated at 37 qC overnight. Following this, the
sample was transferred to a 15 mL tube containing 2 mL of cold chloroform. The tube was
vortexed and left at room temperature for one minute before centrifuging for three minutes at
2500 rpm. The upper aqueous layer was transferred to a 15 mL tube containing 10 mL of cold
absolute ethanol. This was mixed by gentle inversion to precipitate out the DNA, and
centrifuged at 3000 rpm for 15 minutes. The supernatant was removed and washed by adding
4 mL of 70% ethanol and centrifuged for five minutes at 3000 rpm. This step was repeated
twice. The supernatant was then tipped off and the pellet left to air-dry on the bench. Once
dry the pellet was re-suspended in 200 PL of TE buffer.
2.1.4. SAMPLE STORAGE
All samples were stored in 96 well 0.8ml deep well boxes. Stock DNA of 200ng/mL was
stored in deep well plates and this DNA was used to refill a 40ng/mL storage plate. The
storage plate was in turn used to replenish the 6ng/mL working stock plates. This process was
used to minimise cross-contamination of the stock DNA. All plates were stored in the -20ºC
freezer. The working stock plates were moved to the fridge for defrosting as needed. Each
sample set contained a series of plates for cases and controls. These were labelled with a
series of letters and numbers for easy identification (see Table 2.10).
Table 2.10: DNA plate nomenclature for RA cases and controls.
Sample set
New Zealand
Australia
Oxford
London
Plates
Cases
NZRA1-9
ARA1
OXRA1-8
UKRAA-E
72
Control
NZC1-6
OXC1-6
UKNC1-2
2.2. GWASDATASETS
2.2.1. PRIMARY SAMPLE SETS
The Wellcome Trust Case Control Consortium (WTCCC) and Genomic Association
Information Network (GAIN) were utilised as the initial datasets for investigation of SNPs in
this thesis. Selection of SNPs was dependant on the GWAS case control association results
(see section 2.4.7.1). All cases and controls analysed from these datasets were of EuropeanAmerican (EA) ancestry only (>90%).
2.2.1.1. WTCCC
The WTCCC (http://www.wtccc.org.uk/) is a collaborative study which includes over 50
research groups from across the UK (see section 1.4.1). The control group is representative of
the general UK population and made up of two sample sets: 1504 individuals recruited from
the 1958 British Birth Cohort and 1500 individuals recruited from the UK Blood Service
Control Group (WTCCC, 2007). The WTCCC RA group recruited 1999 participants from
previously established UK collections for the case sample set (WTCCC, 2007). Genotyping of
these sample sets was with the Affymetrix 500K platform (>500,000 SNPs).
2.2.1.2. GAIN SZ
GAIN is managed by the Foundation for National Institutes of Health (FNIH,
http://www.fnih.org) in conjunction with a number of other partners in the public and private
sector (The GAIN Collaborative Research Group, 2007). FNIH collected DNA samples
worldwide for GAIN GWAS genotyping. These samples went through a review and selection
process to ensure the samples conformed to disease criteria and met quality standards. (The
GAIN Collaborative Research Group, 2007). GAIN controls were recruited by the market
research company Knowledge Networks (San Jose, CA). Participants are recruited throughout
USA and come from a range of demographics representative of the general population.
Controls were screened for disease related traits (history of mental illness, substance abuse)
73
and personality traits (sex, ethnicity) Samples in the SZ control group incorporate the controls
from the BD subset.
SZ case samples were originally collected by Evanston North-Western Healthcare, from
throughout Australia and USA (Suarez et al., 2006). Case subjects were required to give
signed informed consent, be over 18 years of age and meet the DSM-IV diagnosis of SZ (see
section 1.2.1) or schizoaffective disorder. Following the review process there were 1217 case
subjects for genotyping. The control group for GAIN SZ consisted of 1442 samples. An
Affymetrix Affy 6.0 platform (>900,000 SNPs) was utilised for genotyping of these samples.
2.2.2. ADDITIONAL SAMPLES SETS
2.2.2.1. Non-GAIN SZ
The Molecular Genetics of Schizophrenia (MGS) research group manages a large casecontrol sample set. There are 2686 EA cases and 2656 EA controls, half of which were
genotyped as part of the GAIN GWAS. The other half are referred to as the non-GAIN
sample. These samples were genotyped with the Affymetrix 6.0 platform at the Broad
Institute, Cambridge, USA. Case subjects were recruited from throughout USA and Australia.
All participants had to give signed informed consent, be at least 18 years old and able to
communicate in English. Cases were referred from a clinician or family member and met the
criteria for SZ or schizoaffective disorder as outlined in the DSM-IV. Individuals with SZ
resulting from substance abuse or neurological illness were excluded.
The Knowledge Networks (see section 2.2.1.2) market research company was responsible for
the recruitment of control participants from throughout the USA. Controls were excluded if
they had ever been diagnosed with or treated for SZ, schizoaffective disorder, BD, manicdepression, auditory hallucinations or persecutory delusions.
74
2.2.2.2. GAIN Bipolar Disease
All GAIN sample sets were recruited as outlined in section 2.2.1.2. The case subjects for
bipolar disease (BD) were originally recruited by researchers at the University of California,
San Diego (Dick et al., 2003). Participants located from throughout USA underwent a
diagnostic interview following the conventions of the Diagnostic Instrument for Genetic
Studies, (Nurnberger Jr et al., 1994). These sessions were carried out by trained interviewers.
The interview results and the participant’s medical records were analysed by a senior
clinician. Individuals were included in the study based on DSM-IV criteria for BD. 1081
controls and 691 cases were genotyped over the Affymetrix Affy 6.0 platform.
2.2.3. DATASETS FOR THE DETECTION OF SEX BIAS
To test for sex bias, all the datasets were separated into male cases and females cases. The
genotyping results for these two subsets became controls and cases respectively. This
information was then analysed in the normal way. For GWAS studies, this was through
BC|SNPmax (see section 2.3.2) and for single SNP genotyping this was through SHEsis (see
section 2.4.7.1). This analysis provided information on whether there was a significant (p <
0.05) difference between males and females for that sample set. Next, the datasets were
analysed for each sex separately. The males and females were split into two subsets each
containing cases and controls. These subsets were analysed as normal and the results for p
value and odds ratios (ORs) were recorded. This analysis provided sex specific information
for association with the disease. To provide a complete analysis, all SNPs investigated in this
thesis were analysed for sex bias.
75
2.3. SELECTIONOFSNPS
2.3.1. LITERATURE SEARCH
A search of the published literature was conducted to identify genes within the AKT1 pathway
that had a role in pathways which could potentially be disrupted in RA and SZ. Information
on the genes involved in the AKT1 pathway was obtained from the signalling pathway
overview on the Invitrogen website (see section 2.9.4). A search on the online literature
database, PubMed was (see section 2.9.4) conducted to identify which of these genes were
implicated in immune or neurological systems. These genes were investigated for SNPs
significantly associated with RA as outlined in section 2.3.2.
2.3.2. BC|SNPMAX
SNPmax (provided by Biocomputing Platforms Ltd at http://www.bcplatforms.com/) is a data
management and analysis platform which allowed easy access to genotyping data from SNPs
identified in the WTCCC and GAIN datasets. These datasets first needed to be downloaded
from online databases and uploaded to SNPmax. GAIN SZ, GAIN BD and non-GAIN SZ
were obtained from NCBI Genotypes and Phenotypes (http://www.ncbi.nlm.nih.gov/dbgap).
WTCCC
data
were
obtained
from
the
European
Genome-Phenome
Archive
(http://www.ebi.ac.uk/ega). Permission for access to these datasets were obtained by Tony
Merriman. All datasets were provided as raw data files containing the genotyping calls for the
SNP chips. GAIN and non-GAIN had additional files constraining the phenotype information
for the individuals genotyped in the study. Basic epidemiological information for the WTCCC
was already on file in the Merriman lab. This information was downloaded from the website
and uploaded to SNPmax by Merriman Lab Research Assistant, Ruth Topless.
A number of open source softwares were linked into BC|SNPMax for analysis of data. The
whole
genome
association
toolset
PLINK
version
1
(http://pngu.mgh.harvard.edu/purcell/plink/) was integrated into BC|SNPmax for analysis of
the GWAS and genotyping data. In addition to cases control analysis, PLINK was also
utilised
for
examining
haplotype
associations.
76
IMPUTE
version
3
(https://mathgen.stats.ox.ac.uk/impute/impute.html) was utilised for estimating (imputing)
unobserved genotypes in SNP association studies (see section 2.3.2.1).
The genotyping information for SNPs in AKT1 pathway genes was obtained from these data
sets to identify candidates for genotyping in this study. The protocol for this process of data
collection is as follows. A subset of SNPs was created for each gene of interest using the
following method: in the markers tab the folder and dataset representing the latest NCBI
marker information (currently - NCBI dbSNP Build 129 (Jul 2008)) was selected. On the left
hand menu, subset was chosen and the variables (chromosome number, chromosome position,
subset name) for the new subset were entered. The information on these variables was
obtained from the web database Ensembl (http://www.ensembl.org/index.html). The genes
boundaries were rounded to the nearest 10,000bp to include flanking sequences. Clicking the
CREATE button finalised the subset and it was then available for analysis. This subset was
subsequently run against either the GAIN (as in Table 2.13 below) or WTCCC datasets.
Figure 2.13: Example of SNPmax setup for PLINK case-control analysis in AKT1 over the GAIN SZ sample
set.
77
To analyse the subset the following steps were taken: The genotypes tab at the top of the page
was selected. This opened up a new window with the option to choose a relevant folder and
genotype dataset. Under the analysis heading on the left hand menu GWAS was selected. In
the new window PLINK case-control analysis was selected from under the ‘association and
LD’ header. All the variables for the analysis run were entered as shown in Figure 2.13. Here,
the newly created subset and the SNPs of interest could be entered under the markers section
for analysis. Pressing OK started the run and the progress could be checked via the queue
under tools in the left hand menu. When the run had completed the results could be viewed in
the plink.assoc file under the relevant run title in the results archive (Table 2.11 below).
MARKER
CHR
SNP
BP
A1 F_A
F_U
A2 CHISQ
P
ORX
L95
U95
TRAIT
JOBID
SNP_A-2303587 14
SNP_A-2303587
104161600
C
0.2664
0.2467
G
2.58
0.1082
1.109
0.9775
1.258
job39986 39986
SNP_A-8471789 14
SNP_A-8471789
104181462
G
0.1433
0.1504
A
0.5072
0.4764
0.945
0.8087
1.104
job39986 39986
SNP_A-8699009 14
SNP_A-8699009
104188855
A
0.2511
0.2593
G
0.4506
0.5021
0.9576
0.8436
1.087
job39986 39986
SNP_A-8610251 14
SNP_A-8610251
104207826
A
0.03547
0.03055
G
0.9662
0.3256
1.167
0.8573
1.589
job39986 39986
SNP_A-4279987 14
SNP_A-4279987
104210939
G
0.4317
0.432
A
0.000492 0.9823
0.9987
0.8936
1.116
job39986 39986
SNP_A-8321182 14
SNP_A-8321182
104268612
C
0.4304
0.4447
G
1.054
0.3045
0.9434
0.8442
1.054
job39986 39986
SNP_A-8629494 14
SNP_A-8629494
104278179
C
0.4406
0.456
T
1.207
0.272
0.9397
0.8411
1.05
job39986 39986
SNP_A-2179910 14
SNP_A-2179910
104281252
A
0.4347
0.4491
G
1.064
0.3023
0.9433
0.8442
1.054
job39986 39986
SNP_A-1864783 14
SNP_A-1864783
104284388
G
0.4377
0.4538
A
1.328
0.2491
0.9369
0.8386
1.047
job39986 39986
SNP_A-2232252 14
SNP_A-2232252
104308725
C
0.3283
0.3392
G
0.6655
0.4146
0.952
0.8458
1.071
job39986 39986
SNP_A-8477816 14
SNP_A-8477816
104330751
T
0.1371
0.1417
C
0.2281
0.633
0.962
0.8204
1.128
job39986 39986
SNP_A-2299278 14
SNP_A-2299278
104330779
A
0.2939
0.2925
C
0.0118
0.9135
1.007
0.8917
1.137
job39986 39986
Table 2.11: Example of data output for PLINK case-control analysis in SNPmax (this table shows data for
AKT1 in GAIN)
Only those SNPs with a p value <0.05 in the WTCCC dataset were recorded. This
information was presented against the genotyping data for the same SNPs in the GAIN
dataset. From this list SNPs that had an OR which conveyed protection in one disorder and
susceptibility in the other continued to the next stage of selection.
2.3.2.1. Imputing SNPs in WTCCC and GAIN
Imputations were run through SNPmax using the following protocol. A new folder called
gene name + IMPUTE was set up as a location to save the data to for later analysis. In the
genotypes tab the folder and dataset representing the relevant sample set (WTCCC or GAIN)
was selected. On the left hand menu, GWAS was chosen and then IMPUTE from under the
78
imputation heading. All the variables for the run were entered as shown in Figure 2.14 with
the exception of chromosome number and imputation boundaries which were changeable,
dependant on the gene of interest. Following run completion the imputed genotypes data
could be found in the results archive. These data were uploaded to the new IMPUTE folder
using the converter IMPUTED SNPs (uses most probable genotype). The genotypes scored
were then extracted as outlined in section 2.3.2. This information could be combined (metaanalysis, section 2.5) or compared (haplotype analysis, section 2.4.7.3) with directly
genotyped SNPs.
Figure 2.14: Example of SNPmax setup for IMPUTE of SNPs in AKT1.
79
2.3.3. LINKAGE DISEQUILIBRIUM ANALYSIS
The SNPs of interest in this thesis were analysed for their intermarker linkage disequilibrium
(LD) values with surrounding SNPs. This analysis was carried out using information from the
latest genome build in HapMap (Phase 1 & 2 - full dataset). Data were imported into the
haploview software (see section 2.9.4 for web address) and visualized using a haploview plot.
This software illustrates areas of high LD between SNPs, called haplotype blocks (see Figure
2.15). Two SNPs are in complete LD when the r2 value is 1 and not in LD at all when r2 value
is 0 (as shown in Figure 2.15).
Black squares: r2 =1
Grey squares: 0 >r2< 1
% LD shown numerically
White squares: r2 = 0
Figure 2.15:Haploview LD plot (r2) generated from HapMap genotyping of one block of DISC1 SNPs.
A haplotype is a combination of alleles along the chromosome. A haplotype block occurs
when there is a cluster of SNPs in high LD with each other (see Figure 2.15). Haploview
provides a visual representation of haplotype blocks and the levels of LD within a region. If a
SNP uniquely marks a haplotype then it is a tagging SNP. Due to their ability to provide a
representative overview of the variation in the genome, tagging SNPs are often used in
GWAS studies.
80
If two SNPs of interest were in complete LD with each other, then only one was chosen for
analysis. If suitable primers could not be designed for a significantly associated SNP, or if the
SNP failed to provide a robust genotyping assay (see section 2.4.1) the haploview plot was
scanned for an alternative SNP in complete or very high LD. This SNP could then be tested
for suitable primers and a robust assay.
Figure 2.16: Haploview LD plot (r2) generated from HapMap genotyping
illustrating a region of complete LD between SNPs. SNPs with red boxes
are in complete LD with each other. The SNP in the green box is in high
LD with the other three. The SNP in blue has no LD with the other four
coloured SNPs.
81
2.4. HUMANSAMPLEGENOTYPING
Single nucleotide polymorphisms (SNPs) were used as markers to identify whether a segment
is associated with disease in humans. SNPs are single base pair variations between individuals
of the same species. SNPs are one type of marker that can be used to detect variation between
individuals where a restriction enzyme cuts in the presence of one allele but not the other.
2.4.1. RFLP PRIMER DESIGN FOR HUMAN GENOTYPING
Once a particular SNP had been identified for analysis (see section 0 for criteria) primers
were designed for genotyping: The rs number of the SNP was searched in Ensembl
(www.ensembl.com) and the flanking sequence, SNP position and frequency in Caucasian
populations
was
documented.
The
NEB
cutter
website
(http://tools.neb.com/NEBcutter2/index.php) identified an enzyme with a cutsite which can be
used to distinguish between the variations of the SNP. If there were no natural cutsites for the
SNP a single base pair (bp) change could be made in one primer to allow a forced cutsite (see
Figure 2.17). If a forced cutsite enzyme was needed one primer must finish (3’ end) with the
base which was being changed and the primer length should be approximately 30bps. The
longer length of the forced primer sequence ensured correct binding would take place.
An oligo calculator website (http://www.basic.northwestern.edu/biotools/oligocalc.html) was
used to identify GC content (less than 50% required) and melting temperature for each set of
primers. A string of approximately 20 base pairs (bps, 30 for a forced primer) 30-100bps
upstream of the SNP was pasted into the website and repeated with the downstream primer
until a similar temperature and GC content was reached for both primers. The number of bps
from the start of the upstream primer to the cutsite needs to be significantly (>20bp) different
from the number of bps from the end of the downstream primer to the cutsite. This is to
ensure that the bands are significantly different in size to allow identification on gel
electrophoresis. Primers were then tested with a nucleotide-nucleotide BLAST search to
ensure their binding location was unique to the chromosomal region of interest
(http://www.ncbi.nlm.nih.gov/). Primers were ordered from Sigma-Aldrich, Australia. Twice
the value of the primer concentration labelled on the tube (Pl) was added as volume of TE
82
buffer to reach a stock concentration of 500ngPL-1. These tubes were stored in the -20ºC
freezers until needed. All working stocks of the primers were at 50ngPL-1. Aliquots of 50Pl
primers were added to new Eppendorf tubes containing 450Pl of TE buffer and stored in the
fridge.
1. Use a natural cut site if possible
Forward natural cut-site for BstUI (CG^CG)
G Allele – allows enzyme to cleave DNA
T Allele – prevents enzyme from cleaving
TGACCGACGACGCGTTTAA
DNA
TGACCGACGACTCGTTTAA
2. Use a forced cut-site if no natural one is possible
Forward forced cut-site for BstUI (CG^CG)
G allele
T Allele
TGACCGACGATGCGTTTAA
TGACCGACGATTCGTTTAA
Change T to C to allow enzyme to cleave DNA at this position if correct SNP allele is present
TGACCGACGACGCGTTTAA
TGACCGACGACTCGTTTAA
2.b. Design a forward primer which includes the bp being modified on the end
5’
3’
5’
TGACCGACGACGCGTTTAA
3’
TGACCGACGACTCGTTTAA
Figure 2.17: The process of primer design for forced and natural cut sites. A string of base pairs is shown in
black surrounding a SNP of interest in red. Where it is possible for the enzyme to bind the sequence the cut sites
are double underlined. The forward primers for these sequences are shown in bold with a line above.
83
2.4.2. POLYMERASE CHAIN REACTION
PCR is a technique whereby enzymatic replication via heat cycling is used to exponentially
amplify a fragment of DNA of interest.
2.4.2.1. Optimisation of marker assays
The best magnesium and temperature conditions for each set of primers needed to be assessed
before genotyping of samples could occur. The following procedure was used to find these
conditions: 75Pl of test DNA (6Pl/mL), 11.25Pl Forward primer, 11.25Pl Reverse primer,
22.5Pl Potassium Chloride, 11.25 of dNTPs and 7.5Pl of Taq enzyme was added to each of
five 1.5ml Eppendorf tubes. These tubes then had varied volumes of double distilled H2O
(ddH2O) and magnesium added to give 1mM (81.75Pl ddH2O, 4.5Pl Mg), 2mM (77.25Pl
ddH2O, 9Pl Mg), 3mM (72.75Pl ddH2O, 13.5Pl Mg), 4mM (68.25Pl ddH2O, 18Pl Mg) and
5mM (63.75Pl ddH2O, 22.5Pl Mg) final concentrations of magnesium. The tubes were
vortexed and 15Pl of each solution was added to all the wells in a specified row of a 96 well
plate. 50Pl of mineral oil was used to cover the solution in each well and prevent evaporation.
The plate was added to a gradient mastercycler Eppendorf Polymerase Chain Reaction
machine (PCR machine) with a starting temperature of 50qC (gradient to 65qC). The product
was analysed by gel electrophoresis (see section 2.4.4) and visualized under UV light to
assess which temperature and Mg concentration provided the best conditions (a single bright
band) for the primers.
84
2mM Mg2+
3mM Mg2+
5mM Mg2+
4mM Mg2+
200
150
100
75
50
50°C
65°C
Figure 2.18: Primer optimization reaction for rs821585 (from DISC1). Four different concentrations of Mg2+
were tested over a temperature gradient of 50°C to 65°C (from wells 1 to 12). The optimal conditions for this
assay were set at a Mg2+ concentration of 3mM and a temperature of 65°C. These conditions eliminated nonspecific bands and had a high fluorescence. Low molecular weight DNA ladder (New England BioLabs Inc.),
3.5% agarose gel.
2.4.2.2. PCR amplification
Once an appropriate temperature and Mg concentration was found for the primers, genotyping
of the SNP over all the sample plates was undertaken. The mastermix (as in Table 2.12) was
added to 5 !L of DNA for each sample. Each sample had 50 !L of mineral oil added to
prevent evaporation during PCR. Conditions for this protocol are outlined in Table 2.13.
Table 2.12: PCR reaction mix for a single 10 !L reaction
Reagent
Forward primer (50ng/!L)
Reverse primer (50ng/!L)
10x NH4 (KCl) buffer
4mM dNTPs
50 mM Mg2+
ddH20
Bioline Taq polymerase
Total volume
Single reaction
0.75 !L
0.75 !L
1.5 !L
0.75 !L
X !L*
X !L*
0.5 !L
10.0 !L
X values as determined by the primer optimization reaction
85
Table 2.13: Thermocycler settings for PCR amplification
Step
1
2
3
4
5
Program
Denaturing
Denaturing
Annealing
Extension
Extension
Temperature
94 ºC
94 ºC
x ºC*
72 ºC
72 ºC
Time
4 min.
30 sec.
30 sec.
30 sec.
2 min.
Cycles
x1
x35
x1
* Annealing temperature was determined by the primer optimization reaction
2.4.3. RESTRICTION ENZYME DIGEST
Restriction fragment length polymorphism (RFLP) is a method for identifying different
variations at the same loci. The sample undergoes PCR as normal then an additional
restriction enzyme step is required to allow visualization of each allele of the SNP. Restriction
enzymes that cut the sequence for one allele but not the other are chosen (see section 2.4.1).
When the sample is analysed by gel electrophoresis, the two alleles which have distinct base
pair (bp) sizes allow easy identification. Following PCR, 10Pl of the appropriate enzyme
cocktail was added to each well of the reaction. Enzymes that required an incubation
temperature of 37qC were left overnight in the 37qC controlled temperature room for
approximately 24hrs. For all other incubation temperatures the samples were heated on a PCR
machine for two hours. Buffers are outlined in 2.9.2 on page 116.
Table 2.14: Restriction digest reaction mix per reaction for enzymes not requiring BSA
Reagent
Restriction enzyme (10,000 U)
Buffer (Specified for each enzyme)
Distilled water
Total
Per reaction
0.2 µL
2.5 µL
7.3 µL
10.00 µL
Table 2.15: Restriction digest reaction mix per reaction for enzymes not requiring BSA
Reagent
Restriction enzyme (10,000 U)
Buffer (Specified for each enzyme)
Distilled water
BSA
Total
* For a list of enzymes requiring BSA see Table 2.26
86
Per reaction
0.2 µL
2.5 µL
7.05 µL
0.25 µL*
10.00 µL
2.4.4. GEL ELECTROPHORESIS
2µl of 6x Loading Dye (4.0% Sucrose, 0.25% Bromophenol blue) was added to each sample
and a 15µl aliquot was analysed by electrophoresis on an agarose gel. Gels were
electrophorised in tanks contained 1x TBE buffer and 20mg/mL of ethidium bromide.
Samples underwent gel electrophoresis on a 3.5% agarose gel at 160 volts for 40 minutes.
Table 2.16: 3.5% agarose gel
Reagent
1x TBE Buffer
Ethidium Bromide
Agarose
Volume
150 mL
3 !L
5.25 g
!
!
Genotypes were assigned using the information visualized from gel electrophoresis. Bands
were sized using a low molecular weight marker to identify the alleles (Figure 2.19). Smaller
bands ran further through the gel and represented an allele that allowed a cut at the restriction
site. Larger bands travelled slowly through the gel and these represented an allele that lacked
the restriction site. Information from Ensembl (http://www.ensembl.org/index.html)
population data were used to establish the minor allele and this was scored as a 2. The major
allele was scored as a 1.
200
150
11
12
22
100
75
50bp
Figure 2.19: An example of a RFLP assay, showing 18 NZ RA case DNA samples over rs872624 in the DISC1
gene. Homozygous for allele 1 (major allele) represented by 1/1, homozygous for allele 2 (minor allele)
represented by 2/2 and heterozygous for allele 1 and 2 was represented by 1/2. Low molecular weight ladder,
3.5% agarose gel.
87
2.4.5. TAQMAN
This automated assay differentiates between two alleles of a SNP by utilising two fluorogenic
probes (6FAM dye-MGB and VIC dye-MGB labelled probes). The increased specificity of
this assay is due to quantification of an allele-specific probe sequence in addition to the
primer sequence. This is because each probe will bind to the sequence only if it contains a
specific allele. The TaqMan probe contains two fluorophores, a reporter on the 5’ end and a
quencher on the 3’ end. When these two fluorophores are in close proximity to one another
(i.e. the probe is unbound) there is no fluorescent activity detected. The probe is broken apart
as the DNA polymerase builds up the complementary strand. This releases the reporter
fluorophore from the quencher and fluorescence specific to that probe is detected (FAM dye
detected at wavelength 533nm, VIC dye detected at 568nm). A graphical representation of the
TaqMan process is available at http://www.dkfz.de/gpcf/lightcycler480.html.
The protocol for preparation and implementation of a TaqMan assay is as follows: The
reaction mix (see Table 2.17) was vortexed and spun down before being added at a volume of
3Pl to each well in a 384-well plate using a Biohit dispenser. The 2x TaqMan Universal PCR
Master Mix contains 5’ nuclease and optimised buffer in reaction with probes. A 2Pl aliquot
of sample DNA (6µl/mL) was added to each well using a multichannel pipette. The pipette
dispenses DNA into wells in every second row and this was compensated for in the analysis
of the plates. The plate was sealed and spun down at 1000rpm for 1 minute then stored on ice
and protected from light with a tinfoil cover until ready to run on the LightCycler® 480
version 1.5 (Roche Applied Science, USA) .
Table 2.17: TaqMan reagents for a single reaction.
Reagent
2x TaqMan Universal PCR Master Mix
40x SNP assay
ddH2O
Total
88
Per reaction
2.73 µL
0.07 µL
0.2 µL
3.00 µL
The plate was loaded into the LightCycler® and the software was launched from the desktop
of the LightCycler® PC. A new experiment option was selected followed by the apply
template option. The TaqMan SNP genotyping file was chosen from the Merriman Lab
Templates folder. Once this was set up the Run button could be selected. The plate was
named according to the SNP and sample set being analysed.
While the plate was running the subsets were set up. Typically four DNA plates each with 96
samples are run on one 384 well TaqMan plate. Each DNA plate of samples was recorded as
an individual subset and was highlighted and labelled in the LightCycler® software according
to the plate of samples they correspond to (i.e. NZC1, see Table 2.10). Selecting the apply
option saved the selection and allowed separate analysis for each subset. Occasionally less
than four sample sets were required. In order to omit the blank wells from the analysis, they
were not included in a subset.
Once the run had completed, the data were saved to the computer database and then to a
portable disk drive to allow analysis to be undertaken at another computer station. Clicking
the + symbol on the drop down menu brought up the window for performing an analysis. The
analysis type had to be changed to TM CALLING then each subset was selected and analysed
separately. The MINI MELT PROGRAM was selected and the colour compensation was
turned ON and set to IN DATABASE. The samples were then sorted into the correct order by
toggling the POSITION button on the table at the bottom left. The data files for each colour
channel were exported separately by right clicking on the graph to bring up the EXPORT
function. The data were moved across to the second tab and the file was exported as .xml to
an external data drive. This export process was repeated for each colour and each subset. The
analysed results were then saved in the LightCycler® program for future reference. The
LightCycler® analysis template on the lab PC machine was used to analyse data transferred
from the LightCycler® machine on the data drive. The data were opened in excel as a read
only workbook and copy and pasted into the template workbook. This automatically generates
a graphical representation of the data in a new worksheet (see Figure 2.20). Occasionally, the
genotypes may not fall correctly within in the preset boundaries on the graph. For example,
the boundary separating the 11 individuals from the 12 individuals may fall too closely to one
side. This can be corrected by changing the size of the angle between the Y axis and the
89
boundary manually. Once all corrections had been made the data were saved onto the external
data drive and could be used for further analysis (see section 1.1.1.1).
A
B
Figure 2.20: The two forms of TaqMan analysis software output. Each point on the graph is a genotyped
individual from the sample subset. A) The initial graphical representation of the data for AKT1_rs2494731 over
the DNA plate NZRA1 produced within an excel spreadsheet. The boundaries between the genotypes are
represented by double lines. Individuals that fall outside of these boundaries were scored as unknown.
Individuals close to the X/Y intercept failed genotyping and were scored as 00. Manual alterations could be
made at this stage. B) The final graphical representation of the data for DISC1_rs9431714 for the DNA plate
OXRA4 produced by LightCycler® 480 software 1.5. The three genotypes (11, 12, and 22) are represented by
the three different colours. Blue and green represent the homozygous genotypes and red is the heterozygous. The
homozygous group with the most genotypes was designated 11.
2.4.6. QUALITY CONTROL
Quality control measures ensured samples had correct genotype calling and that no plates
were accidentally inverted during experimentation. Eight samples from each of the NZ case
and control boxes were aliquoted into a separate 96-well box. This box was analysed in the
same way as the rest for that SNP (i.e. either TaqMan or RFLP) and the data were compared
to the previous genotypes. If samples from the original genotyping did not match the quality
control genotyping, the scoring of the samples was investigated further. If there was a mistake
in the recording of the score, the whole plate was checked and samples recorded again where
necessary. If there wasn’t an obvious problem with scoring the sample genotyping was
repeated and the new score recorded.
90
2.4.7. DATA ANALYSIS
2.4.7.1. Association Analysis
The web based program SHEsis (http://analysis.bio-x.cn/myAnalysis.php) was used to assess
the association of specific SNPs with RA (Shi & He, 2005). Genotyping data were entered
into the web based form for cases and controls as shown in Figure 2.21. This program
provided Odds Ratios (ORs), 95% Confidence Intervals (CIs) and Fishers and Pearson’s p
values for the allelic and genotypic frequencies of each SNP. Each sample set was analysed
separately and then together as a combined sample set. To identify if the SNP was associated
with RA in one sex, male case subjects were analysed against female case subjects. SNPs that
differed significantly (Fisher’s and Pearson’s p values<0.05) in frequency between cases and
controls were analysed in further sample sets (i.e. OXRA and UKRA). The effect size of this
difference was measured as an OR with 95% CIs. CIs of 95% indicate that the OR value of
another sampling subset for the population would fall within the stated parameters 95 times
out of 100. An OR>1 indicated that the allele conferred a susceptibility to the disease and an
OR<1 conferred protection. ORs that were far from one or had a confidence interval that did
not cross one were significant.
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Figure 2.21: Data input for single site analysis in SHEsis. Number of sites is the number of SNPs analysed. The
SNP genotype calls were entered as a single line of data for each sample number. Fields related to haplotype
analysis were left blank.
92
2.4.7.2. Hardy-Weinberg Testing
SHEsis also provided statistical information (ORs, 95% CIs and Fishers and Pearson’s p
values) on the HW value. In this thesis Pearson’s p values of > 0.05 were said to be
conforming to HWE. Each individual plate of samples (see section 2.1.4) was assessed for
every SNP. This information was then combined to provide an overall HW score for the cases
plates and the control plates for that SNP. Where individual plates were in HWD the scoring
of the genotypes was investigated. If there were no errors recording genotypes then the
quality of the assay was taken into account. Assays of a poorer quality generally had problems
with the ability to score the genotypes and these plates were repeated. Following these
corrections, if a plate was still in HWD then the entire set of cases or controls was
investigated for scoring errors. If the scoring and genotyping assay were of high quality then
the plate was included in all subsequent analyses. Occasionally, inclusion of a single plate in
HWE would cause the combined score for that set of cases or controls to fall in HWD. If
these plates had the genotyping repeated and calling was recorded accurately then they were
included in the analysis.
2.4.7.3. Haplotype association analysis
A haplotype is a set of SNPs on the same chromatid that are statistically associated (i.e. in
high LD) and are therefore transmitted together. Haplotypes provide stronger evidence for
disease association than single SNPs due to increased power for detection (Liu et al., 2008).
For example, weak effects at multiple loci in LD can be analysed together to give a
statistically significant association for the gene overall. Two or more markers in LD within
the same gene can be analysed for association to RA. In this thesis BC|SNPMax (GWAS
studies) and SHEsis (genotyping data) were utilised to identify haplotypes of SNPs
significantly associated with RA.
SHEsis utilises a simple fixed-point iteration (FPI) algorithm to estimate haplotype
frequencies (Shi & He, 2005). The accuracy of this method is proportionate to the number of
samples used in the dataset (Shi & He, 2005). The genotyping calls were entered into SHEsis
for all SNPs in the haplotype and analysed cases against controls with a lowest frequency
93
threshold of 0.03 (3%). The selection of variables and input of data is outlined in Figure 2.22
below. The full set of SNPs in LD was analysed for association with RA. Next, different
combinations of these SNPs were analysed in the same way. Only those haplotypes with a
frequency above 3% were analysed. Haplotypes were also assessed for male case subjects
versus female case subjects. This was to identify if any of the haplotype effects were sex
specific (see section 2.2.3).
Figure 2.22: Data input for haplotype analysis in SHEsis. Pair-loci D’/r2 value provides information of the LD
between selected SNPs. Haplotype analysis the SNPs together as one instead of individually. Number of sites is
the number of SNPs analysed. There were three individual sites for haplotype analysis and the lowest frequency
threshold was 3%. The SNP genotype calls were entered as a single line of data for each sample number.
The same combinations of SNPs were also analysed in the WTCCC and GAIN using
BC|SNPmax, to provide a comparison with the genotyped data. The protocol for obtaining
haplotype information from BC|SNPmax is as follows: Select the appropriate dataset from the
genotypes tab and click GWAS under analysis in the left hand pane. Select PLINK haplotypic
association under the association and D header. Enter the following variables: run title,
affection status folder and dataset, and marker map. In the ‘include only’ field list the SNPs
for the haplotype analysis (see Figure 2.23). Choose the basic allelic test for the test type and
94
select run. The association results for different combinations of SNPs as well as all of the
SNPs together were available in the results archive once the run was complete. This
information was utilised to provide a comparison with the haplotype analysis results for the
genotyping (i.e. NZRA) data.
Figure 2.23: Run variables for a haplotype association analysis for selected DISC1 SNPs in the WTCCC RA
dataset.
2.4.7.4. Conditional analysis
Conditional analysis was utilised to assess whether a significant haplotype indicated two or
more independent effects within a gene or whether the significance seen was due to the LD
between the SNPs. The protocol is as follows: A Map file and a Ped file need to be created for
the data (see Figure 3.27). The Map file contains information on the chromosome, rs number,
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genetic distance (0) and BP position. The Ped file includes information on the Family ID,
Individual ID, Paternal ID, Maternal ID, Sex (1=male; 2=female; other=unknown) and
Phenotype (1=controls; 2=cases; 0=unknown). These files were created in excel and saved as
a text file. Open the text file and save as unix with the suffix .map or .ped.
Figure 3.24: Example layout for Map and Ped files used in conditional analysis.
The newly created files are saved into a PLINK analysis folder containing information for the
analysis. Open the command line program Terminal (version 2.0.2), change directories to the
PLINK folder and input the analysis command:
./plink --ped pedfilename --map mapfilename --hap-snps rs#-rs# --chap --independenteffect rs#
If you have no gender information in your ped file (just 0 listed for everyone) add the
command –allow-no-sex:
./plink --ped IL2.ped --map IL2.map --chap --hap-snps rs6822844-rs907715 -independent-effect rs907715 --allow-no-sex
An output file (plink.chap) will be saved to the PLINK folder. This file contains statistical
information on the independence of the SNPs tested. If P<0.05 then the results are
independent of each other. If P>0.05 then this indicates there is a single association that has
been revealed at two SNPs due to the LD between the SNPs.
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2.5. METAͲANALYSISOFGENOTYPINGANDPUBLISHED
LITERATURE
Meta-analysis is a form of statistical analysis which combines a series of related studies to
produce a common effect size (OR). In this thesis, the different sample sets were combined to
produce a combined OR, 95% CIs and an allelic and Breslow-Day (B-D, test for
heterogeneity between studies) p-value for each SNP. The pooling of samples increases the
power of the study to detect small to moderate effects on the disease (see section 1.5.3). Metaanalysis was used to combine the sample sets genotyped in this study with the data obtained
for the various groups from BC|SNPmax.
2.5.1. META-ANALYSIS OF GENOTYPING
STATA is one program which provides a form of statistical analysis for individual SNPs over
various sample sets. These tests were undertaken to obtain the combined Mantel-Haenszel
(M-H) odds ratios (ORs), to calculate the allelic and Breslow-Day (B-D, test for
heterogeneity) p-values and to generate the odds-ratio meta-analysis plot.
A tab delimited text file with the information outlined in Table 2.18 below was created for
each SNP, where case1 is allele 2 (minor/lesser count) in cases and case0 is allele 1
(major/higher count) in controls.
Table 2.18: Example of STATA input file layout
Trial Number
Trial Name
Year
Case1
Control1
Case0
Control0
1
Example
2009
252
850
1520
1750
2
Example2
2009
253
852
1542
1852
In the STATA program the file was imported by choosing the ASCII data created by a
spreadsheet option under file, then import. The text file was located using the browse function
with the enable all files option selected. Typing ‘describe’ in the command window allowed
97
the program to identify the data. The analysis could then be undertaken by typing the
following in the command window:
metan case1 control1 case0 control0, or label(namevar=trialnam)
The information provided included p-values, odds ratios and a plot graph delineating odds
ratios of different sample sets. The level of heterogeneity was also assessed and presented as
the B-D p-value. A value less than 0.05 indicated a significant level of heterogeneity between
the sample sets in the analysis. This meant that the sample sets differed significantly in some
respect (i.e. population, ethnicity, disease parameters). For SNPs with significant
heterogeneity score the meta-analysis was run again with the following command:
metan case1 control1 case0 control0, or random label(namevar=trialnam)
The randomisation of the analysis (see section 1.1) did not affect the B-D p value but did
allow for differences between the sample sets by conservatively altering the ORs and allelic p
values.
2.5.2. META-ANALYSIS OF PUBLISHED LITERATURE
A meta-analysis study of the published data for genes genotyped in this thesis was also
carried out. A PubMed (http://www.ncbi.nlm.nih.gov/pubmed) search was utilised to obtain
studies with the necessary information. The search terms included the gene name acronym
(i.e. AKT1) and schizophrenia. The resulting literature was scanned for SZ (or other related
neurological diseases) case-control association studies containing SNPs in the genes of
interest. The information required for meta-analysis included sample size and allele
frequencies. Where this information was not contained in either the publication or the
supplementary material the study was excluded from the analysis. A STATA output graph
was created for Asian and Caucasian populations for each SNP with more than two
publications in each population.
This analysis was also done twice, once with WTCCC included and once with WTCCC
excluded. As mentioned in section 2.5.1 this method provides information on whether the
disease pathway is common to both SNPs or separate for each.
98
2.6.
MOUSESAMPLEPREPARATION
2.6.1. ETHICAL APPROVAL
The University of Otago Animal Ethics Committee approved all housing, breeding and
procedures conducted with the mice in this study. ERMA approval was obtained for all
transgenic mouse strains. Approval codes:
Mus musculus Linnaeus 1758
x GRIK1: ERMA- GMC001197
x GRIK2: ERMA- GMC001197
Biosecurity Authority/Clearance Certificate
x GRIK1 and GRIK 2: CUSMOD Release no – AF100100518364
2.6.2. BREEDING THE MOUSE STRAINS
2.6.2.1. KRN
KRN mice were kept in colony as heterozygotes and bred to wildtype B6 for maintenance
(see Figure 2.25). The resulting heterozygote KRN offspring were identifiable based on their
agouti coat colour. These mice were also systematically genotyped to confirm heterozygosity.
These mice were then crossed to B6 to keep the transgene in stock. Mice were kept as
heterozygous of KRN due to the deleterious effects of the homozygote phenotype. The mice
were sourced from Institut de Genetique et de Biologie Moleculaire et Cellulaire (IGBMC)
and kept in the University of Otago Animal Containment Facility (Hercus-Taieri Resource
Unit, Dunedin Campus).
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2.6.2.2. GRIK
The mixed strain B6/129 GRIK1 heterozygote (5 breeding pairs) and GRIK2 knockout (5
breeding pairs) mice were purchased from the Salk Institute, USA and stored in a mouse
containment facility at the University of Otago. The mice were modified by the insertion of a
DNA fragment, containing a neomycin resistance gene, into the gene of interest. The GRIK2
mice were provided as full knockouts (-/-) and the founder GRIK1 mice were heterozygous
for the knockout (-/+).The breeding plan for GRIK1 knockouts is described in section 2.6.3
below.
2.6.3. THE SERUM TRANSFER MODEL
The heterozygous KRN stock mice were crossed to NOD to produce K/BxN offspring (see
Figure 2.25). Only 50% of the resulting offspring are transgenic for KRN and therefore
develop spontaneous IA. The serum from these individuals was then transferred to GRIK
knockouts for induction of arthritis (see section 2.6.5.7).
In this study, it was intended that IA would be induced via serum transfer. To minimise the
number of variables within this scenario the mixed background B6/129 GRIK mice were bred
to reduce genomic DNA from one strain and increase the DNA from the other. Therefore all
GRIKs were backcrossed onto a B6 strain to minimise the amount of 129 genomic DNA (see
Figure 2.25). To ensure that the 129 DNA component could be reduced in the shortest number
of generation, individuals with high levels of B6 would be chosen for the backcross. A
microsatellite assay (as described in section 2.8 on page 112) was to be used to identify the
percentage of genomic DNA belonging to each strain. At various stages throughout the
backcrossing the GRIK heterozygous progeny were intercrossed to obtain GRIK(-/-) for
serum transfer (see Figure 2.25).
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Figure 2.25: The breeding program for induction of inflammatory arthritis in GRIK knockout mice. GRIK(-/-) =
homozygous knockout, GRIK(+/-)=heterozygous, GRIK(+/+) = wildtype, KRN(+/-) =heterozygous for the KRN
transgene. Where the genotype is not mentioned the mice were wildtype. Only the progeny with genotypes of
interest are shown. Blue box = maintenance backcross. Red line = breeding cycle of GRIK knockouts to achieve
genomes with B6 DNA. Diamonds indicate the colour of the mouse coat (White, grey/agouti or black).
2.6.4. INDUCTION OF IA VIA THE SPONTANEOUS MODEL
As the induction of IA in the GRIK knockouts through serum transfer was ineffective (see
section 3.4.1) a new breeding program was developed. The cross between KRN transgenic
and NOD mice results in the progeny developing spontaneous IA. However, the GRIK
knockouts were supplied on a 129/B6 background. To obtain GRIK knockouts with high
levels of NOD genomic DNA it was intended to backcross these mice to NOD over several
generations (see Figure 2.26) with marker assisted selection. The resulting GRIK knockout
animals would then contain high levels of NOD genomic DNA and could be crossed to KRN
transgenic mice. The resulting progeny will consist of 100% GRIK knockouts with half
transgenic for KRN and half wildtype. Therefore 50% of the offspring should develop
spontaneous IA. The development of IA in the GRIK knockouts is dependent on the effect
this gene has on the disease. If GRIK is involved in development the knockouts should not
have IA or have IA to a lesser degree than non-knockout littermates. If GRIK is not involved
in the disease all offspring will be affected by IA to a similar degree regardless of GRIK
genotype.
101
Figure 2.26: The breeding program for induction of spontaneous inflammatory arthritis in GRIK knockout mice.
GRIK(-/-) = homozygous knockout, GRIK(+/-) = heterozygous, GRIK(+/+) = wildtype, KRN(+/-) =
heterozygous for the KRN transgene. Where the genotype is not labelled the mice were wildtype. Only the
progeny with genotypes of interest are shown. Blue box = maintenance backcross. Red line = Breeding cycle of
GRIK heterozygotes to NOD mice to achieve genomes with maximal NOD DNA. Diamonds indicate the colour
of the mouse coat (White, grey/agouti or black).
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2.6.5. ANIMAL HANDLING
2.6.5.1. Housing Conditions
The mouse housing facility at the University of Otago is a Specific Pathogen Free (SPF)
environment. All animals are kept in the Merriman room in small cages (less than 58cm long
x 27cm wide x 20cm deep) with no more than 10 animals per cage. All cages and the cage
contents were sterilized weekly. Specific pathogen-free (SPF) environmental conditions were
maintained in the housing facilities. All animal handlers were required to wear full-length
surgical gowns, cap, shoe covers, facemask and gloves to avoid skin contact with the mice.
2.6.5.2. Food and Water
Mice were fed a diet of soy/wheat-based chow using New Zealand ingredients. Fresh food
was provided weekly when the cages were replaced. The water supply bottles were checked
and topped up daily as required. These processes were carried out by the Department of
Animal Science laboratory staff.
2.6.5.3. Identification
Cards attached to the outside of each cage were used to record the details of the contained
mice (i.e. mating pair, mouse strain).
2.6.5.4. Weaning
Weaning occurred at approximately twenty-one days post birth. Litters were separated from
their parents and transferred into a new cage. At this time pups were also sexed and the males
and females separated into different cages.
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2.6.5.5. Tail Sample Collection
The Tail-tipping method was utilised when DNA genotyping was required to determine which
animals were required for breeding crosses. This is one of the least invasive DNA collection
methods, therefore minimizing stress to the animals. Once animals had been identified as
potential breeding stock a 5mm section from the end of the tail was cut using a razor blade.
The section was placed in a labelled Eppendorf tube and genomic DNA was extracted (see
section 2.6.6.1).
2.6.5.6. Testing for Inflammatory Arthritis
K/BxN progeny spontaneously developed Inflammatory Arthritis (IA) at approximately 6-7
weeks post birth (see section 1.1.1). IA was diagnosed when the swelling in the ankles
increased by a third of the normal ankle thickness (2.7-3.0mm). Measurements of each animal
were recorded, including ankle size for each leg, weight, sex and strain. When the swelling
reached double the normal thickness the animals were euthanized.
2.6.5.7. Serum transfer
The serum transfer method was utilised to induce IA in otherwise healthy mice (see section
intro 1.6.3.2) . K/BxN progeny that had developed IA had blood samples taken via cardiac
puncture. Approximately 400 Pl was taken from each mouse. The blood of three or four
animals was pooled into a 1.5ml Eppendorf tube and allowed to clot for 30-60 minutes at
room temperature. The tubes were centrifuged at 3000 rpm for 5 minutes and the pellets were
discarded. The remaining serum was pooled with other samples taken on the same day and
stored at -80qC. 250Pl of serum was transferred to each of the test animals (B6 controls,
GRIK1-/- and GRIK2-/-) via Intraperitoneal Injection (IP). The animals were screened for IA
every 2-3 days post transfer. The first symptoms of the disease were expected to appear
around 7-14 days post transfer.
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2.6.5.8. Animal Euthanasia
Animals with swelling over the threshold were immediately euthanized. Those that were
injured or appeared to be suffering were also euthanized. Euthanasia was carried out by
exposing the mice to CO2 gas.
2.6.6. MOUSE GENOMIC DNA EXTRACTION
2.6.6.1. DNA Extraction from Tail-tips
The strain information from the Eppendorf tube was recorded into a lab book and a sample
number was given to each tube. 500µL of tail lysis buffer and 10µL of 10mg/mL Proteinase
K was added to each tube. Each tube was vortexed vigorously for 20 seconds and incubated
on a rotator at 37°C for 24-48hrs to dissolve the tissue. Following this, the tubes were
centrifuged for 5mins at 3000rpm to pellet any remains and the supernatant was transferred to
a new tube. Any remains were kept in the fridge until genotyping success was achieved. An
equal volume of chloroform was added to the solution (~500 µL), vortexed and centrifuged
for 12 minutes at 3000rpm. A cut off 200 µL tip was used to transfer two 150 µL aliquots of
the upper aqueous phase into a new labelled Eppendorf tube. 33 µL of 3mol/L sodium acetate
and 830 µL of cold absolute ethanol were added to the new tube. The solution was left in a
freezer overnight to precipitate out the DNA. The tubes were then centrifuged at 3000rpm for
12 minutes. The ethanol was carefully poured off and 0.5mL of 70% V/V ethanol was added.
This was then centrifuged for another 5 minutes at 3000 rpm and the ethanol poured off. The
tubes were blotted and allowed to air dry for one hour. 100PL TE buffer was added, the tube
vortexed and stored in the fridge for 24hrs to allow the DNA pellet to re-suspend/equilibrate
before PCR.
Table 2.19: Tail lysis buffer for DNA extraction
Reagent
1mol/L TRIS pH 8.0
0.5mol/L EDTA pH 8.0
20% SDS
ddH2O
Total
Volume
5mL
2mL
2.5mL
40.5 mL
50 mL
105
2.6.6.2. DNA Extraction from Mouse Liver
Genomic mouse DNA was extracted from liver samples using the following protocol:
approximately 0.5g of sample tissue was added to a 250Pl beaker with 30ml of extraction
buffer and 0.15ml of Proteinase K. The solution was then mixed with a stick blender for
3minutes and transferred to 50ml Falcon tubes to be incubated in a water bath at 55qC for
approximately 4 to 6 hours. Following incubation an equal volume of phenol was added to
each tube and the mixture was centrifuged for 10minutes at 2000rpm. The supernatant was
removed into new Falcon tubes and an equal volume of chloroform was added to each tube.
The solution was centrifuged again for 10 minutes at 2000rpm. The supernatant was pipetted
off and transferred to a new tube where 20ml of absolute ethanol and 2ml of 7.5M ammonium
acetate was added. Again the mixture was centrifuged for 10 minutes at 2000rpm to reveal a
pellet. The solution was carefully poured off and 5ml of 70% V/V ethanol was added before
vortexing to wash the pellet. The solution was centrifuged again at 2000rpm for 10 minutes
and the ethanol was carefully removed. The pellet was air dried on the bench for 30minutes
before 1.5ml of TE buffer was added and the pellet was re-suspended by vortexing.
2.6.6.3. Phenol Chloroform extraction
The phenol chloroform method was used as a cleanup step to improve the quality of the DNA
sample. The protocol is as follows: 375Pl of the DNA sample was aliquoted into an
Eppendorf tube and an equal volume of phenol chloroform solution was added. The tube was
briefly vortexed and centrifuged for 5 minutes at 3000rpm. The upper layer (~350Pl) was
removed to a new Eppendorf tube and 175Pl of chloroform was added. This solution was
vortexed and centrifuged for 5 minutes at 3000rpm. The supernatant was transferred to a new
Eppendorf tube and 39Pl of 3mol/L sodium acetate and 975 µL of cold absolute ethanol were
added. The tube was vortexed and centrifuged for 5 minutes at 3000rpm. Next, the
supernatant was gently poured off and the pellet was washed in 1mL of 70% V/V ethanol.
This was then centrifuged for another 5 minutes at 3000 rpm and the ethanol poured off. The
tubes were blotted and allowed to air dry for one hour. 100PL TE buffer was added, the tube
vortexed and stored in the fridge for 24hrs to allow the DNA pellet to re-suspend/equilibrate
before PCR.
106
2.6.6.4. NanoDrop spectrophotometery
The spectrophotometer was utilised to determine the concentration of the DNA in the
extracted samples. The protocol is as follows: warm the DNA samples to 37qC for 2 hours,
vortex the tubes and centrifuge at 3000rpm for 30 seconds. Add 2Pl of TE buffer to the
spectrophotometer stage for calibration. Once calibrated clean the stage with Kimberley tissue
and add 2Pl of DNA. Record the reading and clean the stage. Obtain three recordings for each
sample.
107
2.7.
MOUSESAMPLEGENOTYPING
Primer pairs for genotyping were optimised following the protocol outlined in section 2.4.2.1.
The PCR procedure in section 2.4.2.2 was identical to that used to the genotyping of mouse
samples. This process was utilised to identify different genotypes (homozygous wildtype,
heterozygous and homozygous transgenic/knockout) of KRN, GRIK1 and GRIK2.
2.7.1. GEL ELECTROPHORESIS
Samples were analysed by gel electrophoresis on a 3.5% agarose gel at 160 volts for 40
minutes.
Table 2.20: 3.5% agarose gel
Reagent
1x TBE Buffer
Ethidium Bromide
Agarose
Volume
150 mL
3 µL
5.25 g
2.7.2. KRN +/+
As the KRN mice were kept as heterozygotes, genotyping had to take place periodically to
ensure the presence of the transgene. All mice to be tested had DNA extracted from tail-tip
samples (see section 2.6.6.1). Two sets of primers (D and E) were needed for this assay:
D FWD:
D REV:
E FWD:
E REV:
AGGTCCACAGCTCCTTCTGA
GTATTGGAAGGGGCCAGAG
GGGCAAAAACTGACCTTGAA
GAGCCTGGTTGTTTGTGGAT
The PCR reaction was titrated (as described in section 2.4.2.1) and the following conditions
were produced: 4mM Mg2+ at 60qC. The PCR reaction was prepared as in
Table 2.21 for each set of primers separately and the cocktail was added to 1Pl of DNA for
genotyping in a PCR plate. Each genotyping assay was prepared in duplicate. 50Pl of mineral
108
oil was added on the PCR well containing the cocktail and the plate was thermocycled at the
conditions in
Table 2.22.
Table 2.21: PCR reaction mix for a single 25 µL reaction
Reagent
Forward primer (50ng/µL)
Reverse primer (50ng/µL)
4mM dNTPs
50 mM Mg2+
ddH20
Total volume
Single reaction
1.25 µL
1.25 µL
2.5 µL
1 µL
3 µL
14.75 µL
0.25 µL
24 µL
Step
1
2
3
4
5
Program
Denaturing
Denaturing
Annealing
Extension
Extension
Temperature
95 ºC
95 ºC
60 ºC
72 ºC
72 ºC
Time
5 min.
1 min.
1 min.
1 min.
5 min.
Cycles
x1
x30
x1
The PCR product was run out on an agarose gel (section 2.7.1) and visualized under UV light.
The D primers produced a band at 146bp and the E primers gave a band at 227bp. The
presence of either one or both of the bands indicated the animal was heterozygous for the
KRN transgene.
2.7.3. GRIK -/GRIK1 mice were supplied as heterozygotes and GRIK2 as homozygote knockouts.
Genotyping was undertaken to ensure the mice homozygous for the gene knockout were
involved in the serum transfer. Three sets of primers were designed to determine the presence
109
of the knockouts in the GRIK1 mice, GRIK1_1, GRIK1_2 and neo. Only the neo primers
were required to identify presence of the knockout in GRIK2.
GRIK1_1 FWD:
GRIK1_1REV:
GRIK1_2 FWD:
GRIK1_2REV:
Neo FWD:
Neo REV:
5` GCTGAAAGTCGGTGGTGAAT 3`
3` ATCCCTGGGTGTTTCTTCCT 5`
5` CACGCTTGGTCTTGACTCTGGTTTTA 3`
3` ATTAGTTTATATCCCTGGGTGTTTCTTCCT 5`
5` CAGCTGTGCTCGACGTTGTCACTGAAG 3`
3` CATGATATTCGGCAAGCAGGCATCGCCAT 5`
The Neo primer set was utilised to determine whether the Neomycin resistance cassette had
been inserted. The GRIK1_1 and GRIK1_2 primer sets amplified the region of interest, the
GRIK1 gene, only when the neo cassette was absent from the sequence. The primer sets were
titrated (section 2.4.2.1) and optimal conditions chosen (GRIK1_1: 5mM Mg2+ at 55qC,
GRIK1_2: 4mM Mg2+ at 60qC, Neo: 3mM Mg2+ at 55qC). The PCR reaction was made up as
in Table 2.23 and run on the Thermocycler as outlined in Table 2.24.
Table 2.23: PCR reaction mix for a single 10 µL reaction
Reagent
Forward primer (50ng/µL)
Reverse primer (50ng/µL)
4mM dNTPs
50 mM Mg2+
ddH20
Total volume
Single reaction
0.75 µL
0.75 µL
1.5 µL
0.75 µL
X µL*
X µL*
0.5 µL
10.0 µL
* X values as determined by the primer optimization reaction
Step
1
2
3
4
5
Program
Denaturing
Denaturing
Annealing
Extension
Extension
Temperature
94 ºC
94 ºC
x ºC*
72 ºC
72 ºC
Time
2 min.
30 sec.
30 sec.
45 sec.
4 min.
Cycles
x1
x35
x1
* Annealing temperature was determined by the primer optimization reaction
110
The PCR product was run out on an agarose gel (section 2.7.1) and visualized under UV light.
The GRIK1_1 primers produced a band at 417bp, the GRIK1_2 primers gave a band at
760bp, and the neo primers a band at 345bp. Animals homozygous for the GRIK1 knockout
only had a band for the neo primers (see Figure 2.27). If all three bands were present the
animal was heterozygous for the GRIK1 knockout and if only the GRIK1_1 and GRIK1_2
primer bands were present, the animal was homozygous wildtype.
Figure 2.27: The binding pattern of GRIK1 and Neo primers within the GRIK1 gene. A) Wildtype GRIK gene.
GRIK1_1 and GRIK1_2 can bind and produce PCR products of 417bp and 760bp respectively. Neo primers
cannot bind. B) insertion of the Neo cassette interrupts the GRIK1 gene. 245bp of original sequence is replaced
by 1kb of Neo cassette. GRIK1_1 and GRIK1_2 forward primers cannot bind so produce no product. Neo
primers bind within the Neo cassette and produce a PCR product of 300bp.
111
2.8. MOUSEMICROSATELLITEASSAY
Microsatellites are DNA-based markers composed of short repetitive sequences of bases (i.e
CACACACACACA). They are commonly found in eukaryotic genomes with mice having
hundreds of thousands of examples. The variations in microsatellites are produced in the
length of sequence rather than the bases contained within it. The short sequences of DNA that
make up microsatellites are repeated a variable number of times between mouse strains. This
phenomenon can be exploited to identify genomic regions common to different strains of
mice. The region is amplified with PCR by designing flanking primers unique to that area.
The number of repeats defines the size of the PCR product and this can be visualised under
UV light on an agarose gel. The protocols that follow involve using microsatellites to identify
the origin of a mouse DNA segment.
2.8.1. SELECTION OF MICROSATELLITES
2.8.1.1. Microsatellites for the serum transfer model
The GRIK knockout mice supplied for this study were on a mixed B6/129 strain background.
One way in which to minimize the variables with potential to affect the outcome of the serum
transfer model was to reduce the amount of 129 genomic DNA. This was obtained by
repetitively backcrossing the mice onto B6 (see Figure 2.25). An assay to determine the
percentage of B6 DNA was produced.
The microsatellites in the B6/129 assay were chosen based on the informativeness between
the two strains. Previous experiments from the Merriman lab provided information on
microsatellite product size over six different strains (NOD, B6, C3H, DBA, BALBC and
NON) but not for 129 (Hollis-Moffat, 2006). Therefore microsatellite markers were chosen
where the B6 band size was different to that of all the other strains. Genotyping could be
successfully scored when microsatellite markers produced significantly different band size
(>10bp) between strains. This maximized the chance that the marker was informative between
the strains B6 and 129. An even spread of microsatellites over each chromosome was chosen
for testing.
112
2.8.1.2. Microsatellites for the spontaneous IA model
The change from the serum induced IA mouse model to the spontaneous IA model affected
the microsatellite assay. The strains of interest altered from B6 and 129 to B6 and NOD. A
new microsatellite assay was needed to determine the amount of NOD genomic DNA the B6
mice backcrossed to this strain contained. As outlined in section 2.8.1.1 no microsatellite
information was available for the 129 strain. This resulted in the selection of B6/129 assay
microsatellites which had a different product size for B6 compared to all the other strains in
the database (see section 2.4.1.1). One of the strains which was required to have a different
product size to B6 was NOD. This meant that all the microsatellites chosen for their potential
to differentiate between B6 and 129 should also differentiate between B6 and NOD.
2.8.2. PREPARING THE ASSAY
The microsatellite primers were individually optimised as in section 2.4.2.1; those that had
optimal conditions underwent PCR over DNA from three different stocks B6 and 129 to test
for informativeness.
2.8.2.1. Gel electrophoresis
Samples were analysed by gel electrophoresis on a 4%W/V agarose gel at 100 volts for 70
minutes. These samples are run for longer at a lower voltage than samples in SNP genotyping.
This was to allow visualisation of the smaller size differences characteristic of microsatellites.
Table 2.25: 4% agarose gel
Reagent
1x TBE Buffer
Ethidium Bromide
Agarose
Volume
150 mL
3 µL
6g
113
The markers informative between B6 and 129 (i.e. a differing band length in each strain)
chosen to be included in the assay were selected from the gel photograph.
2.8.2.2. Non-informative microsatellites
When a microsatellite failed to titrate or was not informative between B6 and 129 another
marker in a similar location was chosen. The process was repeated until approximately 60
informative microsatellites that represented a spread of the whole genome had been identified.
114
2.9.
MATERIALSANDREAGENTS
Reagents for this study were obtained from: Affymetrix, Inc., California, USA; BDH
Laboratory Supplies, UK; Invitrogen, USA; New England Biolab, Ipswich, USA; Qiagen,
California, USA; Scharlau Chemie, Barcelona, Spain and Sigma Aldrich, Australia.
2.9.1. SOLUTIONS
All solutions were stored at room temperature unless otherwise stated.
Recipes:
Extraction Buffer
Proteinase K
1x TE buffer
1.0 M TRIS (pH 7.5)
0.5 M EDTA (pH8.0)
Made up to 0.1L with distilled water
10x TBE buffer
890 mM TRIS
890 mM Boric acid
20 mM EDTA
pH ~8.3
Made up to 2L with distilled water
6x loading buffer for agarose gel electrophoresis
0.25% bromophenol blue
40% sucrose
3M/L Sodium acetate (pH 5.2)
Na.Acetate.3H2O
Glacial acetic acid
Made up to 100mL with distilled water
115
RBC lysis buffer
9 parts 0.16 M NH4Cl (pH 7.4)
1 part 0.16 M Tris (pH 7.65)
Stored separately and made up as required
Liver lysis buffer
100 mM Tris
20 mM EDTA
20% SDS
Made up to 200mL with distilled water
2.9.2. ENZYME BUFFERS
All enzyme buffers were stored at 4qC unless otherwise stated.
1x NEBuffer 1
10 mM Bis Tris Propane-HCl
10 mM MgCl2
1 mM DDT
Made up to 1.5mL with distilled water
pH ~7.0
1x NEBuffer 2
50 mM NaCl
10 mM Tris-HCl
10 mM MgCl2
1 mM DDT
pH ~7.9
1x NEBuffer 3
100 mM NaCl
50 mM Tris-HCl
10 mM MgCl2
1 mM DDT
pH ~7.0
116
1x NEBuffer 4
50 mM potassium acetate
20 mM Tris-acetate
10 mM Mg-acetate
1 mM DDT
pH ~7.9
1x NEBuffer DpnII
100 mM NaCl
50 mM Bis Tris-HCl
10 mM MgCl2
1 mM DDT
pH ~6.0
117
2.9.3. RESTRICTION ENZYMES
One unit of restriction enzyme is defined as the amount required to digest 1Pg of Ȝ DNA
(Hind III digest) in one hour in a total reaction volume of 50µL. Restriction enzymes were
used at 10,000U/ml and the incubation temperature for digestion varied with each enzyme
(Table 2.26).
Table 2.26: Restriction enzymes utilised in this study and their working conditions.
Enzyme
Aci I
Dpn I
BstU I
Dpn II
Mbo I
TSP509I
Cvi QI
Rsa I
Taq DI
Nla III
Msp I
Restriction site
5'…C^CGC…3'
3'…GGC^G…5'
5'…GA^TC …3'
3'…CA^TG …5'
5'…CG^CG…3'
3'…GC^GC…5'
5'…^GATC…3'
3'…CTAG^…5'
5'…^GATC…3'
3'…CTAG^…5'
5'…^AATT …3'
3'…TTAA^ …5'
5'…G^TAC…3'
3'…CAT^G…5'
5'…GT^AC…3'
3'…CA^TG…5'
5'…T^CGA…3'
3'…AGC^T…5'
5'…CATG^…3'
3'…^GTAC…5'
5'…C^CGG…3'
3'…GGC^C…5'
Buffer for 100%
activity
Activation
temperature
BSA
required
NEBuffer 3
37 ºC
No
NEBuffer 1, 2 and 4
37 ºC
No
NEBuffer 1, 2 and 4
60 ºC
No
NEBuffer DpnII and 3
37 ºC
No
NEBuffer 2, 3 and 4
37 ºC
No
NEBuffer 1, 2 and 3
65 ºC
No
NEBuffer 2 and 3
25 ºC
Yes
NEBuffer 1, 2 and 4
37 ºC
No
NEBuffer 3
65 ºC
Yes
NEBuffer 4
37 ºC
Yes
NEBuffer 2
37 ºC
No
118
2.9.4. WEBSITE ADDRESSES
The Wellcome Trust Case Control Consortium
http://www.wtccc.org.uk/
Foundation for National Institutes of Health
http://www.fnih.org
Molecular Genetics of Schizophrenia
http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs000167.v1.p1
The European Genome-phenome Archive
http://www.ebi.ac.uk/ega/page.php?page=study&study=EGAS00000000011&cat=www.wtcc
c.studies.xml.ega2&subcat=RA&sid=703372d45fb1644192b0212233a45547
NCBI Genotypes and Phenotypes
http://www.ncbi.nlm.nih.gov/dbgap
AKT1 pathway overview
http://www.invitrogen.com/site/us/en/home/Products-and-Services/Applications/Cell-andTissue-Analysis/Signaling-Pathways/Akt.html
Ensembl Genome Browser (Home page)
http://www.ensembl.org/index.html
Haploview 4.1
http://www.broad.mit.edu/mpg/haploview/download.php
International HapMap Project (Home page)
http://www.hapmap.org/
NEBcutter V2.0
http://tools.neb.com/NEBcutter2/index.php
Oligo Calc: Oligonucleotide Properties Calculator
http://www.basic.northwestern.edu/biotools/oligocalc.html
STATA (Meta-analysis)
http://www.stata.com/
SHEsis
http://analysis.bio-x.cn/myAnalysis.php
WTCCC
http://www.wtccc.org.uk/
NCBI – PubMed Online Database
http://www.ncbi.nlm.nih.gov/
119
BC|SNPmax (Biocomputing Platforms Ltd)
http://www.bcplatforms.com/
https://snpmax.otago.ac.nz/bcos/ (secure server)
IMPUTE version 3
https://mathgen.stats.ox.ac.uk/impute/impute.html
PLINK version 1
http://pngu.mgh.harvard.edu/purcell/plink/
HWE calculator version 1
http://ihg2.helmholtz-muenchen.de/cgi-bin/hw/hwa1.pl
Google Scholar
http://scholar.google.co.nz/
OR calculator version 1
http://www.hutchon.net/ConfidOR.htm
European Collection of Cell Cultures
http://www.ecacc.org
120
Chapter 3: Results
3
.
CHAPTERThree
RESULTS 3.1. Genotyping of RA Candidate Genes from the AKT1
Pathways
3.2. Meta Analysis of SNPs from SZ Candidate Genes In the
Literature
3.3. Genotyping mice for KO status
3.4. Induction of Inflammatory Arthritis in a Mouse Knockout
Model
3.5. Microsatellite Assay for Differentiation Between
Genomic DNA From Mouse Strains B6 and 129
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Chapter 3: Results
3.1. GENOTYPINGOFRACANDIDATEGENESFROMTHE
AKT1PATHWAYS
AKT1 has previously been linked to the development of RA (Kim et al., 2002) and SZ (Ikeda
et al., 2004; Schwab et al., 2005). A number of other genes in the AKT1 pathway, such as
DISC1, NFAT and GRIK, have also been implicated in RA and SZ. This evidence suggests
there may be a functional link between the two disorders in this network. This study was
focused on identifying and genotyping SNPs within candidate genes from the AKT1 pathway
to provide evidence for a shared disease network for RA and SZ.
3.1.1.
SNP SELECTION
GWAS publications over large case-control sample sets provided valuable information for
selection of SNPs in this thesis. As outlined in section 2.3.2 all SNPs in the genes identified in
the literature search were extracted from the RA sample set in WTCCC (see section 2.2.1.1)
and the SZ sample set in GAIN (see section 2.2.1.2). The case-control association information
from these two studies was obtained from PLINK via BC|SNPmax (see section 2.3.2) then
aligned for each SNP. As selected SNPs were genotyped over RA sample sets selection was
based on levels of significance in the WTCCC RA dataset (see section 1.4.1). SNPs with a pvalue trending towards significance (p< 0.07) were selected for further analysis. The GAIN
SZ genotyping data were analysed to assess whether any SNPs were significant in both
datasets. It also provided a comparison for the direction of the OR (protective or susceptible)
in each dataset.
3.1.1.1. AKT1
AKT1 had a number of significant (p<0.05) SNPs in the WTCCC RA dataset (Table 3.27).
These and the SNPs trending towards significance (p<0.07) were included in the genotyping
study. Rs1130214 had a p-value outside the levels of significance required for selection in this
thesis. However, as it was one of only two SNPs (also rs2494731) positioned within the AKT1
gene it was chosen for inclusion. Rs1130214 was also a prominent SNP in the AKT1 literature
(see section 1.4.2). There were no significant or trending towards significant SNPs in the
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Chapter 3: Results
GAIN SZ cohort. For one of the SNPs, AKT1_rs7146661, the OR was protective in WTCCC
RA and susceptible in GAIN SZ (see Table 3.28). Three other SNPs, AKT1_rs4983386,
AKT1_rs6644 and AKT1_rs2494731 had an OR conveying susceptibility in WTCCC RA and
protection in GAIN SZ.
Table 3.27: PLINK genotyping information for all AKT1 SNPs in the WTCCC rheumatoid arthritis and GAIN
schizophrenia sample sets. SNPs of interest for this thesis are highlighted in blue.
Table 3.28: A comparison of genotyping data for AKT1 SNPs of interest in the WTCCC and GAIN sample sets.
3.1.1.2. FAT
Although NFATC1_rs2002311 was not significant (p<0.05) in the WTCCC RA dataset it was
trending towards significance and included for genotyping of the NZ case-control sample set
(Table 3.29). The OR for this SNP was in the same direction (susceptible) in both GAIN SZ
and WTCCC RA. No other NFATC1 SNPs were considered for genotyping analysis.
NFATC2_rs8119787 had a p-value (0.057) close to significant in the WTCCC sample set and
123
Chapter 3: Results
was included for genotyping in the NZRA sample set (Table 3.29). The OR was protective for
this SNP in both the WTCCC RA and GAIN SZ datasets (see Table 3.31). There were no
other SNPs of interest in this gene for either the WTCCC or the GAIN sample sets.
Table 3.29: PLINK genotyping information for all NFATC1 SNPs in the WTCCC and GAIN sample sets. SNPs
of interest for this thesis are highlighted in blue.
Table 3.30: PLINK genotyping information for the NFATC2 SNPs with the most significant p-values in the
WTCCC and GAIN sample sets. SNPs of interest for this thesis are highlighted in blue. Table shows SNPs with
a p-value< 0.16, a complete list is available in the appendix.
Table 3.31: A comparison of genotyping data for NFATC1and NFACTC2 SNPs of interest in the WTCCC and
GAIN sample sets.
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Chapter 3: Results
3.1.1.3. DISC1
The DISC1 gene has many significant SNPs in both the WTCCC and GAIN (Table 3.32). All
of the SNPs with a significant (p<0.05) or trending towards significant (p<0.07) p-value in the
WTCCC RA were considered for analysis over the NZ case-control sample set.
Table 3.32: PLINK genotyping information for DISC1SNPs with the most significant p-values in the WTCCC
and GAIN sample sets. SNPs of interest for this thesis are highlighted in blue. Table shows SNPs with a pvalue<0.1, a complete list is available in the appendix.
All of the highlighted SNPs from Table 3.32 above continued to the next stage of the
selection protocol (see section 3.1.2). However, the SNPs of particular importance for the
hypothesis testing were the ones that conferred a protective OR in one sample set and a
susceptible OR in the other (refer to section 1.3.1). Table 3.33 shows a comparison of
genotyping information between WTCCC RA and GAIN SZ for the SNPs that were
genotyped in both sample sets (all of the highlighted SNPs from Table 3.32).
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Chapter 3: Results
Table 3.33: A comparison of genotyping data for DISC1SNPs of interest in the WTCCC and GAIN sample sets.
3.1.1.4. BAD
BAD had no significant SNPs in the WTCCC and only one significant SNP in the GAIN
sample set. This gene was not included in the genotyping study and was left out of the SZ
candidate gene meta-analysis.
Table 3.34: PLINK genotyping information for all BAD SNPs in the WTCCC and GAIN sample sets.
3.1.1.5. CCND1
CCND1 had no significant SNPs in the WTCCC or GAIN sample sets. This gene was not
included in the genotyping study and was left out of the SZ candidate gene meta-analysis.
Table 3.35: PLINK genotyping information for all CCND1 SNPs in the WTCCC and GAIN sample sets.
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Chapter 3: Results
3.1.1.6. GSK3B
GSK3B was also deemed unsuitable for genotyping in this study due to the lack of significant
(or trending towards significant) SNPs in the WTCCC. This gene was also left out of the
candidate gene meta-analysis.
Table 3.36: PLINK genotyping information for all GSK3B SNPs in the WTCCC and GAIN sample sets.
127
Chapter 3: Results
3.1.1.7. PDK1
SNPs within the gene PDK1 showed a lack of significance in the WTCCC and GAIN sample
sets (Table 3.37). This gene was omitted from all future study in this thesis.
Table 3.37: PLINK genotyping information for all PDK1 SNPs in the WTCCC and GAIN sample sets.
3.1.1.8. PTEN
PTEN had one (PTEN_rs2299939) significant (p = 0.038) SNP in the GAIN sample set and
none in the WTCCC (Table 3.38). This gene was omitted from further study.
Table 3.38: PLINK genotyping information for all PTEN SNPs in the WTCCC and GAIN sample sets.
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Chapter 3: Results
3.1.1.9. Beta-CTN (E-CTN)
No significant SNPs were found in either the WTCCC or the GAIN sample sets for E-CTN
(Table 3.39). No future study was conducted on this gene.
Table 3.39: PLINK genotyping information for all E-CTN SNPs in the WTCCC and GAIN sample sets.
3.1.2.
LINKAGE DISEQUILIBRIUM ANALYSIS
3.1.2.1. AKT1
To allow a good spread of SNPs over the gene the pattern of LD was assessed. Of the SNPs of
interest in WTCCC (Table 3.27), only two were from within the AKT1 gene
(AKT1_rs2494731 and AKT1_rs1130214, see intro section 1.4.2). Both of these SNPs were
only trending towards significance but as they were not in LD with each other (Figure 3.28)
they were chosen for genotyping with TaqMan assays. Three SNPs (AKT1_rs7146661,
AKT1_rs4983386 and AKT1_rs6644) downstream of AKT1 were significantly associated with
RA. Appropriate RFLP primers were unable to be designed for AKT1_rs6644. However the
SNP AKT1_rs4983386 was also significant in the WTCCC and was in high LD with
AKT1_rs6644 (r2= 96, Figure 3.28). RFLP assays for both AKT1_rs4983386 and
AKT1_rs7146661 were designable and genotyping was performed for these loci. Two
additional loci were investigated to increase the spread of genotyping over the gene.
AKT1_rs7146030 and AKT1_rs10137814 had primers designed; however, the RFLP
genotyping assay did not produce distinct bands and both were disregarded.
129
Chapter 3: Results
Figure 3.28: A Haploview LD plot showing the SNPs of interest in and around the gene AKT1. SNPs of
interest are highlighted within boxes (green box = downstream of AKT1, red box = within AKT1) and those
that an assay could not be prepared for are crossed out in red.
3.1.2.2. NFAT
LD analysis was unnecessary for NFATC1 and NFATC2. This is due to each gene only
having one SNP of interest (NFATC1_rs2002311 and NFATC2_8119787) in the WTCCC
genotyping data. NFATC1_rs2002311 is located on chromosome 18 at position 75,274,967
and NFATC2_8119787 is location on chromosome 20 at position 49,580,478. In addition, the
RFLP genotyping assay provided clear results on the first attempt and no additional assays
had to be designed. The LD plot for these genes is available in the appendix.
3.1.2.3. DISC1
When a LD plot for DISC1 was prepared it was found that most of the significant SNPs in the
WTCCC study fell in the same haplotype block (see Figure 3.29). All the SNPs, except
DISC1_rs9431714 (block 17), were within block 21. Where SNPs were in complete LD with
others in DISC1 only one was chosen for analysis.
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Chapter 3: Results
Black squares: r2 =1
White squares: r2 = 0
Figure 3.29: Haploview LD plot (r2) showing haplotype block 21(indicated as block1 in this figure) of DISC1,
generated from HapMap genotyping. This block was found to include the majority of SNPs associated with RA
in genome-wide association analysis. SNPs of interest are highlighted in green and those that an assay could not
be prepared for are crossed out in red.
Of the SNPs of interest identified in the WTCCC RA study, three were in complete LD with
each other, DISC1_rs821577, DISC1_rs1341555 and DISC1_rs701158. From this set only
DISC1_rs701158 was chosen for genotyping analysis. Primers were designed for this SNP but
optimal conditions could not be obtained in the titration phase. This SNP was discarded and
an assay was successfully designed for DISC1_rs821577. There were no issues with the
primer design or titration for DISC1_rs872624 and this was completed using an RFLP assay.
TaqMan probes were ordered for the three SNPs DISC1_rs872625, DISC1_rs9431714, and
DISC1_rs4658966. Although these SNPs were genotyped using TaqMan there was
insufficient DNA available to complete the OXRA sample set. Only two of these SNPs
(DISC1_rs872625 and DISC1_rs9431714) were completed over the UKRA sample set for the
same reason. Primers were designed for a forced cut site (see section 2.4.1) at
DISC1_rs821585 however the assay produced many non-specific bands and was therefore not
accurate enough for genotyping. A second set of primers was designed around another forced
cut site at the same loci and, again, the assay quality* was not high enough for genotyping. A
*
High assay quality refers to clarity of the bands allowing the fragment size to be accurately measured and a
absence of non-specific bands. An example of an assay of high quality is shown in Figure 2.19.
131
Chapter 3: Results
second locus (DISC1_rs1341556) that was in complete LD with DISC1_rs821585 was
investigated. Primers were designed but the assay was of poor quality and both loci were
discarded.
3.1.3.
SNPS WITHIN AND SURROUNDING AKT1
3.1.3.1. Case-Control Association Analysis
Case-control analysis was completed in order to replicate the association results found in the
WTCCC. This was usually completed by RFLP as outlined in section 2.4. However, TaqMan
Assays (see section 2.4.5) were used in this thesis where it was anticipated that SNPs would
be genotyped over the UK and OX case control sample sets. These sample sets had limited
volumes of each DNA sample available and the TaqMan assays required less DNA than
RFLP assays.
Association analysis for AKT1_rs2494731 (p = 0.498), AKT1_rs1130214 (p =0.93),
AKT1_rs4983386 (p =0.335) and AKT1_rs7146661 (p =0.72) showed no evidence for
association with RA in the NZ sample set (Table 3.40). Analysis over the Oxford sample set
also found no association of the SNPsAKT1_rs2494731 (p =0.127) and AKT1_rs1130214 (p
=0.105) with RA (Table 3.40). No association was found with RA in the UK sample set for
AKT1_rs2494731 (p =0.112), AKT1_rs1130214 (p =0.685) and AKT1_rs4983386 (p =0.893,
Table 3.40). There was an association between RA and the UK sample set for
AKT1_rs7146661 (p = 0.004). However, this effect was in the opposite direction (OR = 1.61)
to that found for the NZRA and WTCCC (OR = 0.907 and OR = 0.838 respectively) datasets.
The results from the recent RA GWAS by Stahl et al. are included for comparison (2010).
None of the AKT1 SNPs investigated in this thesis reached significance in the Stahl dataset.
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Chapter 3: Results
Table 3.40: Case-control analysis for the AKT1SNPs AKT1_rs2494731, AKT1_rs1130214, AKT1_rs4983386
and AKT1_rs7146661. Genotypes, minor allele frequencies (MAF), odds ratio (95% CI) and the allelic p-value,
for the SNP are from case-control analysis for the follow datasets: NZRA, OXRA (where applicable), UKRA,
WTCCC and GAIN sample sets. Stahl et al., RA GWAS data included to provide a comparison.
SNP
Sample
set
NZRA
rs2494731
OXRA
UKRA
WTCCC
GAIN
Stahl
NZRA
rs1130214
OXRA
UKRA
WTCCC
GAIN
Stahl
NZRA
rs4983386
UKRA
WTCCC
GAIN
Stahl
NZRA
rs7146661
UKRA
WTCCC
GAIN
Stahl
Major/
Minor Samples
Controls
G/C
Cases
Controls
G/C
Cases
Controls
G/C
Cases
Controls
G/C
Cases
Controls
G/C
Cases
Controls
G/C
Cases
Controls
C/A
Cases
Controls
C/A
Cases
Controls
C/A
Cases
Controls
C/A
Cases
Controls
C/A
Cases
Controls
C/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/C
Cases
Controls
G/C
Cases
Controls
G/C
Cases
Controls
G/C
Cases
Controls
G/C
Cases
11
12
215 (0.427)
246 (0.489)
305 (0.448)
297 (0.436)
229 (0.463)
209 (0.422)
277 (0.406)
325 (0.476)
52 (0.344)
82 (0.543)
142 (0.444)
141 (0.441)
1341 (0.457) 1280 (0.437)
786 (0.423)
859 (0.462)
574 (0.438)
581 (0.444)
499 (0.434)
545 (0.474)
4344 (0.454) 4193 (0.439)
1831 (0.456) 1759 (0.438)
247 (0.471)
216 (0.412)
343 (0.460)
326 (0.437)
244 (0.501)
198 (0.407)
320 (0.452)
318 (0.449)
77 (0.513)
68 (0.453)
184 (0.563)
124 (0.379)
1463 (0.499) 1203 (0.411)
858 (0.462)
840 (0.452)
646 (0.493)
559 (0.427)
569 (0.494)
489 (0.424)
4686 0.491) 4010 (0.420)
1985 (0.497) 1655 (0.414)
187 (0.343)
257 (0.472)
233 (0.312)
352 (0.472)
37 (0.268)
70 (0.507)
79 (0.250)
157 (0.497)
945 (0.322) 1424 (0.486)
535 (0.288)
943 (0.508)
395 (0.302)
641 (0.489)
352 (0.306)
590 (0.513)
3142 (0.329) 4671 (0.489)
22
42 (0.083)
79 (0.116)
57 (0.115)
81 (0.119)
17 (0.113)
37 (0.116)
311 (0.106)
214 (0.115)
155 (0.118)
106 (0.092)
1023 (0.107)
424 (0.106)
61 (0.116)
77 (0.103)
45 (0.092)
70 (0.099)
5 (0.033)
19 (0.058)
263 (0.090)
160 (0.086)
105 (0.080)
94 (0.082)
857 (0.090)
353 (0.088)
101 (0.185)
161 (0.216)
31 (0.225)
80 (0.253)
564 (0.192)
379 (0.204)
274 (0.209)
208 (0.181)
1738 (0.182)
MAF
0.328
0.334
0.326
0.357
0.384
0.336
0.324
0.346
0.34
0.329
0.326
0.325
0.323
0.322
0.296
0.323
0.26
0.248
0.295
0.312
0.294
0.294
0.300
0.296
0.421
0.452
0.478
0.502
0.435
0.458
0.454
0.437
0.426
Odds ratio
(95% CI)
1.028
(0.864-1.222)
1.144
(0.962-1.360)
0.811
(0.611-1.078)
1.103
(1.011-1.203)
0.952
(0.845-1.072)
0.993
(0.939-1.040)
0.996
(0.841-1.180)
1.139
(0.954-1.359)
0.937
(0.685-1.282)
1.084
(0.991-1.185)
0.996
(0.880-1.126)
0.981
(0.927-1.030)
1.133
(0.968-1.326)
1.097
(0.827-1.457)
1.097
(1.010-1.192)
0.936
(0.836-1.048)
1.047
p-value
(allelic)
1294 (0.322) 1924 (0.480)
283 (0.553)
200 (0.391)
431 (0.586)
264 (0.359)
76 (0.551)
56 (0.406)
114 (0.442)
110 (0.426)
1546 (0.532) 1153 (0.397)
1075 (0.582) 673 (0.364)
747 (0.570)
480 (0.366)
618 (0.537)
448 (0.390)
5421 (0.568) 3532 (0.370)
794 (0.198)
29 (0.057)
40 (0.054)
6 (0.043)
34 (0.132)
207 (0.071)
100 (0.054)
83 (0.063)
84 (0.073)
597 (0.062)
0.438
0.252
0.234
0.246
0.345
0.270
0.236
0.247
0.268
0.247
(0.993-1.103)
0.907
(0.753-1.092)
1.611
(1.160-2.237)
0.838
(0.761-0.922)
1.118
(0.983-1.271)
0.990
0.923
2307 (0.575) 1436 (0.358)
267 (0.067)
0.246
(0.932-1.050)
133
0.498
0.127
0.112
0.027
0.417
0.829
0.930
0.105
0.685
0.005
0.980
0.737
0.355
0.893
0.047
0.248
0.72
0.004
0.0003
0.088
0.916
Chapter 3: Results
3.1.3.2. Meta-analysis
Meta-analysis was performed to combine the genotyping for each of the four SNPs,
AKT1_rs2494731, AKT1_rs1130214, AKT1_rs4983386 and AKT1_rs7146661 using STATA
software. The analysis was performed twice for each SNP, once with GAIN excluded and
once with GAIN included. SNPs that are associated with RA should give a significant p-value
when combined with the WTCCC RA dataset. If the SNP is having an opposite effect in each
disease (i.e. protective in RA and susceptible in SZ or vice versa), as hypothesised, the
addition of the GAIN SZ dataset should cause the combined p-value to become non
significant. If the disease pathway is common to both RA and SZ the p-value should remain
significant or increase in significance.
The Breslow-Day (B-D) test (Table 3.40) revealed that there was no evidence of genetic
heterogeneity between the combined sample sets for the three of the AKT1 SNPs:
AKT1_rs2494731 (without GAIN p = 0.182, with GAIN p = 0.092), AKT1_rs1130214
(without GAIN p = 0.588, with GAIN p = 0.604) and AKT1_rs4983386 (without GAIN p =
0.940, with GAIN p= 0.108). However, there were significant levels of genetic heterogeneity
in the SNPs AKT1_rs7146661 (without GAIN p = 0.001, with GAIN p = 0.00004), indicating
that the sample sets differed significantly in some respect (see section 2.5.1). In this thesis
SNPs with a significant heterogeneity score were run again using the random effects model
which allows for differences between the sample sets by conservatively altering the ORs and
allelic p values. After reanalysis AKT1_rs7146661 gave a significant between-study variance
when GAIN was included (p = 0.0404). When GAIN was excluded, the between-study
variance was not significant (p = 0.0522). The combined allelic p-values were significant for
two SNPs, AKT1_rs2494731 (p = 0.033) and AKT1_rs4983386 (p = 0.006) and trending
towards significance (p<0.07) for a third, AKT1_rs1130214 (p = 0.064) when GAIN was
excluded. AKT1_rs7146661 had a non-significant result (p = 0.873). When GAIN was
included in the analysis there were no significant values for any of the AKT1 SNPs.
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Chapter 3: Results
Table 3.41: Meta-analysis for the AKT1SNPs over all sample sets genotyped in this study and WTCCC.
Information is given for combined datasets including and excluding GAIN data. Allele count, minor allele
frequencies (MAF), odds ratio (95% CI, M-H), allelic p-value and the B-D p-value are given for
AKT1_rs2494731, AKT1_rs1130214, AKT1_rs4983386 and AKT1_rs7146661. AKT1_rs7146661 was analysed
under the random effects model. All other SNPs were analysed as normal.
rs2494731
SNP
GAIN
Samples
included
NO
Controls
Cases
YES
Controls
rs1130214
Cases
NO
Controls
Cases
YES
Controls
rs7146661
rs4983386
Cases
NO
Controls
Cases
YES
NO
YES
Allele count
11
1837
(0.450)
1510
(0.426)
2411
(0.447)
2009
(0.428)
2031
(0.497)
1705
(0.469)
2677
(0.496)
2274
(0.475)
1169
(0.323)
847 (0.290)
1564
Controls (0.317)
1199
Cases
(0.295)
1905
Controls (0.536)
1620
Cases
(0.570)
2652
Controls (0.545)
2238
Cases
(0.561)
12
1817
(0.445)
1622
(0.458)
2398
(0.445)
2167
(0.462)
1685
(0.412)
1608
(0.442)
2244
(0.416)
2097
(0.438)
1751
(0.484)
1452
(0.497)
2392
(0.486)
2042
(0.502)
1409
(0.396)
1047
(0.369)
1889
(0.388)
1495
(0.375)
22
MAF
427 (0.105) 0.327
Odds ratio
(95% CI)
1.077
P-value P-value
(allelic) (B-D)
0.033
0.182
0.152
0.092
0.064
0.588
0.105
0.604
0.006
0.940
0.087
0.108
0.873
0.0009
0.710
0.00004
411 (0.116) 0.345 (1.006-1.154)
582 (0.108) 0.330
1.044
517 (0.110) 0.341 (0.984-1.108)
374 (0.091) 0.297
1.069
326 (0.090) 0.311 (0.996-1.147)
479 (0.089) 0.296
1.052
420 (0.088) 0.307 (0.990-1.118)
696 (0.192) 0.435
1.104
620 (0.212) 0.461 (1.029-1.186)
970 (0.197) 0.440
1.054
828 (0.203) 0.454 (0.992-1.119)
242 (0.068) 0.266
1.024
174 (0.061) 0.246 (0.769-1.362)
325 (0.067) 0.261
1.043
258 (0.065) 0.252 (0.838-1.296)
For AKT1_rs2494731 and AKT1_rs1130214, combining the OXRA sample set with the
WTCCC genotyping data strengthened the protective effect for RA (see Figure 3.30 and
Figure 3.31). For AKT1_rs4983386 combining the NZRA sample set with the genotyping data
from WTCCC strengthened the protective effect for RA (see Figure 3.32). This effect was not
seen for any other sample sets over the SNPs (for AKT1_rs2494731, see Figure 3.30, for
AKT1_rs1130214, see Figure 3.31, for AKT1_rs4983386, see Figure 3.32 and for
AKT1_rs7146661, see Figure 3.33). For the three SNPs (AKT1_rs2494731, AKT1_rs4983386
and AKT1_rs7146661) where the genotyping data from GAIN and WTCCC convey opposite
135
Chapter 3: Results
effects (protection or susceptibility) toward RA, the combined sample sets including GAIN
decreased the protective effect of the combined sample set excluding
GAIN
(AKT1_rs2494731 see Figure 3.34, AKT1_rs4983386 see Figure 3.36 and AKT1_rs7146661
see Figure 3.37). The GAIN sample set had an OR of 1.00 in AKT1_rs1130214; this
decreased the susceptibility effect of the WTCCC in the combined sample set results (see
Figure 3.35).
Figure 3.30: Odds ratio meta-analysis plot for AKT1_rs2494731 in AKT1. The WTCCC and combined sample
sets show a susceptible effect for RA (OR > 1) while the M-H odds ratio for the NZ RA, OXRA and UKRA
sample sets are not significant.
Figure 3.31: Odds ratio meta-analysis plot for AKT1_rs1130214 in AKT1. The WTCCC sample set
shows a susceptible effect for RA (OR > 1) while the M-H odds ratio for the NZRA, OXRA, UKRA
and combined sample sets are not significant.
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Chapter 3: Results
Figure 3.32: Odds ratio meta-analysis plot for AKT1_rs4983386 near AKT1. The WTCCC and combined sample
sets show a susceptible effect for RA (OR > 1) while the M-H odds ratios for the NZRA and UKRA sample sets
are not significant.
Figure 3.33: Odds ratio meta-analysis plot for AKT1_rs7146661 near AKT1. The WTCCC and combined sample
sets show a susceptible effect for RA (OR > 1), the UKRA sample set conveys a protective effect for RA (OR <
1) and the M-H odds ratio for the NZRA sample set is not significant. This SNP was analysed under the random
effects model.
Figure 3.34: Odds ratio meta-analysis plot for AKT1_rs2494731 in AKT1. The WTCCC show a susceptible
effect for RA (OR > 1) while the M-H odds ratio for the NZ RA, OXRA, UKRA, GAIN and combined sample
sets are not significant.
137
Chapter 3: Results
Figure 3.35: Odds ratio meta-analysis plot for AKT1_rs1130214 in AKT1. The WTCCC is susceptible for RA
(OR > 1) while the M-H odds ratio for the NZ RA, OXRA, UKRA, GAIN and combined sample sets are not
significant.
Figure 3.36: Odds ratio meta-analysis plot for AKT1_rs4983386 near AKT1. The WTCCC show a susceptible
effect for RA (OR > 1) while the M-H odds ratio for the NZ RA, OXRA, UKRA, GAIN and combined sample
sets are not significant.
Figure 3.37: Odds ratio meta-analysis plot for AKT1_rs7146661 near AKT1. The WTCCC show a susceptible
effect for RA (OR > 1) while the UKRA sample set conveys a protective effect for RA (OR < 1). The M-H odds
ratio for the NZ RA, OXRA, UKRA, GAIN and combined sample sets are not significant. This SNP was
analysed under the random effects model.
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Chapter 3: Results
3.1.3.3. Haplotype Analysis
Haplotype analysis was performed in different ways depending on the dataset being analysed.
The WTCCC RA and GAIN SZ genotyping data (see Table 3.42) were analysed with PLINK
via BC|SNPmax. The NZRA and the combined genotyping sample set (NZRA, OXRA and
UKRA) were investigated using SHEsis (see Table 3.44). Two analysis programs were
needed as the genotype information was presented differently in the GWAS datasets and the
datasets genotyped in this thesis. Only SNPs in Linkage disequilibrium (LD>0.50) were
investigated in the haplotype analysis. In the WTCCC RA dataset (Table 3.42) the
AKT1_rs4983386 and AKT1_rs2494731 haplotypes 12 and 22 give significant p-values (12 =
0.028, 22 = 0.007). In NZRA (Table 3.44) none of the haplotypes gave significant p-values
(11 = 0.211, 22 = 0.568, and 21 = 0.286).
Conditional analysis of these SNPs in the combined WTCCC and RA genotyping dataset
(NZRA+ARA1+UKRA) provided evidence of two independent effects (p = 0.415).
Table 3.42: Haplotype analysis for the two AKT1 SNPs in LD, AKT1_rs2494731 and AKT1_rs4983386, shown
for the WTCCC sample set. Haplotypes are listed in the order of decreasing control frequency and only the
haplotypes with haplotype frequency greater than 3% are shown. Significant values are shown in bold.
Polymorphism
Haplotype Frequency
Haplotypes
rs4983386
(G/A)
rs2494731
(G/C)
Cases
Control
11
22
21
G
A
A
G
C
G
0.534
0.337
0.120
0.556
0.315
0.120
P-value
0.036
0.028
0.932
Table 3.43: Haplotype analysis for the two AKT1 SNPs in LD, AKT1_rs2494731 and AKT1_rs4983386 over the
GAIN SZ dataset. Haplotypes are listed in the order of decreasing control frequency and only the haplotypes
with haplotype frequency greater than 3% are shown. Significant values are shown in bold.
Polymorphism
Haplotype Frequency
Haplotypes
rs4983386
(G/A)
rs2494731
(G/C)
Cases
Control
11
22
21
G
A
A
G
C
G
0.555
0.320
0.117
0.538
0.329
0.123
139
P-value
0.254
0.699
0.536
Chapter 3: Results
Table 3.44: Haplotype analysis for the two AKT1SNPs in LD, AKT1_rs2494731 and AKT1_rs4983386, over the
combined genotyping sample set (NZRA, ARA and UKRA). Haplotypes are listed in the order of decreasing
control frequency and only the haplotypes with haplotype frequency greater than 3% are shown. Significant
values are shown in bold.
Polymorphism
Haplotype Frequency
Haplotypes
rs4983386
(G/A)
rs2494731
(G/C)
Cases
Control
11
22
21
G
A
A
G
C
G
0.514
0.317
0.150
0.547
0.326
0.110
P-value
0.073
0.601
0.0009
95%
Odds Ratio Confidence
Intervals
0.879
0.961
1.430
0.763-1.012
0.827-1.116
1.156-1.770
3.1.3.4. Effect in Psychosis
There were no significant p-values (see Tabel 3.45) in any of the mental illness datasets for
any of the AKT1 SNPs, AKT1_rs7146661, AKT1_rs4983386, AKT1_rs2494731 or
AKT1_rs1130214. The combined psychosis dataset also had non-significant p-values for all
SNPs.
For the SNPs AKT1_rs2494731 and AKT1_rs4983386, the psychosis dataset, the OR
conveyed an opposite effect to the OR of the combined RA dataset. For the remaining two
SNPs, AKT1_rs1130214 and AKT1_rs7146661, the ORs for both the psychosis and combined
RA datasets convey a similar effect (susceptibility). Figures 3.41 to 3.44 show a graphical
representation of the alignment of the mental illness datasets.
140
Chapter 3: Results
Table 3.45: Analysis for AKT1 SNPs, AKT1_rs2494731, AKT1_rs1130214, AKT1_rs4983386 and
AKT1_rs7146661, in three mental illness datasets, GAIN schizophrenia, non-GAIN schizophrenia and GAIN
bipolar disorder. The allelic p-value, odds ratios and 95% confidence intervals are shown for each and for the
combined psychosis dataset (sum of the three datasets above). Information for the combined RA dataset (NZRA
and WTCCC) is shown for comparison.
rs2494731
rs1130214
rs7146661
rs4983386
OR
OR
OR
OR
p-allelic
p-allelic
p-allelic
p-allelic
(95% CIs)
(95% CIs)
(95% CIs)
(95% CIs)
0.952
0.996
1.118
0.936
0.417
0.980
0.088
0.248
GAIN SZ
(0.845-1.072)
(0.880-1.126)
(0.983-1.271)
(0.836-1.048)
1.018
1.066
0.98
1.009
non-GAIN
0.762
0.305
0.760
0.882
SZ
(0.905-1.147)
(0.943-1.205)
(0.863-1.113)
(0.902-1.128)
0.988
1.031
1.077
0.981
0.869
0.701
0.364
0.784
GAIN BD
(0.851-1.146)
(0.883-1.203)
(0.917-1.265)
(0.852-1.128)
0.971
1.018
1.085
0.958
0.579
0.734
0.153
0.392
Psychosis
(0.875-1.078)
(0.915-1.133)
(0.970-1.213)
(0.869-1.057)
Combined
RA
0.033
1.077
(0.006-1.154)
0.064
1.069
(0.996-1.147)
0.873
1.024
(0.769-1.362)
0.006
1.104
(1.029-1.186)
Figure 3.38: Odds ratio meta-analysis plot for AKT1_rs2494731 over the mental illness datasets. Non-GAIN SZ
and GAIN BD show a susceptibility effect (OR > 1) towards SZ and BD respectively, the GAIN SZ sample set
conveys a protective effect (OR < 1) for SZ, the combined psychosis sample set is susceptible towards mental
illness.
141
Chapter 3: Results
Figure 3.39: Odds ratio meta-analysis plot for AKT1_rs1130214 over the mental illness datasets. Non-GAIN SZ
and GAIN BD show a susceptibility effect (OR > 1) towards SZ and BD respectively, the GAINSZ sample set
conveys a protective effect (OR < 1) for SZ, the combined psychosis sample set is susceptible towards mental
illness.
Figure 3.40: Odds ratio meta-analysis plot for AKT1_rs7146661 over the mental illness datasets. GAIN SZ and
GAIN BD show a susceptible effect (OR > 1) towards SZ and BD respectively, the non-GAINSZ sample set
conveys a protective effect for SZ (OR < 1), the combined psychosis sample set is susceptible towards mental
illness.
Figure 3.41: Odds ratio meta-analysis plot for AKT1_rs493386 over the mental illness datasets. GAIN SZ and
GAIN BD show a protective effect (OR < 1) towards SZ and BD respectively, the non-GAINSZ sample set
conveys a susceptibility effect (OR > 1) for SZ, the combined psychosis sample set is protective towards mental
illness.
142
Chapter 3: Results
3.1.3.5. Sex Differences
All SNPs were investigated to assess whether association to RA or SZ was sex specific. The
SNPs were first analysed to determine if there was a significant difference between male and
female cases for each dataset. Datasets were analysed separately and the allelic and genotypic
p-values for each SNP are displayed in Table 3.46. There were no significant differences
between males and females in the WTCCC RA, GAIN SZ, non-GAIN SZ or GAIN bipolar
datasets (see Table 3.46) for any of the AKT1 SNPs. In the NZRA dataset there was a
significant difference between the sexes for AKT1_rs7146661 (p-allelic = 0.015, p-genotypic
= 0.043) but not for the other three SNPs (AKT1_rs4983386, AKT1_rs2494731 and
AKT1_rs1130214).
Table 3.46: Analysis of males versus females for the four AKT1 SNPs, AKT1_rs7146661, AKT1_rs4983386,
AKT1_rs2494731 and AKT1_rs1130214. This analysis was completed over five different datasets, NZRA,
WTCCC RA, GAIN SZ, non-GAIN SZ and GAIN bipolar. Significant values are presented in bold text.
RS7146661
RS4983386
RS2494731
RS1130214
NZRA
WTCCC RA
GAIN SZ
Non- GAIN SZ
GAIN Bipolar
p-value p-value p-value p-value p-value p-value p-value p value p-value p-value
allelic genotypic allelic genotypic allelic genotypic allelic genotypic allelic genotypic
0.896
0.849
0.773
0.941
0.139
0.247
0.453
0.717
0.015
0.043
0.216
0.459
0.423
0.563
0.228
0.067
0.601
0.850
0.620
0.858
0.786
0.905
0.183
0.423
0.801
0.900
0.666
0.810
0.214
0.467
0.354
0.599
0.283
0.299
0.182
0.362
0.704
0.914
0.318
0.454
The SNPs with a significant difference between the sexes in the cases were then case-control
analysed for male and female subjects separately. For AKT1 the only SNP to be assessed at
this level was AKT1_rs7146661 (see Table 3.47). This SNP was close to trending towards
significance in the NZRA females only (p = 0.082) and the WTCCC RA males only (p =
0.080) subsets. The OR for females only in NZRA conveyed a protective effect for RA (OR =
0.747). The WTCCC males only subset conveyed a protective effect for RA (OR = 0.862).
The GAIN and psychosis subsets were all non-significant.
143
Chapter 3: Results
Table 3.47: AKT1_rs7146661 association results over NZRA, WTCCC RA, GAIN SZ and psychosis for female
cases versus controls and males cases versus controls. The males and females cases and controls group is shown
for comparison. The allelic p-value, odds ratios and 95% confidence intervals are shown for each dataset. The
significant values are shown in bold text.
NZRA
WTCCC
GAIN SZ
Psychosis
Females only
cases vs. controls
OR
p-allelic
(95% CIs)
0.747
0.082
(0.383-1.029)
0.938
0.449
(0.796-1.106)
1.064
0.553
(0.867-1.306)
1.004
0.949
(0.883-1.142)
Male only
cases vs. controls
OR
p-allelic
(95% CIs)
1.058
0.709
(0.714-1.310)
0.862
0.0798
(0.731-1.018)
1.143
0.122
(0.965-1.353)
1.060
0.307
(0.948-1.187)
144
Males and Females
cases vs. controls
OR
p-allelic
(95% CIs)
0.907
0.720
(0.753-1.092)
0.838
0.0003
(0.761-0.922)
1.118
0.088
(0.983-1.271)
1.035
0.419
(0.952-1.124)
Chapter 3: Results
3.1.4.
SNPS WITHIN NFAT
WTCCC. Association analysis for NFATC1_rs2002311 (p = 0.727) and NFATC2_rs8119787
(p =0.314) showed no evidence for association with RA in the NZ sample set (Table 3.48).
NFATC2_rs8119787 had very consistent OR scores between all three datasets (NZRA =
0.922, WTCCC = 0.923, GAIN = 0.917). Stahl GWAS data were significant for
NFATC1_rs2002311 (p = 0.047) but not for NFATC2_rs8119787 (p =0.508).
Table 3.48: Case-control analysis for the SNPs NFATC1_rs2002311 and NFATC2_rs8119787. Genotypes,
minor allele frequencies (MAF), odds ratio (95% CI) and the allelic p-value, for the SNP are from case-control
analysis for the NZRA (New Zealand Rheumatoid Arthritis), WTCCC and GAIN sample sets.
rs8119787
rs2002311
SNP
Sample
set
Major/
Minor Samples
Controls
A/G
NZRA
Cases
Controls
A/G
WTCCC
Cases
Controls
T/C
GAIN
Cases
Controls
A/G
Stahl
Cases
Controls
A/G
NZRA
Cases
Controls
A/G
WTCCC
Cases
Controls
T/C
GAIN
Cases
Controls
A/G
Stahl
Cases
11
202 (0.403)
339 (0.410)
1373 (0.477)
830 (0.457)
654 (0.475)
544 (0.467)
9539 (0.473)
2578 (0.465)
133 (0.264)
241 (0.298)
739 (0.253)
480 (0.260)
338 (0.245)
314 (0.268)
5102 (0.254)
1426 (0.258)
12
234 (0.467)
364 (0.440)
1253 (0.435)
800 (0.440)
579 (0.420)
478 (0.411)
8681 (0.430)
2384 (0.430)
260 (0.516)
396 (0.489)
1411 (0.482)
937 (0.508)
679 (0.493)
575 (0.491)
10019 (0.498)
2742 (0.496)
22
65 (0.130)
124 (0.150)
253 (0.088)
188 (0.103)
144 (0.105)
142 (0.122)
1946 (0.096)
577 (0.104)
111 (0.220)
173 (0.214)
775 (0.265)
429 (0.232)
361 (0.262)
283 (0.241)
4993 (0.248)
1356 (0.245)
MAF
0.363
0.37
0.305
0.323
0.315
0.327
0.312
0.319
0.478
0.458
0.506
0.486
0.508
0.487
0.497
0.494
Odds ratio
(95% CI)
1.029
(0.8747-1.212)
1.087
(0.9940-1.161)
1.059
(0.941-1.192)
1.036
(0.990-1.080)
0.922
(0.788-1.080)
0.923
(0.850-1.003)
0.917
(0.822-1.024)
0.986
(0.945-1.028)
p-value
(allelic)
0.727
0.068
0.341
0.047
0.314
0.057
0.124
0.508
3.1.4.2. Meta-analysis
STATA software was used to perform meta-analysis to combine the genotyping for all sample
sets. These tests were undertaken to obtain the combined Mantel-Haenszel (M-H) odds ratios
(ORs), to calculate the allelic and Breslow-Day (B-D, test for heterogeneity) p-values and to
generate the odds-ratio meta-analysis plot.
145
Chapter 3: Results
Table 3.49: Meta-analysis for the NFAT SNPs over NZRA, WTCCC. Information is given for combined
datasets including and excluding GAIN data. Allele count, minor allele frequencies (MAF), odds ratio (95% CI,
M-H), allelic p-value and the B-D p-value are given for NFATC1_rs2002311 and NFATC2_rs8119787.
GAIN
Samples
included
rs2002311
SNP
NO
Controls
Cases
YES
Controls
rs8119787
Cases
NO
Controls
Cases
YES
Controls
Cases
Allele count
11
1575
(0.466)
1169
(0.442)
2229
(0.469)
1713
(0.450)
872
(0.254)
721
(0.271)
1210
(0.252)
1035
(0.270)
12
1487
(0.440)
1164
(0.440)
2066
(0.434)
1642
(0.431)
1671
(0.487)
1333
(0.502)
2350
(0.489)
1908
(0.502)
22
318
(0.094)
312
(0.118)
462
(0.097)
454
(0.119)
886
(0.258)
602
(0.227)
1247
(0.259)
885
(0.227)
MAF
Odds ratio
(95% CI)
0.338
1.073
0.338
(0.992-1.161)
0.314
1.069
0.335
(1.01-1.141)
0.502
0.923
0.478
(0.858-0.993)
0.504
0.921
0.480
(0.867-0.979)
P-value
(allelic)
P-value
(B-D)
0.077
0.567
0.046
0.835
0.032
0.991
0.008
0.996
The Breslow-Day (B-D) test revealed that there was no evidence of genetic heterogeneity
between the combined sample sets for either rs2002311 (without GAIN p = 0.567, with GAIN
p = 0.835) or rs8119787 (without GAIN p = 0.991, with GAIN p = 0.996). The combined
allelic p-values were significant for rs8119787 (p = 0.032) but not rs2002311 (p = 0.077)
when GAIN was excluded and significant for both rs8119787 (p = 0.008) and rs2002311 (p =
0.046) when GAIN was included (see Table 3.49).
The combined odds ratios without GAIN for rs2002311 (OR = 1.073[0.99-1.16]), conveyed a
susceptibility effect for RA, whereas for rs8119787 (OR = 0.92[0.86-0.99]) it conveyed a
protective effect. For rs2002311, combining the NZRA sample set with the WTCCC
genotyping data slightly weakened the RA susceptibility effect seen for WTCCC RA (see
Figure 3.42). In rs8119787, the combined dataset had a slightly weaker protective p-value (p
= 0.032) than the WTCCC alone (see Figure 3.43). When GAIN was added to the combined
sample set both SNPs, rs2002311 (OR = 1.069[1.01-1.14]) and rs8119787 (OR = 0.921[0.870.98]), conveyed the same effect, although this was slightly stronger (rs2002311 p = 0.046,
rs8119787 p = 0.008) than in the combined datasets excluding GAIN (Figure 3.44 and Figure
3.45).
146
Chapter 3: Results
Figure 3.42: Odds ratio meta-analysis plot for rs2002311 in NFATC1. The M-H odds ratio for NZRA, WTCCC
and the combined sample set convey susceptibility.
and the combined sample set convey a protective effect.
Figure 3.44: Odds ratio meta-analysis plot for rs2002311 in NFATC1.The M-H odds ratio for NZRA, WTCCC
and the combined sample set convey susceptibility.
147
Chapter 3: Results
and the combined sample set convey a protective effect.
Only SNPs in Linkage disequilibrium (LD>0.50) had the haplotypes investigated. Therefore
rs2002311 and rs8119787 were not analysed in this way.
148
Chapter 3: Results
There were no significant p-values in any of the mental illness datasets for either
NFATC1_rs2002311 (GAIN SZ p = 0.341, non-GAIN p= 0.480, GAIN BD p= 0.828) or
NFATC2_rs8119787 (GAIN SZ p = 0.124, non-GAIN p= 0.537, GAIN BD p= 0.993) (see
Table 3.50). The combined psychosis dataset also had non-significant p-values for both SNPs
(NFATC1_rs2002311 p= 0.640 and NFATC2_rs8119787 p = 0.281). The psychosis dataset
for NFATC1_rs2002311 (see Figure 3.46) had a susceptible OR (OR = 1.059). This was
aligned with a susceptibility OR for the combined RA group (OR = 1.073). The psychosis
dataset for NFATC1_rs8119787 had a susceptible OR (OR = 1.055, see Figure 3.47), in
contrast to a protective OR in the combined RA dataset (OR = 0.923).
Table 3.50: Association analysis for NFAT SNPs, NFATC1_rs2002311 and NFATC2_rs8119787, in three
mental illness datasets, GAIN schizophrenia, non-GAIN schizophrenia and GAIN bipolar disorder. The allelic pvalue, odds ratios and 95% confidence intervals are shown for each and for the combined psychosis dataset (sum
of the three datasets above). Information for the combined RA dataset (NZRA and WTCCC) is shown for
comparison.
rs2002311
p-allelic
GAIN SZ
0.341
non-GAIN SZ
0.480
GAIN BD
0.828
Psychosis
0.640
Combined RA
0.077
rs8119787
OR
(95% CIs)
1.059
(0.941-1.192)
0.958
(0.849-1.080)
1.017
(0.875-1.182)
1.025
(0.924-1.138)
1.073
(0.992-1.161)
149
p-allelic
0.124
0.537
0.993
0.281
0.032
OR
(95% CIs)
0.917
(0.822-1.024)
0.966
(0.864-1.079)
0.999
(0.869-1.149)
1.055
(0.957-1.162)
0.923
(0.858-0.993)
Chapter 3: Results
Figure 3.46: Odds ratio meta-analysis plot for NFATC1_rs2002311. The GAIN SZ and GAIN BD sample sets
show a susceptibility effect (OR > 1) towards SZ and BD respectively, the non-GAINSZ sample set conveys a
protective effect (OR < 1) for SZ and the combined psychosis sample set is susceptible towards mental illness.
Figure 3.47: Odds ratio meta-analysis plot for NFATC2_rs8119787. The GAIN SZ and non-GAIN SZ sample
sets show a protective effect (OR < 1) towards SZ, the GAIN BD sample set is neither protective nor susceptible
(OR = 1) for BD and the combined psychosis sample set is susceptible (OR > 1) towards mental illness.
The initial analysis of sex variation in NFAT indicated that there was a significant difference
between males and females for NFATC1_rs2002311 in WTCCC RA (p-allelic = 0.017) and in
GAIN SZ (p-allelic = 0.010). This SNP was not significant for any other datasets in this
analysis. However, it was trending towards significance for GAIN bipolar (p-allelic = 0.062).
NFATC2_rs8119787 had no significant differences between males and females for any of the
datasets (see Table 3.51).
150
Chapter 3: Results
Table 3.51: Analysis of males versus females for NFATC1_rs2002311 and NFATC2_rs8119787. This analysis
was completed over five different datasets, NZRA, WTCCC RA, GAIN SZ, non-GAIN SZ and GAIN bipolar.
Significant values are presented in bold text.
RS2002311
0.204
0.402
0.017
0.048
0.010
0.038
0.181
0.278
0.062
0.146
RS8119787
NZRA
WTCCC RA
GAIN SZ
Non- GAIN SZ
GAIN Bipolar
0.863
0.525
0.553
0.305
0.880
0.251
0.792
0.865
0.189
0.076
NFATC1_rs2002311 was investigated further with separate analyses for male cases and
controls and female cases and controls. The NZRA females only (p = 0.339) and males only
(p = 0.839) subsets were not significant for this SNP. The WTCCC RA dataset reached
significance when the sexes were analysed separately (females p = 0.028, males p = 0.002).
The OR for WTCCC females only is susceptible (OR = 1.195) and protective for WTCCC
males only (0.779). The GAIN SZ dataset was non-significant over all subsets (females p =
0.186, males p = 0.095, both sexes p = 0.341). The psychosis dataset was significant for
females only (p = 0.034) with a protective OR (OR = 0.876). Males only (p = 0.238) and both
sexes together (p = 0.971) were non-significant for the psychosis data set.
Table 3.52: NFATC1_rs2002311 association results over NZRA, WTCCC RA, GAIN SZ and Psychosis for
female cases versus controls and males cases versus controls. The males and females cases and controls group is
shown for reference. The allelic p-value, odds ratios and 95% confidence intervals are shown for each dataset.
The significant values are shown in bold text.
NZRA
WTCCC
GAIN SZ
Psychosis
Females only
cases vs. controls
OR
p-allelic
(95% CIs)
1.097
0.339
(0.887-1.266)
1.195
0.028
(1.02-1.4)
0.876
0.186
(0.720-1.066)
0.876
0.034
(0.775-0.99)
Male only
cases vs. controls
OR
p-allelic
(95% CIs)
1.028
0.839
(0.788-1.341)
0.779
(0.663-0.915)
0.002
1.143
0.095
(0.977-1.336)
1.065
0.238
(0.959-1.183)
151
Males and Females
cases vs. controls
OR
p-allelic
(95% CIs)
1.029
0.727
(0.875-1.212)
1.087
0.068
(0.994-1.161)
1.059
0.341
(0.941-1.192)
0.999
0.971
(0.924-1.080)
Chapter 3: Results
3.1.5.
SNPS WITHIN DISC1
WTCCC. Association analysis for DISC1_rs9431714 (p = 0.702), DISC1_rs872625 (p
=0.181), DISC1_rs4658966 (p =0.714), DISC1_rs821577 (p =0.244) and DISC1_rs872624 (p
=0.676) showed no evidence for association with RA in the NZ sample set (Table 3.53).
Analysis over the Oxford sample set also found no association of the SNPs DISC1_rs9431714
(p = 0.252) and DISC1_rs872625 (p = 0.427) with RA (Table 3.53). No association was found
with RA in the UK sample set for DISC1_rs9431714 (p = 0.08, OR = 0.777[0.93-1.32]) and
DISC1_rs872625 (p = 0.252, OR = 1.107[0.93-1.32],Table 3.53). The Stahl RA GWAS
results are shown for comparison. None of the 5 DISC1 SNPs analysed in this thesis reached
significance in this GWAS.
152
Chapter 3: Results
Table 3.53: Case-control analysis for the DISC1 SNPs, DISC1_rs9431714, DISC1_rs872625, DISC1_rs4658966,
DISC1_rs821577 and DISC1_rs872624. Genotypes, minor allele frequencies (MAF), odds ratio (95% CI) and the
allelic p-value, for the SNP are from case-control analysis for the NZRA, OXRA (where applicable), UKRA (where
applicable), WTCCC and GAIN sample sets. Values from the Stahl RA GWAS are included for comparison.
Sample
set
NZRA
rs9431714
OXRA
UKRA
WTCCC
GAIN
Stahl
NZRA
rs872625
OXRA
UKRA
WTCCC
GAIN
Stahl
rs4658966
NZRA
WTCCC
GAIN
Stahl
rs821577
NZRA
WTCCC
GAIN
Stahl
rs872624
NZRA
WTCCC
GAIN
Stahl
Major/
Minor Samples
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
A/G
Cases
Controls
A/G
Cases
Controls
A/G
Cases
Controls
A/G
Cases
Controls
A/G
Cases
Controls
A/G
Cases
Controls
T/C
Cases
Controls
T/C
Cases
Controls
T/C
Cases
Controls
T/C
Cases
Controls
T/G
Cases
Controls
T/G
Cases
Controls
T/G
Cases
Controls
T/G
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
Controls
G/A
Cases
11
185 (0.385)
316 (0.397)
186 (0.408)
256 (0.369)
52 (0.364)
120 (0.401)
967 (0.363)
662 (0.386)
539 (0.392)
466 (0.399)
7787 (0.386)
2201 (0.398)
256 (0.577)
497 (0.619)
326 (0.649)
426 (0.616)
89 (0.553)
170 (0.543)
175 (0.129)
1169 (0.630)
825 (0.599)
712 (0.608)
12184 (0.604)
3398 (0.614)
334 (0.711)
573 (0.720)
1708 (0.760)
1187 (0.815)
981 (0.714)
829 (0.712)
14128(0.701)
3935(0.711)
160 (0.318)
301 (0.371)
680 (0.336)
480 (0.368)
420 (0.311)
366 (0.316)
6312 (0.313)
1826 (0.330)
365 (0.726)
618 (0.735)
1568 (0.775)
1061 (0.813)
986 (0.729)
838 (0.723)
14612 (0.725)
4047 (0.731)
12
232 (0.483)
353 (0.444)
212 (0.465)
344 (0.496)
58 (0.406)
135 (0.452)
1275 (0.478)
820 (0.478)
619 (0.451)
553 (0.473)
9523 (0.472)
2575 (0.465)
168 (0.378)
273 (0.340)
150 (0.299)
235 (0.340)
64 (0.398)
121 (0.387)
1045 (0.771)
613 (0.330)
484 (0.351)
396 (0.338)
6999 (0.347)
1893 (0.342)
124 (0.264)
204 (0.256)
495 (0.220)
247 (0.170)
365 (0.266)
301 (0.258)
5506(0.273)
1480(0.267)
264 (0.525)
378 (0.466)
996 (0.493)
627 (0.480)
664 (0.491)
560 (0.483)
10005 (0.496)
2692 (0.486)
129 (0.256)
210 (0.250)
424 (0.210)
228 (0.175)
338 (0.250)
288 (0.248)
5139 (0.255)
1391 (0.251)
153
22
63 (0.131)
126 (0.158)
58 (0.127)
94 (0.135)
33 (0.231)
44 (0.147)
423 (0.159)
233 (0.136)
216 (0.157)
150 (0.128)
2851 (0.141)
760 (0.137)
20 (0.045)
33 (0.041)
26 (0.052)
31 (0.045)
8 (0.050)
22 (0.070)
135 (0.100)
75 (0.040)
69 (0.050)
63 (0.054)
978 (0.049)
246 (0.044)
12 (0.026)
19 (0.024)
45 (0.020)
23 (0.016)
28 (0.020)
35 (0.030)
531(0.026)
124(0.022)
79 (0.157)
133 (0.164)
346 (0.171)
198 (0.152)
268 (0.198)
233 (0.201)
3840 (0.190)
1019 (0.184)
9 (0.018)
13 (0.015)
30 (0.015)
16 (0.012)
28 (0.021)
33 (0.028)
415 (0.021)
100 (0.018)
MAF
0.373
0.381
0.36
0.383
0.434
0.373
0.398
0.375
0.382
0.365
0.378
0.370
0.234
0.211
0.201
0.215
0.248
0.264
0.224
0.205
0.226
0.223
0.222
0.215
0.157
0.152
0.130
0.101
0.153
0.159
0.163
0.156
0.419
0.397
0.417
0.392
0.444
0.443
0.439
0.427
0.146
0.140
0.120
0.100
0.146
0.153
0.148
0.144
Odds ratio p-value
(95% CI)
(allelic)
1.033
(0.875-1.219) 0.702
1.107
(0.931-1.316) 0.252
0.7769
(0.583-1.035) 0.084
0.9075
(0.831-0.991) 0.031
0.9275
(0.828-1.040) 0.1957
0.968
(0.926-1.010) 0.110
0.875
(0.719-1.064) 0.181
1.085
(0.886-1.326) 0.427
1.083
(0.795-1.475) 0.614
0.896
(0.810-0.991) 0.032
0.984
(0.862-1.123) 0.811
0.961
(0.914-1.010) 0.041
0.959
(0.768-1.198) 0.714
0.747
0.0001
2
(0.644-0.867)
1.047
(0.805-0.973) 0.555
0.950
(0.897-1.006) 0.331
0.909
(0.775-1.067) 0.244
0.9
(0.814-0.995) 0.023
0.995
(0.890-1.11)
0.874
0.954
(0.914-0.995) 0.194
0.941
(0.754-1.175) 0.676
0.814
(0.694-0.954) 0.011
1.057
(0.904-1.235) 0.487
0.966
(0.910-1.025) 0.200
Chapter 3: Results
3.1.5.1.
Meta-analysis
STATA software was used to perform meta-analysis to combine the genotyping for all sample
sets on the five DISC1 SNPs. These tests were undertaken to obtain the combined MantelHaenszel (M-H) odds ratios (ORs), to calculate the allelic and Breslow-Day (B-D, test for
heterogeneity) p-values and to generate the odds-ratio meta-analysis plot.
Table 3.54: Meta-analysis for the DISC1 SNPs over the sample sets genotyped in this study and WTCCC.
Information is giving for combined datasets including and excluding GAIN data. Allele count, minor allele
DISC1_rs9431714, DISC1_rs872625, DISC1_rs4658966, DISC1_rs821577 and DISC1_rs872624.
rs872624
rs821577
rs4658966
rs872625
rs9431714
SNP
GAIN
Samples
included
NO
YES
NO
YES
NO
YES
NO
YES
NO
YES
Allele count
11
12
22
MAF
Odds ratio P-value P-value
(95% CI) (allelic) (B-D)
Controls 1390 (0.371) 1777 (0.475) 577 (0.154)
0.391
0.949
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
0.378
0.389
0.374
0.223
0.213
0.224
0.216
0.135
0.119
0.141
(0.886-1.017)
0.943
(0.889-1.001)
0.931
(0.860-1.007)
0.945
(0.883-1.011)
0.806
(0.713-0.911)
0.893
1354 (0.387)
1929 (0.377)
1820 (0.390)
846 (0.344)
2262 (0.617)
1671 (0.435)
2974 (0.615)
2042 (0.751)
1760 (0.781)
3023 (0.739)
1652 (0.472)
2396 (0.468)
2205 (0.472)
1427 (0.580)
1242 (0.339)
1911 (0.498)
1638 (0.339)
619 (0.228)
451 (0.200)
984 (0.240)
497 (0.142)
793 (0.155)
647 (0.138)
189 (0.077)
161 (0.044)
258 (0.067)
224 (0.046)
57 (0.021)
42 (0.019)
85 (0.021)
Cases
2589 (0.757)
Controls 840 (0.333)
Cases
781 (0.369)
Controls 1260 (0.325)
752 (0.220)
1260 (0.499)
1005 (0.475)
1924 (0.496)
77 (0.023)
425 (0.168)
331 (0.156)
693 (0.179)
Cases
Controls
Cases
Controls
1147 (0.350)
1933 (0.766)
1679 (0.782)
2919 (0.753)
1565 (0.478)
553 (0.219)
438 (0.204)
891 (0.230)
564 (0.172)
39 (0.015)
29 (0.014)
67 (0.017)
Cases
2517 (0.762) 726 (0.220)
62 (0.019)
0.138
0.076
0.053
0.136
0.075
0.252
0.099
0.332
0.001
0.068
0.020
0.133 (0.812-0.983)
0.394
0.902
0.018
0.394 (0.829-0.983)
0.427
0.935
0.053
0.411 (0.874-1.001)
0.006
0.125
0.854
0.017
0.116 (0.751-0.972)
0.132
0.931
0.160
0.129 (0.843-1.029)
0.911
0.390
0.298
0.071
between the combined sample sets for three of the SNPs: DISC1_rs9431714 (without GAIN p
= 0.076, with GAIN p = 0.136), DISC1_rs872625 (without GAIN p = 0.252, with GAIN p =
0.332) and DISC1_rs821577 (without GAIN p = 0.911, with GAIN p = 0.390). However,
there were low levels of genetic heterogeneity in the SNPs DISC1_rs4658966 (without GAIN
154
Chapter 3: Results
p = 0.068, with GAIN p = 0.006) and DISC1_rs872624 (without GAIN p = 0.017, with GAIN
p = 0.071). These SNPs were analysed again under the random effect model of meta-analysis
and these results are reported in this section. The combined allelic p-values were significant
for the three SNPs, DISC1_rs4658966 (p = 0.001), DISC1_rs321577 (p = 0.018) and
DISC1_rs872624 (p = 0.017), when GAIN was excluded and significant for one SNP,
DISC1_rs4658966 (p = 0.020) when GAIN was included (see Table 3.54).
Two SNPs, DISC1_rs9431714 and DISC1_rs872625 were trending (p<0.07) towards
significance, DISC1_rs9431714 with GAIN included (p = 0.053, OR = 0.943[0.89-1.00]) was
trending towards significance and DISC1_rs872625 with GAIN excluded (p = 0.075, OR =
0.931[0.86-1.01]) was close to trending. For DISC1_rs9431714, combining the UKRA
sample set with the WTCCC genotyping data strengthened the protective effect for RA (see
Figure 3.48). For DISC1_rs872625 combining the NZRA sample set with the genotyping data
from WTCCC strengthened the protective effect for RA (see Figure 3.49). This effect was not
seen in any of the other SNPs (for rs4659866 see Figure 3.50, for DISC1_rs821577 see Figure
3.51 and for DISC1_rs872624 see Figure 3.52). For the two SNPs (DISC1_rs4658966 and
DISC1_rs872624) where the genotyping data from GAIN and WTCCC convey opposite
effects (protection or susceptibility) toward RA, the combined sample sets including GAIN
decreased the protective effect of the combined sample set excluding
GAIN
(DISC1_rs4658966 see Figure 3.55, DISC1_rs872624 see Figure 3.57)
Figure 3.48: Odds ratio meta-analysis plot for DISC1_rs9431714 in DISC1. The WTCCC, UKRA and
combined sample set show an effect protective for RA (OR < 1) while the NZRA and OXRA sample sets are
susceptible.
155
Chapter 3: Results
Figure 3.49: Odds ratio meta-analysis plot for DISC1_rs872625 in DISC1. The WTCCC, NZRA and combined
sample set show an effect protective for RA (OR < 1) while the OXRA and UKRA sample sets are susceptible.
sample set show an effect protective for RA (OR < 1).
sample sets show an effect protective for RA (OR < 1).
156
Chapter 3: Results
Figure 3.52: Odds ratio meta-analysis plot for DISC1_rs872624 in DISC1. The NZRA, WTCCC and combined
sample sets show an effect protective for RA (OR < 1).
Figure 3.53: Odds ratio meta-analysis plot for DISC1_rs9431714 in DISC1. The UKRA and WTCCC datasets
show an effect protective for RA (OR < 1) while the NZRA and OXRA sample sets are susceptible for RA.
GAIN is protective for SZ.
Figure 3.54: Odds ratio meta-analysis plot for DISC1_rs872625 in DISC1. The NZRA and WTCCC datasets
show an effect protective for RA (OR < 1) while the OXRA and UKRA sample sets are susceptible for RA.
GAIN is protective for SZ.
157
Chapter 3: Results
show an effect protective for RA (OR < 1). GAIN conveys an effect of susceptibility for SZ.
158
Chapter 3: Results
Haplotype analysis was performed on the WTCCC (see Table 3.55) and GAIN (see Table
3.56) genotyping data through BC|SNPmax. The haplotypes for NZRA were investigated
using SHEsis (Table 3.57). Only SNPs in Linkage disequilibrium (LD>0.50) were
investigated
in
the
haplotype
analysis
(DISC1_rs872625,
DISC1_rs4658966,
and
DISC1_rs872624). There were six significant haplotypes in WTCCC (see Table 3.55), two of
these included all three SNPs (111 p = 0.0004, 222 p = 0.0022). There were no significant
haplotypes in GAIN (see Table 3.56). In the NZRA sample set, there were three significant
haplotypes (see Table 3.57). Two of these included all three SNPs (111 p = 0.013, 211 p =
0.002) and one included DISC1_rs872625 and DISC1_rs4658966 (21 p = 0.005).
Table 3.55: The Haplotype analysis for three DISC1 SNPs, DISC1_rs872625, DISC1_rs4658966, and
DISC1_rs872624 are shown for the WTCCC RA sample set. Haplotypes are listed in the order of decreasing
values are shown in bold. Some three SNP haplotypes have a higher haplotype frequency than the related two
SNP haplotypes. This may be due to a large number of samples being excluded from the three SNP haplotype
analyses and not the two SNP analyses due to missing genotypes.
Haplotypes
rs872625
(A/G)
Polymorphism
rs4658966
(T/C)
Haplotype Frequency
rs872624
(G/A)
Cases
Control
P-value
11
A
G
0.831
0.805
0.0076
111*
A
T
G
0.829
0.794
0.0004
11
A
T
0.818
0.787
0.0015
22
G
A
0.100
0.120
0.0106
222*
G
C
A
0.089
0.113
0.0022
22
G
C
0.088
0.112
0.0019
*Some three SNP haplotypes have a higher haplotype frequency than the related two SNP haplotypes. This may
be due to a large number of samples being excluded from the three SNP haplotype analyses and not the two SNP
analyses due to missing genotypes.
159
Chapter 3: Results
DISC1_rs872624, are shown for the GAIN SZ sample set. Haplotypes are listed in the order of decreasing
values are shown in bold.
rs872625
Polymorphism
rs4658966
rs872624
(T/C)
(A/G)
(C/T)
11
11
T
A
-
111*
T
11
22
Haplotypes
Haplotype Frequency
P-value
Cases
Control
C
C
0.842
0.778
0.847
0.776
0.497
0.869
A
C
T
C
A
-
T
0.775
0.766
0.150
0.771
0.762
0.145
0.687
0.745
0.497
222
C
G
T
22
22
C
-
G
G
T
0.143
0.148
0.143
0.139
0.142
0.139
0.600
0.503
0.590
211
C
A
C
0.070
0.078
0.175
21
C
A
0.075
0.085
0.209
21
C
C
0.073
0.079
0.240
*Some three SNP haplotypes have a higher haplotype frequency than the related two SNP haplotypes. This may
be due to a large number of samples being excluded from the three SNP haplotype analyses and not the two SNP
analyses due to missing genotypes.
DISC1_rs872624 are shown for the NZRA sample set. Haplotypes are listed in the order of decreasing control
frequency and only the haplotypes with haplotype frequency greater than 3% are shown. Significant values are
shown in bold.
Haplotypes
11
Polymorphism
rs872625 rs4658966 rs872624
(A/G)
(T/C)
(G/A)
T
G
Haplotype Frequency
Cases
Control
P-value Odds ratio (95% CI)
0.834
0.831
0.906
1.01 (0.82-1.26)
G
G
0.784
0.777
0.757
0.735
0.138
0.013
1.16 (0.95 – 1.41)
1.30 (1.06-1.61)
T
-
0.787
0.737
0.050
1.22 (1.00-1.50)
C
A
A
0.133
0.121
0.139
0.129
0.694
0.637
0.95 (0.75 – 1.21)
0.94 (0.73-1.21)
G
C
-
0.131
0.132
0.960
1.01 (0.78-1.29)
22
211
G
C
T
A
G
0.125
0.060
0.133
0.095
0.548
0.002
0.93 (0.73 – 1.18)
0.60 (0.44-0.83)
21
G
T
-
0.068
0.102
0.005
0.65 (0.49-0.88)
21
G
-
G
0.077
0.095
0.144
0.81 (0.60 – 1.08)
11
111
A
A
T
11
A
22
222
G
G
22
160
Chapter 3: Results
There were no significant p-values (see Table 3.58) in any of the mental illness datasets for
any of the DISC1 SNPs. The combined psychosis dataset also had non-significant p-values for
all SNPs.
For the SNPs DISC1_rs872625, DISC1_rs4658966 and DISC1_rs872624, the OR psychosis
dataset conveyed an opposite effect to the OR of the combined RA dataset (see Table 3.58).
For the remaining two SNPs, DISC1_rs9431714 and DISC1_rs821577, the ORs for both the
psychosis and combined RA datasets convey a similar effect (protection). Figures 3.61 to 3.65
show a graphical representation of the alignment of the mental illness datasets.
161
Chapter 3: Results
Table 3.58: Analysis for DISC1 SNPs, DISC1_rs9431714, DISC1_rs872625, DISC1_rs4658966, DISC1_rs821577 and DISC1_rs872624, in three mental illness datasets, GAIN
schizophrenia, non-GAIN schizophrenia and GAIN bipolar disorder. The allelic p-value, odds ratios and 95% confidence intervals are shown for each and for the combined
psychosis dataset. Information for NZRA and WTCCC are shown for comparison.
GAIN SZ
non-GAIN
SZ
GAIN BD
Psychosis
Combined
RA
rs9431714
OR
p-allelic
(95% CIs)
0.928
0.196
(0.828-1.040)
1.005
0.937
(0.896-1.127)
0.993
0.922
(0.860-1.146)
0.953
0.347
(0.862-1.054)
0.138
0.949
(0.886-1.017)
rs872625
p-allelic
0.811
0.110
0.642
0.756
0.075
OR
(95% CIs)
0.984
(0.862-1.123)
1.113
(0.976-1.270)
1.040
(0.883-1.225)
1.019
(0.907-1.144)
0.931
(0.860-1.007)
rs4658966
OR
p-allelic
(95% CIs)
1.047
0.555
(0.805-0.973)
1.144
0.083
(0.983-1.333)
1.158
0.125
(0.960-1.396)
1.083
0.240
(0.948-1.238)
0.001
162
0.806
(0.7613-0.911)
rs821577
p-allelic
0.874
0.493
0.775
0.670
0.018
OR
(95% CIs)
0.995
(0.890-1.113)
1.040
(0.930-1.163)
0.980
(0.852-1.127)
0.979
(0.888-1.079)
0.902
(0.829-0.983)
rs872624
p-allelic
0.487
0.201
0.376
0.310
0.017
OR
(95% CIs)
1.057
(0.904-1.235)
1.107
(0.947-1.293)
1.091
(0.900-1.322)
1.074
(0.936-1.232)
0.854
(0.751-0.972)
Chapter 3: Results
Figure 3.58: Odds ratio meta-analysis plot for DISC1_rs9431714 in DISC1. The GAIN SZ and GAIN BD
sample sets show a protective effect (OR < 1) towards SZ and BD respectively, the non-GAIN SZ sample set is
neither protective nor susceptible (OR = 1) for BD and the combined psychosis sample set is protective (OR < 1)
towards mental illness.
Figure 3.59: Odds ratio meta-analysis plot for DISC1_rs872625 in DISC1. The non-GAIN SZ and GAIN BD
sample sets show a susceptibility effect (OR > 1) towards SZ and BD respectively, the GAIN SZ sample set is
protective (OR < 1) for SZ and the combined psychosis sample set is susceptible (OR > 1) towards mental
illness.
Figure 3.60: Odds ratio meta-analysis plot for DISC1_rs4658966 in DISC1. The GAIN SZ and non-GAIN SZ
sample sets show a susceptibility effect (OR > 1) towards SZ, the GAIN BD sample set is protective (OR < 1)
for BD and the combined psychosis sample set is susceptible (OR > 1) towards mental illness.
163
Chapter 3: Results
Figure 3.61: Odds ratio meta-analysis plot for DISC1_rs821577 in DISC1. The GAIN SZ sample set is neither
protective nor susceptible (OR = 1) for SZ, non-GAIN SZ sample set is susceptible (OR > 1) for SZ, GAIN BD
is protective (OR < 1) for BD and the combined psychosis sample set is protective (OR < 1) towards mental
illness.
Figure 3.62: Odds ratio meta-analysis plot for DISC1_rs872624 in DISC1. The GAIN SZ, non-GAIN SZ and
GAIN BD sample sets show a susceptibility effect (OR > 1) towards SZ and BD. The combined psychosis
sample set is susceptible (OR > 1) towards mental illness.
The initial analysis of sex variation in DISC1 indicated that there was a significant difference
between males and females for DISC1_rs9431714 in NZRA (p-genotypic = 0.009) and in
GAIN SZ (p-allelic = 0.023). This SNP was not significant for any other datasets. However, it
was close to trending towards significance for WTCCC RA (p-allelic = 0.076).
DISC1_rs4658966 was significant for WTCCC RA (p-genotypic = 0.015) but not significant
for any other datasets. None of the other DISC1 SNPs were significant for sex variation over
these datasets (see Table 3.59). However, as the published literature suggests that there may
164
Chapter 3: Results
be a sex specific effect for DISC1 in SZ (see introduction section 1.4.3), all SNPs genotyped
in this gene were investigated further for sex differences (Table 3.60).
Table 3.59: Analysis of males versus females for the SNPs DISC1_rs9431714, DISC1_rs872625,
DISC1_rs4658966, DISC1_rs821577 and DISC1_rs872624. This analysis was completed over five different
datasets, NZRA, WTCCC RA, GAIN SZ, non-GAIN SZ and GAIN bipolar. Significant values are presented in
bold text.
RS9431714
RS872625
RS4658966
RS821577
RS872624
NZRA
WTCCC RA
GAIN SZ
Non- GAIN SZ
GAIN Bipolar
0.307
0.076
0.065
0.050
0.822
0.607
0.435
0.733
0.009
0.023
0.815
0.472
0.645
0.484
0.987
0.998
0.897
0.810
0.807
0.957
0.805
0.531
0.065
0.940
0.977
0.455
0.274
0.137
0.229
0.015
0.692
0.926
0.443
0.697
0.520
0.059
0.197
0.308
0.661
0.712
0.676
0.499
0.688
0.113
0.727
0.860
0.621
0.451
0.449
0.645
DISC1_rs9431714 shows sex bias towards females in the WTCCC RA (p = 0.0041) and
GAIN SZ (p = 0.0055) datasets conveying a susceptibility effect in RA (OR = 1.255) and a
protective effect in SZ (OR = 0.881). None of the datasets were significant in males only for
this SNP. DISC1_rs872624 reached significance for females only in the psychosis dataset (p
= 0.041), with a susceptibility OR towards mental illness (see Table 3.60). No other datasets
produced significant p-values for this SNP, although the WTCCC RA sample set was trending
towards significant for females only (p = 0.056). An OR protective for RA (OR = 0.82) was
produced in the WTCCC for DISC1_rs872624. There were no significant results for the
separate sex analysis of DISC1_rs872625, DISC1_rs4658966 and DISC1_rs821577. For all
SNPs the female only subset produced ORs in opposite directions for the WTCCC RA and
GAIN SZ/psychosis datasets. DISC1_rs9431714 has a susceptibility OR in WTCCC RA and
a
protective
OR
in
GAIN
SZ/psychosis,
DISC1_rs872625,
DISC1_rs4658966,
DISC1_rs821577 and DISC1_rs872624 all have susceptibility ORs for GAIN SZ/psychosis
and protective ORs for WTCCC RA.
165
Chapter 3: Results
Table 3.60: Sex specific association results over NZRA, WTCCC RA, and GAIN SZ for DISC1_rs9431714,
DISC1_rs872625, DISC1_rs4658966, DISC1_rs821577 and DISC1_rs872624. The psychosis dataset is a
combination of cases and controls form GAIN SZ, non-GAIN SZ and GAIN bipolar. The allelic p-value, odds
ratios and 95% confidence intervals are shown for each dataset. The significant values are shown in bold text.
Rs9431714
NZRA
WTCCC
GAIN SZ
Psychosis
Rs872625
NZRA
WTCCC
GAIN SZ
Psychosis
Rs4658966
NZRA
WTCCC
GAIN SZ
Psychosis
Rs821577
NZRA
WTCCC
GAIN SZ
Psychosis
Rs872624
NZRA
WTCCC
GAIN SZ
Psychosis
Females only
cases vs. controls
OR
p-allelic
(95% CIs)
1.109
0.335
(0.899-1.368)
1.255
0.0041
(1.074-1.465)
0.881
0.0055
(0.72-1.078)
0.894
0.058
(0.796-1.004)
0.806
0.170
(0.453-1.073)
0.853
0.076
(0.716-1.017)
1.107
0.871
(0.876-1.398)
1.069
0.326
(0.936-1.221)
0.961
0.789
(0.622-1.216)
0.939
0.619
(0.733-1.203)
1.081
0.540
(0.843-1.385)
1.160
0.056
(0.996-1.352)
0.913
0.430
(0.662-1.116)
0.997
0.968
(0.861-1.154)
1.109
0.263
(0.925-1.329)
1.008
0.883
(0.901-1.129)
0.977
0.875
(0.636-1.232)
0.820
0.056
(0.668-1.005)
1.180
0.200
(0.916-1.52)
1.178
0.041
(1.006-1.378)
Male only
cases vs. controls
OR
p-allelic
(95% CIs)
0.830
0.151
(0.627-1.075)
1.065
0.4302
(0.911-1.245)
1.106
0.732
(0.904-1.353)
1.027
0.609
(0.928-1.136)
0.851
0.397
(0.414-1.167)
1.016
0.860
(0.849-1.217)
0.990
0.545
(0.787-1.245)
1.040
0.511
(0.925-1.169)
0.525
0.225
(0.216-1.128)
1.278
0.054
(0.995-1.641)
1.011
0.912
(0.827-1.237)
1.072
0.314
(0.937-1.227)
0.942
0.678
(0.621-1.189)
0.916
0.247
(0.789-1.063)
0.891
0.126
(0.769-1.033)
0.977
0.641
(0.885-1.078)
0.999
0.994
(0.561-1.303)
0.982
0.862
(0.795-1.211)
0.958
0.681
(0.781-1.175)
1.015
0.834
(0.885-1.164)
166
Males and Females
cases vs. controls
OR
p-allelic
(95% CIs)
1.033
0.702
(0.875-1.219)
0.908
0.031
(0.831-0.991)
0.984
0.196
(0.853-1.135)
0.970
0.421
(0.900-1.045)
0.875
0.181
(0.719-1.064)
0.896
0.032
(0.810-0.991)
1.042
0.811
(0.885-1.228)
1.053
0.238
(0.966-1.148)
0.959
0.714
(0.768-1.198)
0.747
0.0001
(0.644-0.867)
1.047
0.555
(0.805-0.973)
1.108
0.045
(1.003-1.224)
0.909
0.244
(0.775-1.067)
0.900
0.023
(0.814-0.995)
0.995
0.874
(0.890-1.11)
1.000
0.999
(0.930-1.076)
0.941
0.676
(0.754-1.175)
0.814
0.011
(0.694-0.954)
1.057
0.487
(0.904-1.235)
1.086
0.114
(0.981-1.203)
Chapter 3: Results
3.2. METAͲANALYSISOFSNPSFROMSZCANDIDATEGENES
INTHELITERATURE
A meta-analysis of SNPs published in the schizophrenia literature was completed for AKT1
and DISC1 but not for NFATC1 or NFATC2. A literature search as outlined in section 2.5.2
produced no results for NFAT. Search terms for this gene were reduced to just the gene name
but no case-control association literature in mental illness was available.
3.2.1.
AKT1
3.2.1.1. SNP selection
A meta-analysis of AKT1 SNPs in the schizophrenia (and other neurological disorders)
literature was completed (see section 2.5.2). As BC|SNPmax used the latest NCBI build (July
2008) to obtain information on SNPs in the WTCCC and GAIN, only the SNPs that were
typed in this build were analysed. This meant that many of the most common SNPs in the
literature (rs2494732, rs2498799, rs3730358 and rs3803300) were not studied further (see
Table 3.61). Only SNPs appearing in two or more publications were included for metaanalysis. The papers listed in Table 3.25 below were those included in the meta-analysis using
STATA. Of the two SNPs within the AKT1 gene that were studied in this project
(AKT1_rs1130214 and AKT1_rs2494731) only AKT1_rs1130214 had appropriate data from
meta-analysis published in the literature. The information for the SNPs was combined with
data from the WTCCC and GAIN sample sets obtained through BC|SNPmax.
Table 3.61: Table of SNPs occurring more than once in the literature, the number of studies
with data for that SNP and whether the SNP was included in the NCBI July 2008 build. SNPs
in bold were analysed by meta-analysis.
SNP
rs1130214
rs2494732
rs3803300
rs3730358
rs2498799
rs2498804
rs3803304
Number of studies
10
8
8
7
6
5
2
167
In NCBI build July 2008
Yes
No
No
No
No
Yes
Yes
Chapter 3: Results
Table 3.62: Literature information (paper title, main author, year and journal published in) for AKT1 SNPs
investigated in a range of neurological disorders. Also included is the population studied.
Author Journal
Date
Disease
Population SNPs
GENETIC STUDY OF EIGHT AKT1 GENE POLYMORPHISMS AND THEIR INTERACTION
WITH DRD2 GENE POLYMORPHISMS IN TARDIVE DYSKINESIA
Zai
Schizophr
2008 Tardive
American
AKT1_rs3803304, AKT1_rs2498804,
Res.
Dyskinesia
rs249473, AKT1_rs1130214
ASSOCIATION ANALYSIS OF AKT1 AND SCHIZOPHRENIA IN A UK CASE CONTROL
SAMPLE
Norton Schizophr
2007 Schizo
UK/Ireland rs3803300, AKT1_rs1130214, rs3730358,
Res.
rs2498799, rs2494732, rs2498784,
rs10149779, AKT1_rs2498804, rs2494738,
AKT1_rs3803304
NO ASSOCIATION BETWEEN AKT1 POLYMORPHISM AND SCHIZOPHRENIA: A CASE–
CONTROL STUDY IN A KOREAN POPULATION AND A META-ANALYSIS
Lee
Neuroscience 2009 Schizo
Korean
Rs3803300, AKT1_rs1130214, rs3730358,
Research
rs1130233, rs2494732, AKT1_rs2498804
ASSOCIATION OF AKT1 WITH SCHIZOPHRENIA CONFIRMED IN A JAPANESE
POPULATION
Ikeda I Biol
2004 Schizo
Japanese
rs3803300, AKT1_rs1130214,
Psychiatry
rs3730358,rs2498799, rs2494732
AKT1 GENE POLYMORPHISMS AND OBSTETRIC COMPLICATIONS IN THE PATIENTS
WITH SCHIZOPHRENIA
Joo
Psychiatry
2009 Schizo
Korean
Rs3803300, AKT1_rs1130214, rs3730358,
Invest
rs1130233, rs2494732, AKT1_rs2498804
ASSOCIATION BETWEEN AKT1 GENE AND PARKINSON'S DISEASE: A PROTECTIVE
HAPLOTYPE
Xiromeri Neuroscience 2008 Parkinson's Greek
rs2494743, rs2498788,rs2494746,
siou
letters
AKT1_rs1130214
ASSOCIATION OF AKT1 GENE POLYMORPHISMS WITH RISK OF SCHIZOPHRENIA
AND WITH RESPONSE TO ANTIPSYCHOTICS IN THE CHINESE POPULATION.
Xu
J Clin
2007 Schizo
Han Chinese rs3803300, AKT1_rs1130214,
Psychiatry.
rs3730358,rs2498799, rs2494732
ASSOCIATION OF AKT1 HAPLOTYPE WITH THE RISK OF SCHIZOPHRENIA IN
IRANIAN POPULATION.
Bajestan Am J Med
2006 Schizo
Iranian
rs3803300, AKT1_rs1130214,
Genet B
rs3730358,rs2498799, rs2494732
NeuroGenet.
POSITIVE ASSOCIATION OF AKT1 HAPLOTYPE TO JAPANESE METHAMPHETAMINE
USE DISORDER.
Ikeda II Int J Neuro2006 Meth use Japanese
rs3803300, AKT1_rs1130214,
psychopharma
disorder
rs3730358,rs2498799, rs2494732,
FAILURE TO CONFIRM ASSOCIATION BETWEEN AKT1 HAPLOTYPE AND
SCHIZOPHRENIA IN A JAPANESE CASE-CONTROL POPULATION.
Ohtsuki Mol
2004 Schizo
Japanese
rs3803300, AKT1_rs1130214,
Psychiatry.
rs3730358,rs2498799, rs2494732
168
Chapter 3: Results
3.2.1.2. AKT1 Meta-analysis – part 1
Meta-analysis was completed on three SNPs (AKT1_rs1130214, AKT1_rs2498804, and
AKT1_rs3803304) utilizing data from the schizophrenia (and related disorders) literature (see
Table 3.63). The information obtained was used to find the combined Mantel-Haenszel (M-H)
test, odds ratios (ORs), allelic and Breslow-Day (B-D, test for heterogeneity) p-values and to
generate the odds-ratio meta-analysis plot. The analysis was performed twice for each SNP,
once with GAIN excluded and once with GAIN included. If the SNP is having an opposite
effect in each disease (i.e. protective in RA and susceptible in SZ or vice versa), as
hypothesised, the addition of the WTCCC RA dataset should cause the combined p-value to
become non significant. If the disease pathway is common to both RA and SZ the p-value
should remain significant or increase in significance.
Table 3.63: Genotyping information for AKT1 SNPs of interest extracted from the literature.
SNP
Study
(Lee et al., 2009)
(Joo et al., 2009)
(Zai et al., 2008)
(Xiromerisiou et al., 2008)
(Norton et al., 2007)
(Xu et al., 2007)
(Bajestan et al., 2006)
(Ikeda et al., 2005)
(Ohtsuki et al., 2004)
GAIN SZ
WTCCC RA
(Joo et al., 2009)
rs2498804
(Lee et al., 2009)
GAIN SZ
WTCCC RA
(Zai et al., 2008)
rs3803304
GAIN SZ
WTCCC RA
rs1130214
No. of
Cases
No. of
Controls
283
23
73
306
586
286
321
182
507
544
1152
2851
23
283
660
182
1172
1861
75
653
1172
1861
350
157
114
214
660
375
383
437
437
566
1310
3579
157
350
709
437
1378
2938
113
707
1378
2938
Minor Allele
Cases
Controls
95 (0.168)
100 (0.143)
6 (0.130)
48 (0.153)
42 (0.288)
51 (0.224)
144 (0.235) 111 (0.259)
361 (0.308) 415 (0.314)
79 (0.138)
83 (0.111)
126 (0.196) 162 (0.211)
57 (0.157)
136 (0.156)
146 (0.144) 136 (0.156)
157 (0.144) 179 (0.158)
677 (0.294) 769 (0.294)
1682 (0.295) 2055 (0.287)
24 (0.522)
123 (0.392)
238 (0.420) 283 (0.404)
409 (0.310) 458 (0.323)
146 (0.401) 384 (0.439)
757 (0.323) 910 (0.330)
1258 (0.338) 1857 (0.316)
39 (0.260)
57 (0.252)
327 (0.250) 368 (0.260)
579 (0.247) 711 (0.258)
986 (0.265) 1474 (0.251)
169
Major Allele
Cases
Controls
471 (0.832)
600 (0.857)
40 (0.870)
266 (0.847)
104 (0.712)
177 (0.776)
468 (0.765)
317 (0.741)
811 (0.692)
905 (0.686)
493 (0.862)
667 (0.889)
516 (0.804)
604 (0.789)
307 (0.843)
738 (0.844)
868 (0.856)
738 (0.844)
931 (0.856)
953 (0.842)
1627 (0.706) 1851 (0.706)
4020 (0.705) 5103 (0.713)
22 (0.478)
191 (0.608)
328 (0.580)
417 (0.596)
911 (0.690)
960 (0.677)
218 (0.599)
490 (0.561)
1587 (0.677) 1846 (0.670)
2464 (0.662) 4019 (0.684)
111 (0.740)
169 (0.748)
979 (0.750) 1046 (0.740)
1765 (0.753) 2045 (0.742)
2736 (0.735) 4402 (0.749)
Chapter 3: Results
Table 3.64: Meta-analysis for the AKT1SNPs investigated in the schizophrenia literature. Information is given
for the combined literature and GAIN dataset including and excluding the RA datasets (rs1130214 = WTCCC
and genotyping, rs2498804 and rs3803304 = WTCCC). Combined genotypes, minor allele frequencies (MAF),
odds ratio (95% CI, M-H), allelic p-value and the B-D p-value are given for AKT1_rs1130214,
AKT1_rs2498804, rs2494746, and AKT1_rs3803304.
rs3803304
rs2498804
rs1130214
RA
SNP datasets Samples
included
Controls
NO
Cases
YES
NO
YES
NO
YES
Allele count
MAF
Odds ratio (95%
CI)
2190
0.219
0.992
6636
12919
10656
3904
1890
4245
3572
2158
0.222
0.247
0.251
0.356
(0.923-1.065)
1.013
(0.962-1.068)
0.963
Controls
Cases
Controls
Cases
Controls
3066
7923
1574
4015
0.339
0.336
(0.885-1.048)
1.030
5530
3260
2855
7662
2832
1136
945
2610
0.339
0.258
0.249
0.254
(0.971-1.094)
0.950
(0.859-1.049)
1.015
Cases
5591
1931
0.257
(0.948-1.087)
Controls
Cases
Controls
Cases
1
2
7816
P-value
(allelic)
P-value
(B-D)
0.819
0.576
0.617
0.595
0.386
0.331
0.324
0.090
0.311
0.924
0.672
0.339
between the combined sample sets for any of the SNPs AKT1_rs1130214 (without RA p =
0.576, with RA p = 0.595), AKT1_rs2498804 (without RA p = 0.331, with RA p = 0.090), and
AKT1_rs3803304 (without RA p = 0.924, with RA p = 0.339). The combined allelic p-value
was not significant for any of the SNPs and this effect was not dependant on whether the RA
datasets were included or not: AKT1_rs1130214 (without RA p = 0.819, with RA p = 0.617),
AKT1_rs2498804 (without RA p = 0.386, with RA p = 0.324) and AKT1_rs3803304 (without
RA p = 0.311, with RA p = 0.672).
For AKT1_rs1130214, three studies (Lee et al., 2009, Zai et al., 2008, and Xu et al., 2007)
were trending towards significance for susceptibility effects in schizophrenia. All the other
papers were close to an OR of 1 (see Figure 3.63). For AKT1_rs2498804, two studies (Joo et
al., 2009 and Lee et al., 2009) were trending towards significance for a susceptibility effect in
SZ and two (Norton et al., 2007 and Ikeda et al., 2006) studies were trending towards
protective effects (see Figure 3.74). The overall effect was non-significant (see Table 3.63).
For AKT1_rs3803304, one paper (Norton et al., 2007) was trending towards a protective
effect while the other study (Zai et al., 2008) was trending towards a susceptibility effect for
SZ.
170
Chapter 3: Results
Figure 3.63: Odds ratio meta-analysis plot for AKT1_rs1130214 in AKT1. The M-H odds ratios for all sample
sets including the combined sample set are not significant.
sets, including the combined sample set, are not significant.
171
Chapter 3: Results
sets including the combined sample set are not significant.
172
Chapter 3: Results
3.2.1.3. AKT1 Meta-analysis – Part 2
A separate analysis was undertaken for Asian and Caucasian ethnicity. This was only
completed for the SNPs that had two or more datasets in either Asian or Caucasian (Table
3.65). To provide evidence for a wider effect in psychosis the non-GAIN SZ and GAIN BD
datasets were included along with GAIN SZ for analysis of the Caucasian datasets.
Table 3.65: SNPs for inclusion in the ethnicity based meta-analysis. Only SNPs with two or
more datasets for either ethnicity are shown.
SNP
rs1130214
Ethnicity
Caucasian
Asian
Asian
Caucasian
rs2498804
rs3803304
Datasets
Zai, Xiromerisiou, Norton, Baiestan
Lee, Joo, Xu, Ikeda, Ikeda II, Ohtsuki
Joo, Lee, Ideka II
Zai. Norton
The Breslow-Day (B-D) test (see Table 3.66) revealed that there was no evidence of genetic
heterogeneity between the combined sample sets for any of the SNPs AKT1_rs1130214
(Caucasian p = 0.663, Asian p = 0.391), AKT1_rs2498804 (p = 0.098), and AKT1_rs3803304
(p = 0.464). The combined allelic p-value was not significant for any of the SNPs,
AKT1_rs1130214 (Caucasian p = 0.786, Asian p = 0.851), AKT1_rs2498804 (p = 0.970) and
AKT1_rs3803304 (p = 0.070).
Table 3.66: Meta-analysis for the AKT1 SNPs investigated in the schizophrenia literature. Information is given
for either Caucasian or Asian ethnicity from the literature. For the Caucasian datasets this is combined with
GAIN SZ, GAIN BD and non-GAIN SZ to produce a combined dataset. Combined genotypes, minor allele
AKT1_rs1130214, AKT1_rs2498804 and AKT1_rs3803304.
SNP
Ethnicity Samples
Caucasian
rs1130214
Asian
rs2498804
Asian
rs3803304 Caucasian
Allele count
MAF
Odds ratio
(95% CI)
1
2
Controls
7172
2856
0.285
1.009
Cases
5990
3962
3110
1098
2396
682
540
790
0.286
0.147
0.148
0.418
(0.946-1.076)
1.012
(0.893-1.147)
1.003
568
6913
5321
408
2227
1773
0.418
0.244
0.250
(0.854-1.178)
1.027
(0.956-1.104)
Controls
Cases
Controls
Cases
Controls
Cases
173
P-value
(allelic)
P-value
(B-D)
0.786
0.663
0.851
0.391
0.970
0.098
0.070
0.464
Chapter 3: Results
Figure 3.66: Odds ratio meta-analysis plot for Caucasian ancestry AKT1_rs1130214 in AKT1. The psychosis
datasets all show borderline susceptibility. Xiromerisiou, Norton and Bajestan have a protective OR and Zai has
a susceptibility OR. The M-H odds ratios for the combined sample set are not significant.
Figure 3.67: Odds ratio meta-analysis plot for Asian ancestry AKT1_rs1130214 in AKT1. Joo, Ikeda I and
Ohtsuki have non-significant protective ORs, whereas Lee, Xu and Ikeda II have non-significant susceptible
ORs. The M-H odds ratios for the combined sample set are not significant.
174
Chapter 3: Results
Figure 3.68: Odds ratio meta-analysis plot for Asian ancestry AKT1_rs2498804 in AKT1. Joo and Lee are nonsignificant for susceptibility ORs and Ikeda II is non-significant for a protective OR. The M-H odds ratios for the
combined sample set are not significant.
Figure 3.69: Odds ratio meta-analysis plot for Caucasian ancestry AKT1_rs3803304 in AKT1. GAIN SZ and
GAIN BD are borderline protective whereas non-GAIN SZ is significant for susceptibility to SZ. Zai is nonsignificant for susceptibility and Norton is non-significant for a protective OR. The M-H odds ratios for the
combined sample set are not significant.
175
Chapter 3: Results
3.2.2.
DISC1
A meta-analysis of DISC1 SNPs in the Schizophrenia literature was completed using the
guidelines outlined in section 2.5.2. Only one common SNP (rs2492367, three papers) was
excluded from the meta-analysis due to a lack of genotyping in the NCBI build (see section
2.5.2). A list of SNPs with two or more occurrences in the literature is shown in Table 3.67. A
list of papers with genotyping information for the DISC1 SNPs is included in Table 3.68. The
information from the papers was combined with data from the WTCCC and GAIN sample
sets obtained through BC|SNPmax. None of the DISC1SNPs investigated in this thesis
(DISC1_rs9431714,
DISC1_rs872625,
DISC1_rs4658966,
DISC1_rs821577,
DISC1_rs872624) were found in two or more publications in the schizophrenia literature
(DISC1_rs701158, one study). The SNPs included in the meta-analysis were checked to
determine whether any were in high LD with those investigated in this thesis. No SNPs from
the literature were in high or complete LD with the genotyped SNPs.
Table 3.67: Table of DISC1 SNPs occurring more than once in the literature, the number of
studies with data for that SNP and whether the SNP was included in the NCBI July 2008 build.
SNPs in bold were analysed by meta-analysis.
SNP
rs821616
rs3738401
rs2492367
rs1000731
rs2038636
rs3738398
rs751229
rs821597
rs843979
rs999710
Number of studies
5
3
3
2
2
2
2
2
2
2
176
In NCBI build July 2008
Yes
Yes
No
Yes
No
Yes
No
Yes
Yes
Yes
Chapter 3: Results
Table 3.68: Literature information (paper title, main author, year and journal published in) for DISC1 SNPs
investigated in a range of neurological disorders. Also included is the population studied.
Author
Journal
Date
Disease
Population SNPs
ASSOCIATION BETWEEN THE TRAX/DISC LOCUS AND BOTH BIPOLAR DISORDER AND
SCHIZOPHRENIA IN THE SCOTTISH POPULATION
Thomson Molecular 2005 Schizo
Scottish
rs999710, rs821663, rs821616, rs821597,
Psychiatry
rs766288, rs751229, rs734551, rs701158,
rs6675281, rs3890280, rs3738401, rs2812393,
rs2759346, rs2492367, rs2038636, rs1954175,
rs1630250, rs1615409, rs1538977, rs1411776,
rs1411771, rs1322784, rs1285730,
rs12404162, rs1160491, rs11122396,
rs1030711, rs1025526, rs1000731, rs1000730
ASSOCIATION STUDY OF POLYMORPHISMS IN THE 5ƍ UPSTREAM REGION OF HUMAN
DISC1 GENE WITH SCHIZOPHRENIA
Kockelkorn Neurosci
2004 Schizo
Japanese
rs3806372, rs3738399, rs3738398, rs3738398
I and II
Letters
ASSOCIATION STUDY OF THE DISC1/TRAX LOCUS WITH SCHIZOPHRENIA IN A
JAPANESE POPULATION
Zhang I
Schizo
2005 Schizo
Japanese
rs999710, rs843979, rs821616, rs3738402,
Research
rs3738401, rs3737597, rs3524, rs2492367,
rs2273890, rs1865225, rs1572899, rs1000731
CASE-CONTROL ASSOCIATION STUDY OF DISRUPTED-IN-SCHIZOPHRENIA-1 (DISC1)
GENE AND SCHIZOPHRENIA IN THE CHINESE POPULATION
Chen
J Psych
2007 Schizo
Chinese
rs2295959, rs2492367, rs821616
Research
GENETIC ASSOCIATION BETWEEN SCHIZOPHRENIA AND THE DISC1 GENE IN THE
SCOTTISH POPULATION
Zhang II
American J 2006 Schizo
Scottish
rs1984895, rs3738401, rs751229, rs821616
of Medical
Genetics
NO SIGNIFICANT ASSOCIATION OF 14 CANDIDATE GENES WITH SCHIZOPHRENIA IN A
LARGE EUROPEAN ANCESTRY SAMPLE: IMPLICATIONS FOR PHYCHIATRIC GENETICS
Sanders
European
rs10864695, rs17768115, rs2038636,
Psychiatry
rs9431997, rs9432010
POSITIVE ASSOCIATION OF THE DISRUPTED-IN-SCHIZOPHRENIA-1 GENE (DISC1)
WITH SCHIZOPHRENIA IN THE CHINESE HAN POPULATION
Tang
Han Chinese rs4658971, rs821597, rs821616, rs843979
of Medical
Genetics
177
Chapter 3: Results
3.2.2.1. DISC1 Meta-analysis – part 1
Meta-analysis was completed via STATA on seven DISC1SNPs (DISC1_rs821616,
DISC1_rs3738401,
DISC1_rs1000731,
DISC1_rs3738398,
DISC1_rs821597,
DISC1_rs843979 and DISC1_rs999710) utilizing data from the schizophrenia literature (see
Table 3.69). The information obtained was used to find the combined Mantel-Haenszel (MH) test, odds ratios (ORs), allelic and Breslow-Day (B-D, test for heterogeneity) p-values and
to generate the odds-ratio meta-analysis plot.
Table 3.69: Genotyping information for DISC1 SNPs of interest extracted from the literature.
SNP
Study
(Thomson et al., 2005)
(Zhang et al., 2005)
GAIN SZ
WTCCC RA
(Kockelkorn et al., 2004) I
rs3738398
(Kockelkorn et al., 2004) II
GAIN SZ
WTCCC RA
rs3738401 (Zhang et al., 2005)
(Zhang et al., 2006a)
GAIN SZ
WTCCC RA
rs821597
(Tang et al., 2007)
GAIN SZ
WTCCC RA
rs821616 (Chen et al., 2007)
(Zhang et al., 2006a)
(Tang et al., 2007)
GAIN SZ
WTCCC RA
rs843979
(Tang et al., 2007)
GAIN SZ
WTCCC RA
rs999710
GAIN SZ
WTCCC RA
rs1000731
No. of No. of
Cases Controls
394
338
1351
1850
198
532
1351
1850
394
338
584
1351
1850
394
313
1164
1850
394
338
560
606
313
1164
1850
338
312
1164
1850
394
338
1172
1850
478
338
1378
2934
198
519
1378
2934
478
338
573
1378
2934
478
317
1369
2934
478
338
574
578
314
1369
2934
338
314
1369
2934
478
338
1378
2934
178
Minor Allele
Cases
Control
174 (0.221) 210 (0.220)
313 (0.463) 328 (0.485)
649 (0.240) 631 (0.229)
592 (0.160) 979 (0.167)
34 (0.086)
46 (0.116)
113 (0.106) 117 (0.113)
1318 (0.488) 1270 (0.461)
1552 (0.419) 2493 (0.425)
260 (0.330) 344 (0.360)
308 (0.456) 335 (0.496)
404 (0.346) 312 (0.272)
864 (0.320) 836 (0.303)
1185 (0.320) 1909 (0.325)
268 (0.340) 354 (0.370)
310 (0.497) 370 (0.584)
818 (0.351) 988 (0.361)
1324 (0.358) 2047 (0.349)
220 (0.279) 278 (0.291)
334 (0.494) 337 (0.499)
126 (0.113) 157 (0.137)
337 (0.278) 345 (0.298)
98 (0.157)
65 (0.104)
776 (0.333) 931 (0.340)
1052 (0.284) 1640 (0.279)
308 (0.456) 312 (0.462)
237 (0.380) 206 (0.328)
776 (0.333) 931 (0.340)
1244 (0.336) 1907 (0.325)
316 (0.401) 364 (0.381)
304 (0.450) 305 (0.451)
1172 (0.500) 990 (0.359)
1484 (0.401) 2302 (0.392)
Major allele
Cases
Controls
614 (0.779) 746 (0.780)
363 (0.537) 348 (0.515)
2053 (0.760) 2125 (0.771)
3108 (0.840) 4889 (0.833)
362 (0.914) 350 (0.884)
951 (0.894) 921 (0.887)
1384 (0.512) 1486 (0.539)
2148 (0.581) 3375 (0.575)
528 (0.670) 612 (0.640)
368 (0.544) 341 (0.504)
764 (0.654) 834 (0.728)
1838 (0.680) 1920 (0.697)
2515 (0.680) 3959 (0.775)
520 (0.660) 602 (0.630)
314 (0.503) 264 (0.416)
1510 (0.649) 1750 (0.639)
2376 (0.642) 3821 (0.651)
568 (0.721) 678 (0.709)
342 (0.506) 339 (0.501)
994 (0.888) 991 (0.863)
875 (0.722) 811 (0.702)
528 (0.843) 563 (0.896)
1552 (0.667) 1807 (0.660)
2648 (0.716) 4228 (0.721)
368 (0.544) 364 (0.538)
387 (0.620) 422 (0.672)
1552 (0.667) 1807 (0.660)
2456 (0.664) 3961 (0.674)
472 (0.599) 592 (0.619)
372 (0.550) 371 (0.549)
1172 (0.500) 1766 (0.641)
2216 (0.599) 3566 (0.608)
Chapter 3: Results
Table 3.70: Meta-analysis for the DISC1 SNPs investigated in the schizophrenia literature. Information is given
for the combined literature and GAIN dataset including and excluding the WTCCC data. Combined genotypes,
minor allele frequencies (MAF), odds ratio (95% CI, M-H), allelic p-value and the B-D p-value are given for
DISC1_rs821616,
DISC1_rs3738401,
DISC1_rs1000731,
DISC1_rs3738398,
DISC1_rs821597,
DISC1_rs843979 and DISC1_rs999710. Significant values are shown in bold.
rs999710
rs843979
rs821597
rs3738398
rs1000731
rs3738401
rs821616
SNP
WTCCC
included
NO
YES
NO
YES
NO
YES
NO
YES
NO
YES
NO
YES
NO
YES
Samples
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Controls
Cases
Allele count
1
2
5189
4859
9417
7507
3707
2498
7666
6013
3219
3303
8108
6138
2757
2697
6132
4845
2616
2344
6437
4720
2593
2307
6554
4763
2730
1949
6296
4165
2113
1891
3753
2943
1827
1736
3736
2921
1169
1136
2148
1728
1433
1465
3926
3017
1712
1396
3759
2720
1449
1321
3356
2565
1658
1826
3960
3310
MAF
0.289
0.280
0.285
0.282
0.330
0.332
0.328
0.327
0.266
0.256
0.209
0.220
0.342
0.352
0.390
0.384
384
0.396
Odds ratio (95%
CI)
0.976
(0.861-1.107)
0.988
(0.933-1.05)
1.04
(0.845-1.281)
0.956
(0.776-1.178)
1.021
(0.926-1.125)
0.990
(0.920-1.07)
0.373
0.369
0.366
1.069
(0.970-1.178)
1.016
(0.954-1.082)
0.894
(0.816-0.979)
0.968
(0.910-1.031)
0.358
0.364
0.339
0.350
0.378
0.484
0.386
0.443
1.013
(0.923-1.113)
1.034
(0.970-1.102)
1.392
(1.016-1.906)
1.287
(0.950-1.742)
P-value
(allelic)
P-value
(B-D)
0.710
0.040
0.696
0.050
0.711
<0.0001
0.670
<0.0001
0.678
0.482
0.788
0.506
0.176
0.113
0.625
0.101
0.016
0.053
0.310
0.009
0.782
0.143
0.307
0.239
0.040
<0.0001
0.103
<0.0001
As shown in Table 3.70 the Breslow-Day (B-D) test revealed that there was no evidence of
genetic heterogeneity between the combined sample sets for the SNPs DISC1_rs843979
(without WTCCC p = 0.143, with WTCCC p = 0.239), DISC1_rs1000731 (without WTCCC
p = 0.482, with WTCCC p = 0.506) and DISC1_rs3738398 (without WTCCC p = 0.113, with
WTCCC p = 0.101). However there was genetic heterogeneity in the SNPs DISC1_rs821616
(without WTCCC p = 0.040), DISC1_rs999710 (without WTCCC p = <0.0001, with WTCCC
p = <0.0001), DISC1_rs3738401 (without WTCCC p = <0.0001, with WTCCC p = <0.0001)
and for DISC1_rs821597 (with WTCCC p = 0.009). These SNPs were analysed using the
179
Chapter 3: Results
random effects meta-analysis program on Stata (see section 2.5). The combined allelic pvalue was not significant for DISC1_rs821616 (without WTCCC p = 0.710, with WTCCC p =
0.696), DISC1_rs3738401 (without WTCCC p = 0.711, with WTCCC p = 0.670),
DISC1_rs1000731 (without WTCCC p = 0.678, with WTCCC p = 0.788), DISC1_rs3738398
(without WTCCC p = 0.176, with WTCCC p = 0.625) and DISC1_rs843979 (without
WTCCC p = 0.782, with WTCCC p = 0.307) regardless of whether WTCCC was included or
not. DISC1_rs821597 was significant when WTCCC was excluded (p = 0.016) and not
significant when WTCCC was included (p = 0.310, see Table 3.70). The same was also true
for DISC1_rs999710 (without WTCCC p = 0.040, with WTCCC p = 0.103). A graphical
representation showing the alignment of the studies with the GAIN SZ datasets included in
this meta-analysis is provided in figures 3.73 to 3.79.
Figure 3.70: Random effects meta-analysis plot for DISC1_rs821616. The M-H odds ratios for all sample sets
bar Tang are trending towards protection. The M-H odds ratios for the combined sample set are not significant.
180
Chapter 3: Results
Figure 3.71: Random effects meta-analysis plot for DISC1_rs3738401. The M-H odds ratios for Thomson and
Zhang I (Zhang 2005) are protective. Zhang II (Zhang 2006) was significant for susceptibility to SZ. The M-H
odds ratios for the combined sample set are not significant.
Figure 3.72: Odds ratio meta-analysis plot for DISC1_rs1000731. The M-H odds ratio for Thomson and GAIN
SZ is borderline susceptible, and protective for Zhang. The M-H odds ratios for the combined sample set are not
significant.
181
Chapter 3: Results
Figure 3.73: Odds ratio meta-analysis plot for DISC1_rs3738398. The M-H odds ratio for all Kockelkorn I and
II is trending towards protection. The GAIN and combined sample set are trending towards susceptibility. The
M-H odds ratios for the combined sample set are not significant.
Figure 3.74: Odds ratio meta-analysis plot for DISC1_rs821597. The M-H odds ratio for all sample sets is
protective. The combined sample set is significant for protection (p = 0.016).
182
Chapter 3: Results
Figure 3.75: Odds ratio meta-analysis plot for DISC1_rs843979. The M-H odds ratio for Tang is susceptibility
to SZ and borderline protective for GAIN and Zhang. The M-H odds ratios for the combined sample set are not
significant.
Figure 3.76: Random effects meta-analysis plot for DISC1_rs999710. The M-H odds ratio for Thomson, Zhang
I and GAIN is susceptive to SZ. The combined sample set is significant for susceptibility to SZ (p = 0.040).
183
Chapter 3: Results
3.2.2.2. DISC1 Meta-analysis – Part 2
A separate analysis was undertaken for Asian and Caucasian ethnicity. This was only
completed for the SNPs that had two or more datasets in either Asian or Caucasian (Table
3.71). To provide evidence for a wider effect in psychosis the non-GAIN SZ and GAIN BD
datasets were included along with the GAIN SZ.
Table 3.71: SNPs for inclusion in the ethnicity based meta-analysis. Only SNPs with two
or more datasets for either ethnicity are shown.
SNP
rs821616
rs3738401
rs843979
Ethnicity
Caucasian
Asian
Caucasian
Asian
Datasets
Zhang II, Thomson
Chen, Tang, Zhang I
Thomson, Zhang II
Tang, Zhang I
As shown in Table 3.72 the Breslow-Day (B-D) test revealed that there was no evidence of
genetic heterogeneity between the combined sample sets for Caucasian ethnicity
DISC1_rs821616 (p = 0.968) or DISC1_rs843979 (p = 0.120). There was significant
heterogeneity between populations for DISC1_rs3738401 (p = 0.010) and Asian ethnicity
DISC1_rs821616 (p = 0.005). These SNPs were analysed using the random effects metaanalysis program on Stata (see section 1.1). The combined allelic p-value was not significant
for DISC1_rs821616 (Caucasian p = 0.145, Asian p = 0.738), DISC1_rs3738401 (p = 0.275)
or DISC1_rs843979 (p = 0.120). A graphical representation showing the alignment of the
studies included in this meta-analysis is provided in figures 3.80 to 3.83.
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Chapter 3: Results
Table 3.72: Meta-analysis for the DISC1 SNPs investigated in the schizophrenia literature. Information is given
for either Caucasian or Asian ethnicity from the literature. This is combined with GAIN SZ, GAIN BD and nonGAIN SZ to produce a combined dataset. Combined genotypes, minor allele frequencies (MAF), odds ratio
(95% CI, M-H), allelic p-value and the B-D p-value are given for DISC1_rs821616, DISC1_rs3738401 and
DISC1_rs843979.
SNP
Ethnicity
Caucasian
rs821616
Asian
rs3738401 Caucasian
rs843979
Asian
Samples
Allele count
MAF
Odds ratio
(95% CI)
2915
0.304
0.953
5589
1893
1864
7578
2333
559
558
2027
0.294
0.228
0.230
0.211
(0.892-1.02)
1.062
(0.747-1.508)
1.081
6305
786
755
1947
518
545
0.236
0.397
0.419
(0.940-1.244)
1.096
(0.936-1.282)
1
2
Controls
6679
Cases
Controls
Cases
Controls
Cases
Controls
Cases
P-value
(allelic)
P-value
(B-D)
0.145
0.968
0.738
0.005
0.275
0.010
0.255
0.120
Figure 3.77: Odds ratio meta-analysis plot for Caucasian ancestry DISC1_rs821616. The three psychosis datasets
have borderline protective ORs. The M-H odds ratios for Zhang II and Thomson are non-significant for
protection. The combined sample set is non-significant for protection.
185
Chapter 3: Results
Figure 3.78: Random effects meta-analysis plot for Asian ancestry DISC1_rs821616. The three psychosis
datasets have borderline protective ORs. The M-H odds ratios for Tang are significantly susceptible. Chen and
Zhang I are non-significant for protection. The combined sample set is non-significant for protection.
Figure 3.79: Random effects meta-analysis plot for Caucasian ancestry DISC1_rs3738401. The three psychosis
datasets have borderline susceptible ORs. Zhang II is significant for susceptibility and Thomson is nonsignificant for a protective OR. The combined sample set is non-significant for susceptibility.
186
Chapter 3: Results
Figure 3.80: Odds ratio meta-analysis plot for Asian ancestry DISC1_rs843979. The three psychosis datasets
have borderline protective ORs. The M-H odds ratios for Tang are significantly protective. Zhang I is nonsignificant for susceptibility. The combined sample set is non-significant for protection.
187
Chapter 3: Results
3.3. GENOTYPINGMICEFORKOSTATUS
K/BxN mice spontaneously develop RA. This is as a result of the cross between autoimmune
prone NOD mice, with a specific MHC haplotype, and KRN mice, transgenic for a TCR
which recognises a bovine pancreas RNase (see section 1.1.1) (Kouskoff et al., 1996). In the
spontaneous KRN model the candidate gene knockout (ie GRIK1/GRIK2) was bred onto a
NOD background. It was expected that when the NOD knockout mice were crossed with the
KRN mice the offspring (K/BxN) would develop spontaneous IA.
3.3.1.
KRN
KRN mice were originally chosen for breeding based on coat colour. Animals with the KRN
transgene had an agouti coat colour and this provided a quick and easy method of
identification. Genotyping was completed systematically to confirm the presence of the
transgene.
The initial genotyping of the KRN primers was completed for a number of animals. Both
control animals that underwent this initial genotyping were successfully found to be wildtype
(see Table 3.73). A sample of stock animals was then genotyped. All except one (stock animal
6, heterozygous KO) of these mice were genotyped as wildtype (see Table 3.73). This meant
that selecting the mice based on coat colour was not a robust method of selection and the mice
chosen for breeding did not have the KRN transgene. Following this revelation, the three male
breeding stock mice which fathered the mice from the stock set were genotyped for KRN (see
Table 3.73). One of the mice was found to be wildtype (KRN27). KRN31, the father of stock
animal 6, was heterozygous for the transgene. KRN30 also tested positive for KRN. Progeny
from other subsequent KRN crosses were genotyped for the presence of the KRN transgene
(see Table 3.73). Cross 2 is the offspring of KRN31, all of these mice tested positive for
KRN. All mice in cross 3 were also heterozygous for the transgene.
188
Chapter 3: Results
Table 3.73: Results from the genotyping of KRN mice. All mice tested were males.
Cross
Test
Stock
Breeding
stock
2
3
3.3.2.
Mouse ID
B6 F control 1
B6 F control 2
KRN stock 3
KRN stock 4
KRN stock 5
KRN stock 6
KRN stock 7
KRN stock 8
KRN 27
KRN30
KRN 31
31.1
31.2
31.3
31.4
31.5
KRN1
KRN2
KRN3
Genotype
Wildtype
Wildtype
Wildtype
Wildtype
Wildtype
Heterozygous KO
Wildtype
Wildtype
Wildtype
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
GRIK1
Initial crosses between heterozygous GRIK1 mice were set up to produce GRIK1-/- progeny.
These F1 mice were then interbred to increase the numbers of homozygote KOs in the colony
for involvement in the serum transfer/spontaneous IA models. The progeny of cross 1 and 2
were tested for identification of homozygous KO animals in the breeding colony. There were
six heterozygous and two homozygous (6.1M and 6.2M) progeny from cross 1 and 2
heterozygous, one homozygous KO (9.3M) and three homozygous wildtype in cross 2 (see
Table 3.74). Cross 3 represents the first cross between homozygous KO parents. Yet the
offspring of this cross indicates that the parents were not KOs (discussed below). Crosses 4
and 5 were subsequent homozygous crosses. All animals from cross 4 were homozygous for
GRIK1-/- while cross 5 produced four heterozygous (9.4M, 9.5M, 9.6F and 9.7M) and three
homozygous (9.6M, 9.8M and 9.9M) offspring (see Table 3.74).
Genotyping information from this thesis could also be utilised to confirm the genotype of the
parents. As mentioned earlier, the parents of cross 3 are not homozygous KOs. The progeny
189
Chapter 3: Results
of cross 3 indicate the parents were a homozygous wildtype and a homozygous KO. As the
progeny from cross 4 are all homozygotes, it is certain that the parents of this cross were both
homozygotes. However, in cross 5 the progeny are 50% homozygous and 50% heterozygous.
This indicates that the parents of this cross were a heterozygote and a homozygote.
Table 3.74: Results from the genotyping of GRIK1 mice. M=Male, F=Female.
Cross
1
2
3
4
5
Mouse ID
6.1F
6.1M
6.2F
6.2M
6.3F
6.3M
6.4M
6.5M
9.1M
9.3M
9.3M
9.1F
9.2F
9.3F
8.1M
8.1F
8.2F
8.3F
7.1M
7.2M
7.1F
7.2F
7.3F
9.4M
9.5M
9.6M
9.7M
9.8M
9.9M
9.6F
Genotype
Heterozygous KO
Homozygous KO
Heterozygous KO
Homozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Homozygous KO
Wildtype
Wildtype
Wildtype
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Heterozygous KO
Homozygous KO
Homozygous KO
Homozygous KO
Homozygous KO
Homozygous KO
Heterozygous KO
Heterozygous KO
Homozygous KO
Heterozygous KO
Homozygous KO
Homozygous KO
Heterozygous KO
190
Chapter 3: Results
3.3.3.
GRIK2
The GRIK2 mice were genotyped using the NEO primers for presence of the neo cassette. As
these animals were supplied as homozygotes for the GRIK KO, presence of the neo cassette
was evidence of the KO state (either heterozygous or homozygous). Cross 1 in Table 3.75 is
the result of the first backcross (BC1) of GRIK2-/- mice to NOD. Cross 2 is the progeny of a
heterozygous BC1 mouse to NOD. Only the males of each litter were genotyped for
identification of breeding animals for the next backcross. Cross 1 contained one animal
(1.5M) with the neo cassette and three animals without. Cross 2 also contained one animal
(2.2M) with the neo cassette and had five animals without it.
Table 3.75: Results from the genotyping of GRIK2 mice. M=Male, F=Female.
Cross
1
2
Mouse ID
from BC1
1.4M
1.5M
1.6M
1.7M
2.1M
2.2M
2.3M
2.8M
2.9M
2.10M
Genotype
Wildtype
contains NEO cassette
Wildtype
Wildtype
Wildtype
contains NEO cassette
Wildtype
Wildtype
Wildtype
Wildtype
191
Chapter 3: Results
3.4. INDUCTIONOFINFLAMMATORYARTHRITISINAMOUSE
KNOCKOUTMODEL
3.4.1.
SERUM TRANSFER MODEL
As discussed earlier, transfer of serum from spontaneously arthritic K/BxN mice into healthy
animals results in the induction of IA (see section 1.1.1). The serum transfer method was
performed on three groups of mice: GRIK1-/-, GRIK2-/- and wildtype controls. Pooled serum
from K/BxN arthritic mice was transferred to 3-4 mice in each of these groups as outlined in
section 2.6.3. After fourteen days, none of the mice were showing signs of IA. Therefore the
alternative strategy was chosen; induction of IA was to be through the spontaneous model for
all future experiments.
3.4.2.
SPONTANEOUS MODEL
As the induction of IA in the GRIK knockouts through serum transfer was ineffective, a new
breeding program was developed (see section 2.6.4). The cross between KRN transgenic and
NOD mice results in the progeny developing spontaneous IA (see section 1.6.3.1). However,
the GRIK knockouts were supplied on a 129/B6 background. To obtain GRIK knockouts with
minimal 129/B6 genomic DNA these mice were to be backcrossed to NOD over several
generations (see section 1.1.1). This would result in a cross between GRIK knockouts with
high levels of NOD DNA and KRN transgenic mice. In order to test the effect of GRIK KO
on spontaneous IA, animals with maximal NOD DNA identified from the microsatellite assay
will be set up for breeding the next backcross. The breeding of K/BxN to GRIK KOs is yet to
be completed.
192
Chapter 3: Results
!"#" $%&'()*+,--%+,.*))*/.0('.1%00,',2+*+%(2.3,+4,,2.
5,2($%&.12*.0'($.$(6),.)+'*%2).37.*21.89:.
3.5.1.
B6/129 ASSAY
A database of microsatellites’ sizes across different strains of mice was available in the
Merriman lab. The strains included were NOD, B6, C3H, DBA, BALBC and NON. There
was no information on the 129 strain. All of the microsatellites had primers stored in the
Merriman Lab. Initially these were titrated to determine PCR conditions (see section 2.4.2.1)*.
Where there was a distinct difference in size between the two bands, they were deemed
informative (see Figure 3.81 numbers 1, 3, 4 and 6). Microsatellites that produced two equally
sized bands were excluded from further analysis. Microsatellites occasionally failed titration
due to non-specific amplification of the PCR, no amplification (see Figure 3.81 number 7) or
bands not sufficiently defined to allow measurement. These microsatellites were retitrated and
repeated. Those that were still unsuccessful were excluded from the assay. Of the 110
microsatellites investigated in this thesis sixteen were excluded for failing titration (see Table
3.76). To test for informativeness between B6 and 129, each microsatellite was analysed for
band size differences using genomic DNA for purebred B6 and 129 (see Figure 3.81). Of the
94 microsatellites that titrated 37 were excluded for lack of informativeness between B6 and
129 (see Table 3.76). DNA for testing the assay was extracted from mouse liver samples (see
section 2.6.6.2).
1
2
3
4
5
6
7
8
9
B6 129
Figure 3.81: Gel electrophoresis visualisation of nine microsatellites over B6 and 129 DNA.
*
A full list of primer sequences and PCR conditions for the microsatellites used in this thesis is available in the
appendices (see APPENDIX A:)
193
Chapter 3: Results
Table 3.76: Microsatellites investigated in this thesis and whether they were included in the B6/129 assay.
Microsatellites that were excluded have the reasoning listed.
Microsatellite
Included in
Reason for
Microsatellite
Included in
Reason for
assay (Y/N)
exclusion
assay (Y/N)
exclusion
D1Mit373
Y
D9Mit223
N
not informative
D1Mit236
N
not informative
D9Mit182
N
not informative
D1Mit490
Y
D10Mit213
Y
D1Mit102
Y
D10Mit109
Y
D1Mit209
Y
D10Mit95
Y
D2Mit1
Y
D10Mit180
Y
D2Mit56
Y
D11Mit51
N
not informative
D2Mit305
Y
D11Mit349
N
did not titrate
D2Mit343
Y
D11Mit109
Y
D2Mit296
N
did not titrate
D11Mit98
N
not informative
D2Mit147
Y
D11Mit4
Y
D3Mit46
N
not informative
D11Mit212
N
not informative
D3Mit25
Y
D11Mit116
N
not informative
D3Mit230
N
not informative
D11Mit333
Y
D3Mit84
Y
D11Mit77
N
not informative
D3Mit147
Y
D11Mit263
Y
D4Mit111
N
not informative
D11Mit112
N
not informative
D4Mit26
N
not informative
D11Mit60
N
did not titrate
D4Mit204
Y
D11Mit79
N
did not titrate
D4Mit166
Y
D11Mit36
N
not informative
D4Mit251
N
did not titrate
D12Mit172
Y
D4Mit254
Y
D12Mit77
Y
D5Mit145
Y
D13Mit3
Y
D5Mit164
N
not informative
D13Mit76
Y
D5Mit80
Y
D14Mit14
N
did not titrate
D5Mit94
N
did not titrate
D14Mit37
N
not informative
D5Mit200
N
not informative
D14Mit160
Y
D5Mit239
N
not informative
D14Mit265
Y
D5Mit292
N
not informative
D15Mit107
Y
D6Mit86
Y
D15Mit12
Y
D6Mit188
N
not informative
D15Mit209
Y
D6Mit105
Y
D15Mit93
Y
D6Mit109
N
not informative
D15Mit193
Y
D6Mit31
N
not informative
D16Mit146
N
not informative
D6Mit314
N
not informative
D16Mit106
Y
D6Mit14
N
not informative
D16Mit163
Y
D7Mit178
Y
D16Mit87
N
not informative
D7Mit228
N
not informative
D16Mit131
Y
D7Mit44
N
did not titrate
D16Mit9
N
did not titrate
D7Mit40
Y
D16Mit152
Y
D8Mit291
N
not informative
D17Mit49
Y
D8Mit242
Y
D17Mit113
N
did not titrate
D8Mit24
N
did not titrate
D17Mit139
N
not informative
D8Mit226
N
did not titrate
D17Mit221
Y
D8Mit225
N
not informative
D18Mit111
N
not informative
D8Mit113
Y
D18Mit60
Y
D8Mit125
N
not informative
D18Mit37
N
not informative
D8Mit204
N
did not titrate
D18Mit124
Y
D8Mit223
N
not informative
D18Mit184
Y
D8Mit64
N
did not titrate
D18Mit207
Y
D8Mit249
N
not informative
D18Mit154
Y
D9Mit205
Y
D18mit106
N
not informative
N
not informative
D19Mit68
Y
D9Mit196
D9Mit104
Y
D19Mit53
Y
D9Mit191
N
did not titrate
D19Mit106
N
did not titrate
194
Chapter 3: Results
The microsatellites investigated were representative of the whole genome, yet the successful
markers did not cover all regions.
Figure 3.82: Visual representation of the approximate placement of microsatellite markers included in the
B6/129 assay.
195
Chapter 3: Results
Table 3.77: Successful informative microsatellites with their exact position on the chromosome.
Microsatellite
Chromosome
Microsatellite
Chromosome
Position (Mb)*
Position
D1Mit373
26.5
D11Mit109
37.7
D1Mit490
106
D11Mit263
55.6
D1Mit102
149.1
D11Mit4
68.4
D1Mit209
193.3
D11Mit333
108.6
D2Mit1
3.8
D12Mit172
47.2
D2Mit56
70.5
D12Mit77
100.3
D2Mit305
128.5
D13Mit3
20.5
D2Mit343
169.1
D13Mit76
111.4
D2Mit147
172.2
D14Mit160
40
D3Mit25
56.6
D14Mit265
101.7
D3Mit84
143.4
D15Mit12
3.2
D3Mit147
148.4
D15Mit209
61.5
D4Mit166
93.5
D15Mit93
72.4
D4Mit204
133
D15Mit107
84.2
D4Mit254
153.1
D15Mit193
97.8
D5Mit145
7.4
D16Mit106
98.0
D5Mit80
47.8
D16Mit163
7.33
D6Mit86
4.4
D16Mit131
7.32
D6Mit105
107.7
D16Mit152
85.8
D7Mit178
3.5
D17Mit49
45.4
D7Mit40
124
D17Mit221
90.5
D8Mit242
104.2
D18Mit60
32.6
D8Mit113
111.4
D18Mit124
57.6
D9Mit205
37.1
D18Mit184
67
D9Mit104
65.8
D18Mit207
68.8
D10Mit213
20.1
D18Mit154
75.8
D10Mit109
41.3
D19Mit68
3.6
D10Mit95
92
D19Mit53
45.2
D10Mit180
117.6
* = Chromosome positions are taken from The Jackson Laboratory website
http://www.informatics.jax.org/genes.shtml
196
Chapter 3: Results
3.5.2.
B6/NOD ASSAY
The change from the serum induced IA mouse model to the spontaneous IA model affected
the microsatellite assay. The strains of interest changed from B6 and 129 to B6 and NOD. A
new microsatellite assay was needed to determine the amount of NOD genomic DNA the B6
mice backcrossed to this strain contained. As no information was available on 129, the
original microsatellites were chosen if they had a product size different to all the other strains.
One of these strains was NOD. This meant that all the microsatellites chosen for their
potential to differentiate between B6 and 129 should also differentiate between B6 and NOD.
The primers with informativeness between B6 and 129 were then genotyped over genomic B6
and NOD DNA (see Figure 3.83).
1
2
3
4
5
Figure 3.83: Gel electrophoresis visualisation of five microsatellites over B6 and NOD DNA. Where there was a
distinct difference in size between the two bands, they were deemed informative (i.e. numbers 3 and 4).
Microsatellites that produced two equally sized bands were excluded from further analysis (i.e. number 2).
Microsatellites that failed the assay (i.e. number 1 and 5) were retitrated and repeated. Those that were still
unsuccessful were excluded from the assay.
3.5.2.1. Testing the Assay
Some of the microsatellites in the B6/NOD assay had size differences between the strains that
were too small to differentiate on gel electrophoresis. These microsatellites were deemed not
informative for this assay. The spread of microsatellites was reconsidered and the appropriate
amount for each chromosome continued to the next stage of investigation. Some
chromosomes had few informative microsatellites in the B6/129 assay. The uninformative
microsatellites for these chromosomes were tested again in the B6/NOD assay. The results are
197
Chapter 3: Results
listed in Table 3.78. Some microsatellites that titrated in the B6/129 assay would not titrate in
the B6/NOD assay. Of the 80 microsatellites investigated for this assay only 47 titrated (see
Table 3.78). This may be due to the age and storage conditions degrading the microsatellites
primers, thus affecting their ability to amplify the DNA.
Table 3.78: Microsatellites investigated in this thesis and whether they were included in the B6/NOD assay.
Microsatellites that were excluded have the reasoning listed.
Microsatellite
D1Mit373
D1Mit236
D1Mit490
D1Mit102
D1Mit209
D2Mit1
D2Mit56
D2Mit305
D2Mit343
D2Mit147
D2Mit225
D3Mit46
D3Mit25
D3Mit230
D3Mit84
D3Mit147
D4Mit111
D4Mit26
D4Mit166
D4Mit204
D4Mit254
D5Mit145
D5Mit164
D5Mit80
D5Mit200
D5Mit239
D5Mit292
D6Mit86
D6Mit188
D6Mit105
D6Mit109
D6Mit31
D6Mit314
D6Mit14
D7Mit178
D7Mit228
D7Mit40
D8Mit291
D8Mit242
D8Mit225
D8Mit113
D8Mit125
D8Mit223
D8Mit249
D9Mit205
D9Mit196
D9Mit104
Included in
assay (Y/N)
N
Y
N
N
N
N
Y
N
Y
N
N
N
N
Y
N
N
Y
N
Y
N
N
Y
N
Y
N
N
Y
Y
N
Y
N
Y
Y
Y
Y
Y
N
Y
Y
N
N
Y
Y
N
Y
N
N
Reason for
exclusion
did not titrate
Microsatellite
did not titrate
did not titrate
did not titrate
not informative
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
stock empty
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
stock empty
did not titrate
stock empty
did not titrate
did not titrate
stock empty
did not titrate
did not titrate
198
D9Mit223
D9Mit182
D10Mit213
D10Mit109
D10Mit95
D10Mit180
D11Mit51
D11Mit98
D11Mit4
D11Mit212
D11Mit116
D11Mit333
D11Mit77
D11Mit263
D11Mit112
D11Mit36
D12Mit172
D12Mit77
D13Mit3
D13Mit76
D14Mit37
D14Mit160
D14Mit265
D15Mit107
D15Mit12
D15Mit209
D15Mit193
D16Mit146
D16Mit106
D16Mit163
D16Mit87
D16Mit131
D16Mit152
D17Mit49
D17Mit139
D17Mit221
D18Mit111
D18Mit60
D18Mit37
D18Mit124
D18Mit184
D18Mit207
D18Mit154
D18mit106
D19Mit68
D19Mit53
Included in
assay (Y/N)
N
N
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
N
Y
Y
N
Y
Y
Y
Y
N
N
N
N
Y
Y
Y
N
N
N
Y
N
N
Y
N
Y
N
N
N
Y
Y
Y
N
N
Y
Reason for
exclusion
stock empty
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
not informative
did not titrate
not informative
stock empty
did not titrate
not informative
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
did not titrate
Chapter 3: Results
3.5.2.2. Backcross One
The microsatellites underwent PCR and gel electrophoresis over two test DNA samples. On
the same gel there were also two reference samples, of B6 and NOD DNA, for each
microsatellite. The test DNA samples were extracted from tail tips of mice from the first
GRIK2 heterozygous KO/NOD backcross progeny. The progeny had been screened for the
presence of the neo cassette and two mice (samples 2 and 5) were positive.
Of the 47 microsatellites tested in this assay only 25 produced bands on gel electrophoresis
for the test samples (see Table 3.81). These failed microsatellites underwent PCR and gel
electrophoresis again but produced no new results. The microsatellites produced bands for
the reference B6 and NOD DNA but not the test samples. The quality of the test sample DNA
was thought to be an issue for the assay. The DNA concentration of all the samples were
analysed via NanoDrop! spectrophotometer (see Table 3.79).
Table 3.79: DNA concentrations of Mouse DNA samples used in the
backcross one assay. Results are given as an average for three recordings.
Sample
B6
NOD
Test sample 2
Test sample 5
DNA concentration (ng)
35.1
11.7
2.4
2.3
Due to the low concentration of the test samples, a phenol-chloroform clean up was required.
The DNA concentrations of the test samples following this protocol are listed in Table 3.80.
These concentrations were still too low to successfully repeat the assay.
Table 3.80: DNA concentrations of Mouse DNA test samples used in the
backcross one assay. Results are given as an average for three recordings.
Sample
Test sample 2
Test sample 5
DNA concentration
2.15
0.9
199
Chapter 3: Results
Table 3.81: B6/NOD assay results. 2 = test sample 2, 5 = test sample 5.
Microsatellite
All bands
present
Test DNA =
reference DNA
Microsatellite
All bands
present
Test DNA =
reference DNA
D1Mit236
D2Mit56
D2Mit343
D3Mit230
D4Mit111
D4Mit166
D5Mit145
D5Mit80
D5Mit292
D6Mit86
D6Mit105
D6Mit31
D6Mit314
D6Mit14
D7Mit178
D7Mit228
D8Mit291
D8Mit242
D8Mit125
D8Mit223
D9Mit205
D10Mit213
D10Mit109
D10Mit95
only reference
only reference
only reference
only reference
only reference
yes
yes
only reference
yes
yes
yes
only reference
only reference
only reference
only reference
yes
test 2 missing
yes
yes
only reference
test 2 missing
only reference
yes
only reference
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = B6
2 = NOD, 5 = NOD
5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
5 = NOD
2 = NOD, 5 = NOD
-
D10Mit180
D11Mit51
D11Mit98
D11Mit4
D11Mit116
D11Mit333
D11Mit77
D11Mit112
D11Mit36
D12Mit77
D13Mit3
D13Mit76
D14Mit37
D15Mit209
D15Mit193
D16Mit146
D16Mit131
D17Mit139
D18Mit111
D18Mit184
D18Mit207
D18Mit154
D19Mit53
yes
yes
yes
yes
only reference
yes
only reference
only reference
only reference
only reference
only reference
yes
yes
yes
test 2 missing
only reference
yes
only reference
only reference
yes
yes
yes
test 2 missing
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
5 = B6
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
5 = NOD
2 = NOD, 5 = NOD
2 = NOD, 5 = NOD
2 = B6, 5 = NOD
2 = NOD, 5 = NOD
5 = NOD
Twenty-five microsatellites produced results for one or both test samples. Both samples had a
high proportion of NOD genomic DNA (sample 2 = 19/20, sample 5 = 23/25). Test sample 2
had the highest percentage of NOD genomic DNA (sample 5 = 92%, sample 2 = 95%).
However, sample 5 had greater representation of the genome (sample 5 = 25 successful
microsatellites, sample 2 = 20 successful microsatellites).
200
Chapter 3: Results
3.5.2.3. Selecting breeding animals from the assay
The mouse with the greatest proportion of NOD genomic DNA was to be selected from the
assay results for future backcrosses. In this case only two out of seven males from two litters
had the neo cassette and were analysed in the microsatellite assay. As there were only two
mice for potential breeding and both had a high percentage of NOD genomic DNA (see
section 3.5.2.2), both were chosen for the breeding program.
201
Chapter 4: Discussion
4
.
CHAPTERFour
DISCUSSION AND CONCLUSIONS 4.1. Genotyping of RA Candidate Genes from the AKT1
Pathways
4.2. Two Hypotheses for the Negative Correlation Between
RA and SZ
4.3. Meta-Analysis of SNPs from SZ Candidate Genes In the
Literature
4.4. Induction of Inflammatory Arthritis in Mice
4.5. Microsatellite Assay for Differentiation Between
Genomic DNA From Mouse Strains B6 and 129
4.6. Summary and Future Research
202
In addition to the debilitating symptoms of RA there is the potential for a number of
secondary complications to occur (section 1.1.1). Clinical management (section 1.1.1.6),
although crucial in maintaining quality of life in patients, has not yet being successful in
reversing development of RA. Therefore identification of associated genes and pathways is
paramount in providing therapies in this area. This thesis was designed to investigate the
potential association of AKT1 and GRIK pathways in the development of RA and SZ.
4.1. GENOTYPINGOFRACANDIDATEGENESFROMTHEAKT1
PATHWAYS
It is hypothesised in this thesis that RA and SZ have a common disease pathway. Previous
findings suggest AKT1 may be a shared part of this pathway (refer to section 1.4.2).
Potentially, RA-associated SNPs in the human genome were selected for analysis based on
data from a publically available GWAS study in RA. A total of eleven SNPs, in three
different genes, which were significantly associated or trending towards significance with RA
in the WTCCC data, were genotyped across NZ and UK RA (OXRA and UKRA) sample sets
(refer to sections 3.1).
4.1.1. AKT1
AKT1 is likely to be a factor in the negative correlation between RA and SZ. This is because
AKT1 plays a role in the normal developmental systems which are defective in both RA and
SZ. It is involved in growth factor-induced neuronal development in the developing nervous
system and in cell survival and angiogenesis (Hemmings, 1997; Tarnawski et al., 2010).
4.1.1.1. Association with RA
WTCCC data provided evidence of association with two AKT1 SNPs and was trending
towards association with three other SNPs. Two SNPs within the AKT1 gene,
AKT1_rs2494731
and
AKT1_rs1130214,
and
203
two
SNPs
downstream
of
AKT1,
AKT1_rs7146661 and AKT1_rs4983386, were genotyped over the NZ and UK RA sample
sets. Of these SNPs only AKT1_rs7146661 showed evidence for association, over the UKRA
cohort. Meta analysis of the genotyping data with the WTCCC data provided significant
results for two SNPs, AKT1_rs2494731 and AKT1_rs4983386, and was trending towards
significance for a third, AKT1_rs1130214. All three SNPs had ORs with the minor allele
conveying a susceptibility effect to RA. However, only the analysis of the AKT1_rs4983386
combined dataset (WTCCC and RA genotyping) produced a more significant association with
RA than WTCCC alone. Haplotype analysis of the genotyped data for AKT1_rs4983386 and
AKT1_rs2494731 was significant for one haplotype (21, p = 0.0009). This haplotype
conveyed a susceptibility OR for RA. However the genotyped results were not significant for
the two haplotypes significantly associated with RA in the WTCCC (11 and 22). Conditional
analysis of this haplotype in the combined WTCCC and genotyping (NZRA+UKRA) dataset
revealed evidence for two independent effects in AKT1 (p = 0.415).
4.1.1.1. Association with psychosis
The AKT1 SNPs were assessed for association over three mental illness datasets,
encompassing SZ and BD. This study found no significant association of the AKT1 SNPs with
SZ or BD in the single datasets or with mental illness in the combined dataset. Due to the
large size of the combined psychosis dataset it is not likely the AKT1 SNPs are associated
with psychosis. A power analysis undertaken for this dataset revealed the sample size is
adaquate to detect association (ie for rs7146661 it is 99% likely association will be detected
with an OR of 1.2). This is true even with weak effect sizes seen in this thesis (ie for
rs7146661 there is a 66% probability association will be detected with an OR of 1.1).
4.1.1.2. Sex bias
A significant difference between the sexes was only detected in one of the four SNPs.
AKT1_rs7146661 produced a significant result for sex bias in NZRA, yet when the sexes were
analysed separately there was no significant association with RA. It is unlikely that there is
sex bias within AKT1.
204
4.1.1.3. Summary
The strengthening effect, seen for locus AKT1_rs4983386, when combining results for sample
sets genotyped in this study with the WTCCC data indicates that increasing the sample size
may produce a significant result for this SNP. The genotyping data of this study provides
support for a significant association for a two SNP AKT1 haplotype (AKT1_rs2494731 and
AKT1_rs4983386) with RA. This data, together with conditional analysis, suggests that there
are two independent effects within AKT1. There is also potential for evidence of a third effect
at AKT1_rs1130214. However, a larger sample size would be needed to reach statistical
significance. It is also possible that the SNP AKT1_rs6644 which is at 96% LD with
AKT1_rs4983386 is associated with RA and that AKT1_rs4983386 is simply encompassing
the significant association of this SNP.
4.1.1. NUCLEAR FACTOR OF ACTIVATED T CELLS (NFAT)
NFAT is a Ca2+ dependant nuclear factor with five subtypes, NFATC1-C5 (see section 1.4.4).
These factors are involved in various parts of the bone remodelling process (Sitara &
Aliprantis, 2009). Genes in this pathway have been functionally linked to RA (Yoo et al.,
2006) and SZ (Thirunavukkarasu et al., 2006).
There were no significantly associated SNPs in the WTCCC for NFATC1 and NFATC2.
However,
there
was
one
SNP
trending
towards
significance
for
each
gene,
NFATC1_rs2002311 and NFATC2_rs8119787. When these two SNPs were genotyped over
the NZRA cohort the level of significance (p< 0.05) for association was not reached (see
section 1.1.1). However, adding the NZRA genotyping data to the WTCCC data for
NFATC2_rs8119787 decreased the p-value to below the threshold for significance (p =
0.032). Although there was no change to the M-H odds ratios, the significance of the result
was strengthened for the combined cohort (see section 3.1.4.2). It is possible that this finding
205
is a false positive. If this was a true result then using a larger sized WTCCC RA sample set
may result in this SNP reaching significance for protection against RA. The Stahl dataset
which included information for the WTCCC among other large GWAS studies reached
significance for NFATC1_rs2002311 (p = 0.047) but not NFATC2_rs8119787. The minor
allele OR for both SNPs were conveying the same effect in Stahl as all other datasets.
NFATC1 and NFATC2 have both been shown to trans-activate ADAMTS subtypes
(Thirunavukkarasu et al., 2006; Yaykasli et al., 2009), a gene which has previously been
associated with SZ.
For both NFATC1_rs2002311 and NFATC2_rs8119787, the addition of the GAIN SZ dataset
to the combined RA (WTCCC and genotyping) dataset increased the significance of
association (p = 0.046 and 0.008 respectively). The OR of the minor allele in the RA datasets
was also well aligned with the GAIN SZ dataset for both SNPs. This suggests that for
NFATC1_rs2002311
the
minor
allele
is
susceptible
to
both
diseases
and
for
NFATC2_rs8119787 the minor allele is protective against both diseases. This phenomenon is
discussed in a later section (see section 4.2.2.1). This study found no significant association of
the AKT1 SNPs with SZ or BD in the single datasets or with mental illness in the combined
dataset.
4.1.1.3. Sex bias
A significant difference between the sexes was detected for NFATC1_rs2002311 but not for
NFATC2_rs8119787. This difference was found in the analysis of male cases against female
cases for the WTCCC RA (p = 0.017) and GAIN SZ datasets (p = 0.010). This analysis was
also trending towards significance for the GAIN BD (p = 0.062) dataset. When the sexes were
analysed separately the WTCCC RA dataset was significantly associated with disease in both
the male only (p = 0.002) and female only (p = 0.028) subsets. These results were more
significant than the analysis of males and females together which, as previously discussed,
206
was only trending towards significance (p = 0.06). The ORs for the subsets were in opposite
directions, with females susceptible to RA and males protected. The psychosis dataset was
significant for the females only analysis (p = 0.034) and the OR for this subset was protective
against mental illness. These analyses indicate that sex bias may be playing a role at the
NFATC1_rs2002311 locus. To provide more supporting evidence for the involvement of sex
in determining the effect of the minor allele a haplotype of NFATC1 SNPs should be assessed
for this bias.
4.1.1.4. Summary
For both NFATC1_rs2002311 and NFATC2_rs8119787, the addition of the GAIN SZ dataset
to the RA datasets increased the significance of the association with disease. Therefore it is
possible that these SNPs fall within genes in a disease pathway common to the two diseases.
In addition there is potential for sex bias to play a role in the gene NFATC1. Further studies
should be undertaken to assess whether the bias seen at the SNP NFATC1_rs2002311 is
illustrating a bias for the whole gene or is isolated to this one locus.
4.1.2. DISRUPTED IN SCHIZOPHRENIA 1 (DISC1)
DISC1 is an excellent candidate gene for the negative association between RA and SZ (see
section 1.4.3) as it has been associated with SZ in a number of independent studies (Clair et
al., 1990; Ekelund et al., 2004; Hennah et al., 2003; Kockelkorn et al., 2004; Thomson et al.,
2005) and is implicated in the AKT1 pathway, which is known to be disrupted in RA (Kim et
al., 2002).
A recent study has implicated DISC1 in the RA associated AKT1 pathway through direct
inhibition of GSK3E (Mao et al., 2009).The WTCCC data provides evidence for significant
association of DISC1 with RA at eight SNPs and trends towards significance with one
207
additional SNP. Two SNPs within the DISC1 gene were genotyped over the NZ and UK RA
samples and three SNPs were genotyped over the NZRA samples only (see section 3.1.5).
None of these individual SNPs illustrated association with RA. However, when the
genotyping information was meta-analysed with the WTCCC RA data there was significant
association for three SNPs (DISC1_rs4658966, DISC1_rs821577, and DISC1_rs872624). Of
these only DISC1_rs821577 improved the significance of the association in comparison to the
WTCCC RA data alone (combined dataset p = 0.018 and WTCCC p = 0.023). This SNP may
have reached significance in the genotyped dataset had the sample size been larger. It is
recommended that this SNP be genotyped over the UK sample set to ascertain whether this is
the case*.
DISC1_rs872625 was the only DISC1 SNP in this thesis to reach significance in the Stahl
GWAS study (p = 0.041). The finding of this study could not be replicated in any of the
genotyped sample sets. The use of a large sample set may be the reason for significance in the
Stahl dataset.
Significance of three SNP haplotypes, containing DISC1 SNPs DISC1_rs872625,
DISC1_rs4658966 and DISC1_rs872624, illustrated evidence of association with the disease
in the NZRA dataset. One of these haplotypes was also associated in the WTCCC and two
were associated only in the genotyped data of this study (see section 3.1.5.2).
DISC1 was first identified as associated with SZ in 1990 (Clair et al.). Further investigation
revealed that this gene is also associated with bipolar disorder (Hodgkinson et al., 2004; Palo
et al., 2007), major depression, autism and Asperger’s syndrome (Kilpinen et al., 2007).
For all SNPs, except DISC1_rs9431714, the addition of the GAIN SZ dataset to the combined
RA dataset decreased the significance of association. DISC1_rs9431714 was more significant
(p = 0.053) when these sample sets were combined than for either GAIN SZ (p = 0.196) or
*
DISC1_rs821577 was analysed via PCR RFLP. DISC1 SNPs analysed in this way were not genotyped over the
UK samples due to the restricted amount of DNA available. A TaqMan probe should be ordered for this SNP to
allow genotyping over these sample sets.
208
the RA datasets (p = 0.138) alone. Although this SNP was only trending towards significance
in the combined dataset, it is possible that it is playing a role in the development of both
diseases. This study found no significant association of the DISC1 SNPs with SZ or BD in the
single datasets or with mental illness in the combined dataset.
4.1.2.3. Sex bias
Evidence from the literature suggests that there is potential for SNPs within DISC1 to display
sex bias, in particular towards females (Hennah et al., 2003). Therefore, although a significant
difference between the sexes was only detected for DISC1_rs9431714 and DISC1_rs4658966
all DISC1 SNPs were analysed over the separate male and female subsets (see section
3.1.5.4). In this analysis, only two SNPs, DISC1_rs9431714 and DISC1_rs872624, reached
significance in the female only subset and no SNPs were significantly associated with disease
in the male only subset. For DISC1_rs9431714 the minor allele was significantly (p = 0.0041)
susceptible to RA in the WTCCC RA dataset and significantly (p = 0.0055) protective against
SZ in the GAIN SZ dataset. This indicated that in females this SNP is having an opposite
effect in each disorder (see section 4.2.1.1). For DISC1_rs872624 the psychosis dataset was
significantly susceptible (p = 0.0413) to mental illness for females only. The females only
WTCCC RA dataset for this SNP was trending towards significance (p = 0.056) for protection
against RA, indicating a similar effect as seen for DISC1_rs9431714.
4.1.2.4. Summary
The significance of the three SNP haplotypes over the genotyped sample sets indicates that it
is likely DISC1 is involved in the development of RA. To increase the significance of this
result additional genotyping should be completed to increase the sample size. It is unlikely
that any of the DISC1 SNPs analysed in this thesis are associated with SZ except
DISC1_rs9431714. There is also potential for DISC1_rs872624 to be associated with
psychosis in general. Genotyping data from the GAIN and non GAIN SZ datasets should be
combined with other large GWAS studies to determine whether this potential association is
real.
209
4.1.3. LIMITATIONS OF THIS STUDY
In this thesis there are many instances where a significant association in the WTCCC data
could not be replicated in the NZ and UK RA sample sets. For example, DISC1_rs1130214
reached significance in the WTCCC (p=0.005), but not in NZRA (p=0.930 in NZRA), OXRA
(p=0.105) or UKRA (p=0.685). It is possible that this effect is due to the limited power of this
study, which can cause false-negative results. The WTCCC contains 1860 case subjects and
2938 control subjects compared with the 763 cases and 568 controls of the NZRA cohort.
This disparity is large and it has been shown that over 1000 cases and 1000 controls are
required to generate sufficient power to detect a risk ratio larger than 2 (Gregersen & Olsson,
2009). However, in complex disease there are many genes for susceptibility each with a much
weaker effect, usually OR=1.2-1.3. To detect odds ratios in this range sample sizes containing
three to four thousand individuals are necessary (Gregersen & Olsson, 2009). As the genes
investigated in this study have already been implicated in autoimmune pathways it is possible
that increasing the NZ cohort size will generate a significant association. It is also possible
that the association of these genes with RA or SZ is through processes not studied in this
thesis, such as copy number variation or epigenetics.
The issue of mutliple testing may also have had an effect on the results in this thesis. It is
possible that undergoing statistical analysis that corrects for multiple testing may have led to
the conculsion that the significant or near-significant findings were not valid. Therefore
further testing needs to be completed.
210
4.2. TWOHYPOTHESESFORTHENEGATIVECORROLATION
BETWEENRAANDSZ
A negative association between RA and SZ was first established over 50 years ago and has
been widely reported since. Recently, studies have addressed whether these two diseases have
a common disease pathway, with one study hypothesising that they share susceptibility alleles
(Watanabe et al., 2009). Watanabe et al. utilised case-control analysis to determine whether
known RA associated genes were also associated with SZ patients. They were unable to
provide evidence for the involvement of NFKBIL1, SLC22A4, RUNX1, FCRL3 or PADI4 in a
Japanese SZ sample set. In this thesis, potentially associated RA genes were genotyped firstly
in our NZ and UK sample sets, then investigated for a role in SZ using publically available
GWAS data.
As in the Watanabe et al. study, one aim of this thesis was to uncover an allelic variant
common to both diseases that could be responsible for this correlation. The initial hypothesis
favoured an allelic variant with a minor allele conferring susceptibility to one disorder and
protection against the other (hypothesis one). Throughout the course of this project and others
in the Merriman laboratory (Bellacosa et al., 1991) evidence was found to suggest that in
some genes there may be a shared minor allele for protection or susceptibility towards both
RA and SZ. This second school of thought is referred to here as hypothesis two. The two
hypotheses are compatible with each other.
4.2.1. HYPOTHESIS 1
As previously discussed, this study focused on the hypothesis that the minor allele of a shared
allelic variant would confer susceptibility to one disorder and protection against the other.
This hypothesis supports a shared pathway for the two diseases with intercellular activation of
a signalling network upstream of AKT1 (see Figure 4.84).
211
Figure 4.84: A schematic of a proposed theoretical model for the negative association between rheumatoid
arthritis and schizophrenia. Hypothesis 1 proposes that an allele which confers susceptibility in one disease will
confer protection in the other. This effect occurs intercellularly upstream of the AKT1 signalling pathway.
Hypothesis 2 proposes that an allele which confers susceptibility in one disease will also confer susceptibility in
the other and vice versa. This effect occurs intracellularly downstream of the AKT1 signalling pathway. (Taken
from Bellacosa et al., 1991)
4.2.1.1. Support for hypothesis 1
The data for SNPs investigated in this thesis was primarily taken from the publically available
WTCCC RA data. This information was compared with that from the GAIN SZ dataset and a
number of the SNPs initially selected (seven of fifteen SNPs) had an opposite effect in each
group (i.e. protective in WTCCC RA and susceptible in GAIN SZ or vice versa).
Four of these SNPs (AKT1_rs2494731, AKT1_rs4983386, DISC1_rs4658966 and
DISC1_rs872624) had the level of significance for the combined dataset reduced when GAIN
SZ was included with the RA dataset (see Table 5.82). The same effect was also seen for
DISC1_rs9431714 when the females were analysed separately. This provides supporting
evidence for the minor allele having a protective effect in one disorder and a susceptive effect
in the other. However, it may be that the addition of the non-significant GAIN SZ dataset is
naturally reducing the level of significance seen for the RA dataset alone. To test if these
minor alleles are truly illustrating an opposite effect in each disorder, the SNPs would need to
212
reach significance for both disease datasets separately. The combined dataset should show a
reduced significance compared to the two datasets alone.
Table 5.82: Supporting evidence for hypothesis one.
SNP
AKT1_rs2494731
AKT1_rs4983386
DISC1_rs4658966
DISC1_rs872624
RA datasets
p allelic
0.033
0.006
0.001
0.017
RA+SZ datasets
p allelic
0.152
0.087
0.020
0.160
Failure of these SNPs to provide significant evidence for hypothesis 1 may be due to
population heterogeneity (see section 4.3.3), a lack of power due to the small number of
individuals available for genotyping or hypothesis 1 may not be correct.
4.2.2. HYPOTHESIS 2
The non-significant findings of this thesis for the initial hypothesis provided support for a
second hypothesis. In hypothesis 2 it is proposed that the association between RA and SZ is
caused by a single allelic variant in a shared signalling pathway conferring susceptibility to or
protection against both diseases. This theory is based on intracellular activation of a signalling
network downstream of AKT1 (see Figure 4.87).
4.2.2.1. Support for hypothesis 2
Of the significant WTCCC RA SNPs selected for analysis in this thesis, eight were in the
same direction in both the RA and SZ reference datasets (i.e. protective in both WTCCC RA
and GAIN SZ or both susceptible). An increase in the significance of association when the
GAIN SZ data were analysed together with the combined RA data indicates that the minor
allele of the SNP is protective or susceptible for both diseases. This effect was seen for three
SNPs, NFATC1_rs2002311, NFATC2_rs8119787 and DISC1_rs9431714. For all SNPs, the
addition of the GAIN SZ dataset decreased the p value from that seen for the RA data alone
(Table 5.83). For the NFAT SNPs this decrease resulted in a significant association with
disease. For DISC1_rs9431714 the p-value only decreased to trending towards association.
213
Table 5.83: Supporting evidence for hypothesis two.
SNP
NFATC1_rs2002311
NFATC2_rs8119787
DISC1_rs9431714
RA datasets
p allelic
0.077
0.032
0.138
RA+SZ datasets
p allelic
0.046
0.008
0.053
Further investigation of SNPs within these genes may determine whether hypothesis 2
provides an accurate explanation of the effect seen here. A lack of power to detect small
effect sizes may be the reason why there is limited evidence to support hypothesis 2.
Population heterogeneity (see section 4.3.3) may also have played a role or hypothesis 2 may
be incorrect.
214
4.3. METAͲANALYSISOFSNPSFROMSZCANDIDATEGENES
INTHELITERATURE
Genes of interest from this study (AKT1, NFAT and DISC1) were investigated in the SZ
literature for significant association with SZ through meta-analysis. This was to provide
additional evidence for a shared biological pathway for RA and SZ. DISC1 has been the focus
of many genetic studies in the SZ literature. There were also a number of AKT1 studies.
However, NFAT did not produce any relevant SZ papers in the literature database search and
was excluded from this analysis.
4.3.1. AKT1
The three SNPs meta-analysed from the literature were not significant when all data sets were
combined. Although not significant, all SNPs had an OR indicating protection for SZ
(rs1130214 OR = 0.99, rs2498804 OR = 0.96, rs3803304 OR = 0.95). When RA datasets
were added to the meta-analysis, the OR of the combined sample set changed to >1 in all
three SNPs (rs1130214 OR = 1.03, rs2498804 OR = 1.03, rs3803304 OR = 1.02). This
indicates that the minor alleles for these SNPs may be conferring protection to SZ and
susceptibility to RA. However without significant p- values it is not possible to support this
statement with the data produced in this thesis. There were also no significant results when
these SNPs were analysed separately for Asian and Caucasian ancestry.
4.3.2. DISC1
Seven SNPs were meta-analysed from the published SZ literature. All SNPs for this gene had
similar combined dataset ORs regardless of whether the WTCCC data were included or not
(i.e. both >1 or both <1). This indicates that the minor allele of these SNPs may be
consistently conferring protection or susceptibility to both diseases. As for AKT1 above, it is
difficult to support this statement without significant p-values. Only two SNPs were
significant in this analysis (rs3738401 and rs821587) and neither were significant for both the
combined dataset excluding WTCCC and the combined dataset including WTCCC.
Rs3738401 was significant (p = 0.003) when WTCCC was included in the combined dataset
215
but not significant when it was excluded (p = 0.484). Yet rs821587 was only significant when
WTCCC was excluded (p = 0.025, with WTCCC included p = 0.222).
Three SNPs,
rs821616, rs3738401 and rs843979, were analysed for Caucasian and Asian ancestry
separately. However none of these were significant.
Population heterogeneity was an issue when undertaking meta-analysis. Combining sample
sets is an effective way to increase statistical power and this analysis was performed on both
genotyped and literature review SNPs in this thesis. The fixed effects model used in the metaanalysis assumes that all variation between the sample sets is due to random variation, and
SNPs with a heterogeneity value of greater than 0.05 are considered to conform to this
assumption (i.e. they are homogenous). SNPs with a result of less than 0.05 have an
underlying difference between the populations that could influence the meta-analysis results.
These SNPs were analysed again using the random effects meta-analysis model which
incorporates potential population differences when weighting the sample sets.
One potential difference between the sample sets is ancestral background (Begni et al., 2002).
All participants were screened for inclusion based on Caucasian ancestry, yet there may be
significant differences between WTCCC and NZRA and the UK sample sets. Two approaches
are currently available for addressing the population stratification issue: family based control
approach, where cases are matched with controls from the same family, and the genomic
control approach, when the population is analysed for stratification using unlinked markers
and the meta-results adjusted accordingly (Beekman et al., 2004). As these populations are
post recruitment the first option is not viable. The WTCCC study has been analysed for
population structure using the marker approach and they concluded that once non-European
recent migrants are excluded, the effect on their association results is small (WTCCC, 2007).
Therefore it is suggested that similar population stratification markers are developed for the
RA sample sets.
When considering the diversity geographic distribution of the sample sets it is apparent that
there is also potential for different environmental factors to play a role. Gene-environment
interactions may be responsible for the inconsistence in results between sample groups
216
(Beekman et al., 2004). To determine the effect of environmental factors on the development
of RA, a history of exposure risk for each individual should be included with the
epidemiological information.
217
4.4. INDUCTIONOFINFLAMMATORYARTHRITISINMICE
Two models were assessed in this thesis, serum transfer and spontaneous IA. Both models
take advantage of the spontaneously arthritic progeny (K/BxN) of a KRN and NOD cross.
The serum transfer method was developed from the discovery by Kouskoff et al. that IA can
develop spontaneously in mice with a specific T cell receptor transgene and MHC complex
(Kouskoff et al., 1996). Later it was found that IA can be induced in healthy mice injected
with serum or IgG from K/BxN mice (Maccioni et al., 2002). This model is not dependant on
the strain of the recipient mouse and therefore it is less resource intensive than the
spontaneous model. In this thesis, the serum transfer method failed to induce IA in both test
and control animals therefore the spontaneous IA method was required.
The spontaneous IA method required backcross breeding of several generations of knockout
mice to the NOD strain. Once the knockouts have a sufficient amount of genomic NOD DNA
they can be crossed with KRN mice to produce K/BxN progeny containing the knockout. The
B6/NOD assay in this thesis was utilised to identify animals with a high percentage of NOD
genomic DNA for breeding stock. At the completion of this thesis the model was yet to be
tested.
It is possible that the failure of the serum transfer method was due to a lack of IgG in the
serum caused by a lack of exposure to pathogens in the environment. In a previous study,
failure of the model to induce IA in recipient mice was attributed to an insufficient
concentration of IgG in the injected serum (Begovich et al., 2007b). The low titre may be due
to a number of reasons. Firstly, it has been found that over time a serum producing colony
may produce less-arthritogenic serum. In addition, anti-IgG antibodies have a reduced halflife in the recipient mice (Begovich et al., 2007a). Lastly, the pathogen free environment in
which the animals are contained is responsible for the amount of antibodies generated. In a
previous study it was noted that different animal containment facilities produced different
titres of serum, with more stringent conditions leading to a 5 fold reduction in IgG serum
concentration than facilities with more relaxed conditions (Monach et al., 2007). These
218
factors may have had a role in the present study and thus prevented the development of IA in
the controls. In future the IgG titre of the serum should be checked by measuring the anti-GPI
antibodies via ELISA to ensure that it reaches adequate levels for induction. The breeding
animals which consistently produce offspring with low IgG titre should be removed from the
colony. It is also suggested that the animals be exposed to pathogens or allergens in a
controlled environment to assist in the production of antibodies.
219
4.5. MICROSATELLITEASSAYFORDIFFERENTATIONBETWEEN
GENOMICDNAFROMMOUSESTRAINSB6AND129
An assay was prepared to establish differences in the percentage of genomic B6 and 129
DNA contained in animals being segregated for breeding. 57 microsatellites that were
informative between these two strains were identified. A second assay was prepared for B6
and NOD utilising microsatellites from the first. 47 microsatellites were informative between
the strains for this assay. Nearly half of these microsatellites’ primers failed to produce PCR
product for the two test animals. Of the microsatellites that worked over 90% were
representative of NOD genomic DNA (test mouse 2 = 95%, test mouse 5 = 92%).
Many of the microsatellites did not produce PCR product for the test samples in the B6/NOD
assay yet did produce product for the B6 and NOD reference samples. All microsatellites
were originally titrated using B6 and NOD DNA extracted from liver samples. The poor
quality of the DNA extracted from the tail tip samples of the mice was most likely the reason
the PCR did not work for nearly half of the microsatellites. As the mice are undergoing
testing for identification of breeding animals for the IA models, it is not possible to extract
DNA from liver samples. Another protocol for DNA extraction is required for use in the
microsatellite assay. One option is the ear clip extraction method.
220
4.6. SUMMARYANDFUTURERESEARCH
4.6.1. HUMAN
Genotyping DISC1 across the NZ RA sample sets provided some evidence for the
involvement of this gene in the development of RA. There was supporting evidence for the
involvement of two independent AKT1loci in RA. NFATC1 and NFATC2 may be playing a
role in the link between RA and SZ with the minor allele of SNPs in these genes conferring
protection against or susceptibility to both diseases.
4.6.2. MOUSE
The serum transfer model was not effective at inducing IA in this study. The microsatellite
assays developed in this thesis can successfully be used to determine strain differences in the
genomic DNA of mice. This assay will be key in identifying the animals for breeding the
knockouts against the NOD background.
4.6.3. FUTURE RESEARCH
The following list details suggestions for potential studies to complement the results of this
thesis:
1. More participants should be recruited to increase the NZ and UK RA cohort sizes.
This would increase the power and therefore the probability of replicating
associations.
2. Statistical analysis which corrects for mutliple testing should be undertaken in all
future studies.
3. Deep sequencing may be utilised to identify novel genetic variants that can be
genotyped to further refine associations.
4. Copy number variation and epigenetic processes could be investigated for the SNPs
221
not found to be associated with RA in this thesis.
5. Other autoimmune and neurodevelopmental pathways should be investigated to
discover genes with a role in both disease types. For example, the hormone systems
used by the CNS to signal the immune system or immune mediators and cytokines
utilised by the immune system to signal the CNS. Both these pathways could be
scanned for genes showing an association to RA in the NZ and UK sample sets.
6. DISC1 SNPs with association to RA or SZ in the GWAS literature should be
genotyped over the RA sample sets to provide a more comprehensive coverage of the
gene.
7. The DISC1, AKT1 and NFAT SNPs genotyped in this thesis should be genotyped over
other autoimmune disease sample sets to establish is there is a broad association with
these types of diseases.
8. Testing hypothesis 1 – further SNPs within and surrounding AKT1 should be selected
based on an opposite effect in RA and SZ.
9. Testing hypothesis 2 – Additional NFAT SNPs should be selected to develop a
haplotype for testing this hypothesis.
10. The pathogen free environment that the transgenic mice are housed in should be
altered to allow uptake of IA on serum transfer. Controlled exposure to allergens or
pathogens could be used to achieve an inflammatory response.
11. There should be an addition of more microsatellites to the B6/129 assay to allow
distinction between genomes to a closer degree.
222
References
REFERENCES
Ahlqvist E, Hultqvist M, Holmdahl R (2009). The value of animal models in predicting
genetic susceptibility to complex diseases such as rheumatoid arthritis. Arthritis Res Ther
11(3): 226.
Ahmad Y, Bhatia M, Mediratta P, Sharma K, Negi H, Chosdol K, Sinha S (2009).
Association between the ionotropic glutamate receptor kainate3 (GRIK3) Ser310Ala
polymorphism and schizophrenia in the Indian population. The world journal of biological
psychiatry: the official journal of the World Federation of Societies of Biological Psychiatry:
1.
Aho K, Palosua T, Raunio V, Puska P, Aromaa A, Salonen JT (1985). When does rheumatoid
disease start? Arthritis and rheumatism 28(5): 485-489.
Alamanos Y, Voulgari PV, Drosos AA (2006). Incidence and Prevalence of Rheumatoid
Arthritis, Based on the 1987 American College of Rheumatology Criteria: A Systematic
Review. Seminars in Arthritis and Rheumatism 36: 182-188.
Aleman A, Kahn R, Selten J (2003). Sex differences in the risk of schizophrenia: evidence
from meta-analysis. Archives of General Psychiatry 60(6): 565.
Allardyce J, Morrison G, McCreadie R, VAN O (2000). Schizophrenia is not disappearing in
south-west Scotland. The British Journal of Psychiatry 177(1): 38.
Allebeck P, Rodvall Y, Wistedt B (1985). Incidence of rheumatoid arthritis among patients
with schizophrenia, affective psychosis and neurosis. Acta Psychiatrica Scandinavica 71(6):
615-619.
American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental
Disorders. American Psychiatric Association.
Arend WP (1997). The Pathophysiology and Treatment of Rheumatoid Arthritis. Arthritis and
rheumatism 40(4): 595-597.
Arend WP, Dayer JM (1995). Inhibition of the production and effects of interleukin-1 and
tumor necrosis factor alpha in rheumatoid arthritis. Arthritis Rheum 38(2): 151-160.
Arinami T, Ohtsuki T, Ishiguro H, Ujike H, Tanaka Y, Morita Y, Mineta M, Takeichi M,
Yamada S, et al. (2005). Genomewide high-density SNP linkage analysis of 236 Japanese
families supports the existence of schizophrenia susceptibility loci on chromosomes 1p, 14q,
and 20p. The American Journal of Human Genetics 77(6): 937-944.
223
References
Ariza-Ariza R, Mestanza-Peralta M, Cardiel M (1998). Omega-3 fatty acids in rheumatoid
arthritis: an overview. Seminars in Arthritis and Rheumatism 27: 366-370.
Arnett F, Edworthy S, Bloch D, McShane D, Fries J, Cooper N, Healey L, Kaplan S, Liang
M, et al. (1988). The American Rheumatism Association 1987 revised criteria for the
classification of rheumatoid arthritis. Arthritis & Rheumatism 31(3): 315-324.
Arnett WD, Rosner JL (1987). Neutrino mass limits from SN1987A. Phys Rev Lett 58(18):
1906-1909.
Arseneault L, Cannon M, Poulton R, Murray R, Caspi A, Moffitt T (2002). Cannabis use in
adolescence and risk for adult psychosis: longitudinal prospective study. British Medical
Journal 325(7374): 1212.
Asquith D, Miller A, McInnes I, Liew F (2009). Autoimmune disease: Rheumatoid arthritis.
Eur. J. Immunol 39: 1991-2058.
Bajestan S, Sabouri A, Nakamura M, Takashima H, Keikhaee M, Behdani F, Fayyazi M,
Sargolzaee M, Bajestan M, et al. (2006). Association of AKT1 haplotype with the risk of
schizophrenia in Iranian population. AMERICAN JOURNAL OF MEDICAL GENETICS.
PART B 141(4): 383.
Balandraud N, Roudier J, Roudier C (2004). Epstein–Barr virus and rheumatoid arthritis.
Autoimmunity Reviews 3(5): 362-367.
Baldwin JA (1980). Schizophrenia and physical disease: a preliminary analysis of the data
from the Oxford Record Linkage Study. The Biochemistry of Schizophrenia and Addiction: In
Search of a Common Factor. Edited by Hemmings G. Lancaster, England, MTP.
Balsa A, Minaur NJ, Pascual-Salcedo D, McCabe C, Balas A, Fiddament B, Vicario JL, Cox
NL, Martin-Mola E, et al. (2000). Class II MHC antigens in early rheumatoid arthritis in Bath
(UK) and Madrid (Spain). Rheumatology 39(8): 844.
Banks J, Banks C, Cheong B, Umachandran V, Smith A, Jessop J, Pritchard M (1992). An
epidemiological and clinical investigation of pulmonary function and respiratory symptoms in
patients with rheumatoid arthritis. QJM 85(2-3): 795.
Baron J (1996). Beneficial effects of nicotine and cigarette smoking: the real, the possible and
the spurious. British medical bulletin 52(1): 58.
Barrett J, Brennan P, Fiddler M, Silman A (1999). Clinical Science-Does Rheumatoid
Arthritis Remit During Pregnancy and Relapse Postpartum? Results from a Nationwide Study
in the United Kingdom Performed Prospectively from Late Pregnancy. Arthritis and
Rheumatism 42(6): 1219-1227.
224
References
Barton A, Eyre S, Ke X, Hinks A, Bowes J, Flynn E, Martin P (2009). Identification of
AF4/FMR2 family, member 3 (AFF3) as a novel rheumatoid arthritis susceptibility locus and
confirmation of two further pan-autoimmune susceptibility genes. Human Molecular Genetics
18(13): 2518.
Barton A, Thomson W, Ke X, Eyre S, Hinks A, Bowes J, Gibbons L, Plant D (2008a). Reevaluation of putative rheumatoid arthritis susceptibility genes in the post-genome wide
association study era and hypothesis of a key pathway underlying susceptibility. Human
Molecular Genetics 17(15): 2274.
Barton A, Thomson W, Ke X, Eyre S, Hinks A, Bowes J, Plant D, Gibbons L, Wilson A, et
al. (2008b). Rheumatoid arthritis susceptibility loci at chromosomes 10p15, 12q13 and 22q13.
Nature genetics 40(10): 1156-1159.
Barton A, Worthington J (2009). Genetic susceptibility to rheumatoid arthritis: an emerging
picture. Arthritis Rheum 61(10): 1441-1446.
Beekman JM, Bakema JE, van de Winkel JG, Leusen JH (2004). Direct interaction between
FcgammaRI (CD64) and periplakin controls receptor endocytosis and ligand binding
capacity. Proc Natl Acad Sci US A 101(28): 10392-10397.
Begni S, Popoli M, Moraschi S, Bignotti S, Tura G, Gennarelli M (2002). Association
between the ionotropic glutamate receptor kainate 3 (GRIK3) ser310ala polymorphism and
schizophrenia. Molecular psychiatry 7(4): 416.
Begovich A, Carlton V, Honigberg L, Schrodi S, Chokkalingam A, Alexander H, Ardlie K,
Huang Q, Smith A, et al. (2004). A missense single-nucleotide polymorphism in a gene
encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis.
The American Journal of Human Genetics 75(2): 330-337.
Begovich AB, Chang M, Caillier SJ, Lew D, Catanese JJ, Wang J, Hauser SL, Oksenberg JR
(2007a). The autoimmune disease-associated IL12B and IL23R polymorphisms in multiple
sclerosis. Hum Immunol 68(11): 934-937.
Begovich AB, Chang M, Schrodi SJ (2007b). Meta-analysis evidence of a differential risk of
the FCRL3 -169T-->C polymorphism in white and East Asian rheumatoid arthritis patients.
Arthritis Rheum 56(9): 3168-3171.
Bellacosa A, Staal SP, Tsichlis PN (1991). A retroviral oncogene, akt, encoding a serinethreonine kinase containing an SH2-like region. Science 254(5029): 274-277.
Bellamy N, Duffy D, Martin N, Mathews J (1992). Rheumatoid arthritis in twins: a study of
aetiopathogenesis based on the Australian Twin Registry. British Medical Journal 51(5): 588.
225
References
Bendele A (2001). Animal models of rheumatoid arthritis. J Musculoskelet Neuronal Interact
1(4): 377-385.
Berlin JA, Laird NM, Sacks HS, Chalmers TC (2006). A comparison of statistical methods
for combining event rates from clinical trials. Statistics in Medicine 8(2): 141-151.
Bero L, Rennie D (1995). The Cochrane Collaboration: preparing, maintaining, and
disseminating systematic reviews of the effects of health care. Jama 274(24): 1935.
Bersani G, Orlandi V, Kotzalidis GD, Pancheri P (2002). Cannabis and schizophrenia: impact
on onset, course, psychopathology and outcomes. European Archives of Psychiatry and
Clinical Neuroscience 252(2): 86-92.
Bijlsma J, Huber-Bruning O, Thijssen J (1987). Effect of oestrogen treatment on clinical and
laboratory manifestations of rheumatoid arthritis. British Medical Journal 46(10): 777.
Blackwood D, Fordyce A, Walker M, St. Clair D, Porteous D, Muir W (2001). Schizophrenia
and affective disordersócosegregation with a translocation at chromosome 1q42 that directly
disrupts brain-expressed genes: clinical and P300 findings in a family. The American Journal
of Human Genetics 69(2): 428-433.
Bond C, Cleland LG (1996). Rheumatoid arthritis: Are pets implicated in its etiology?
Seminars in Arthritis and Rheumatism 25: 308-317.
Bray N, Preece A, Williams N, Moskvina V, Buckland P, Owen M, O'Donovan M (2005).
Haplotypes at the dystrobrevin binding protein 1 (DTNBP1) gene locus mediate risk for
schizophrenia through reduced DTNBP1 expression. Human Molecular Genetics 14(14):
1947.
Brenner G, Melamed B, Panush R (1994). Optimism and coping as determinants of
psychosocial adjustment to rheumatoid arthritis. Journal of Clinical Psychology in Medical
Settings 1(2): 115-134.
Brinkman B, Huizinga T, Kurban S, Van der Velde E, Schreuder G, Hazes J, Breedveld F,
Verweij C (1997). Tumour necrosis factor alpha gene polymorphisms in rheumatoid arthritis:
association with susceptibility to, or severity of, disease? Rheumatology 36(5): 516.
Brown A, Begg M, Gravenstein S, Schaefer C, Wyatt R, Bresnahan M, Babulas V, Susser E
(2004). Serologic evidence of prenatal influenza in the etiology of schizophrenia. Archives of
General Psychiatry 61(8): 774.
Brown A, Cohen P, Greenwald S, Susser E (2000). Nonaffective psychosis after prenatal
exposure to rubella. American Journal of Psychiatry 157(3): 438.
226
References
Brown A, Schaefer C, Wyatt R, Begg M, Goetz R, Bresnahan M, Harkavy-Friedman J,
Gorman J, Malaspina D, et al. (2002). Paternal age and risk of schizophrenia in adult
offspring. American Journal of Psychiatry 159(9): 1528.
Bühler B, Hambrecht M, Löffler W, an der Heiden W, Häfner H (2002). Precipitation and
determination of the onset and course of schizophrenia by substance abuse—a retrospective
and prospective study of 232 population-based first illness episodes. Schizophrenia Research
54(3): 243-251.
Byrne M, Agerbo E, Ewald H, Eaton W, Mortensen P (2003). Parental age and risk of
schizophrenia: a case-control study. Archives of general psychiatry 60(7): 673.
Camargo L, Collura V, Rain J, Mizuguchi K, Hermjakob H, Kerrien S, Bonnert T, Whiting P,
Brandon N (2006). Disrupted in Schizophrenia 1 Interactome: evidence for the close
connectivity of risk genes and a potential synaptic basis for schizophrenia. Molecular
psychiatry 12(1): 74-86.
Cannon M, Jones P, Huttunen M, Tanskanen A, Huttunen T, Rabe-Hesketh S, Murray R
(1999). School performance in Finnish children and later development of schizophrenia: a
population-based longitudinal study. Archives of general psychiatry 56(5): 457.
Cannon M, Jones P, Murray R (2002). Obstetric complications and schizophrenia: historical
and meta-analytic review. American Journal of Psychiatry 159(7): 1080.
Cannon T, Hennah W, van Erp T, Thompson P, Lonnqvist J, Huttunen M, Gasperoni T,
Tuulio-Henriksson A, Pirkola T, et al. (2005). Association of DISC1/TRAX haplotypes with
schizophrenia, reduced prefrontal gray matter, and impaired short-and long-term memory.
Archives of general psychiatry 62(11): 1205.
Cantor-Graae E, Selten J (2005). Schizophrenia and migration: a meta-analysis and review.
American Journal of Psychiatry 162(1): 12.
Cantorna M (2000). Vitamin D and autoimmunity: is vitamin D status an environmental
factor affecting autoimmune disease prevalence? Experimental Biology and Medicine 223(3):
230.
Cantorna M, Hayes C, DeLuca H (1998). 1, 25-Dihydroxyvitamin D3 prevents and
ameliorates symptoms in two experimental model of human arthritis. J Nutr 128(1): 68-72.
Capecchi MR (1989). The new mouse genetics: altering the genome by gene targeting. Trends
in Genetics 5: 70-76.
Cardno AG, Marshall EJ, Coid B, Macdonald AM, Ribchester TR, Davies NJ, Venturi P,
Jones LA, Lewis SW, et al. (1999). Heritability Estimates for Psychotic Disorders The
Maudsley Twin Psychosis Series Vol. 56, pp 162-168: Am Med Assoc.
227
References
Cash JM, Klippel JH (1994). Second-Line Drug Therapy for Rheumatoid Arthritis. New
England Journal of Medicine 330(19): 1368-1375.
Castro FA, Haimila K, Sareneva I, Schmitt M, Lorenzo J, Kunkel N, Kumar R, Försti A,
Kjellberg L, et al. (2009). Association of HLA-DRB1, interleukin-6 and cyclin D1
polymorphisms with cervical cancer in the Swedish population-A candidate gene approach.
International journal of cancer. Journal international du cancer.
Chang M, Rowland C, Garcia V, Schrodi S, Catanese J, van der Helm-van A (2008). A largescale rheumatoid arthritis genetic study identifies association at chromosome 9q33. 2. PLoS
Genetics 4(6).
Chen C, Lin S, Sham P, Ball D, Loh E, Hsiao C, Chiang Y, Ree S, Lee C, et al. (2003). Premorbid characteristics and co-morbidity of methamphetamine users with and without
psychosis. Psychological Medicine 33(08): 1407-1414.
Chen Q, Chen Q, Feng G, Lindpaintner K, Wang L, Chen Z, Gao Z, Tang J, Huang G, et al.
(2007). Case-control association study of Disrupted-in-Schizophrenia-1 (DISC1) gene and
schizophrenia in the Chinese population. Journal of psychiatric research 41(5): 428-434.
Choi SJ, Rho YH, Ji JD, Song GG, Lee YH (2006). Genome scan meta-analysis of
rheumatoid arthritis. Rheumatology 45(2): 166-170.
Chowdari K, Mirnics K, Semwal P, Wood J, Lawrence E, Bhatia T, Deshpande S, BK T,
Ferrell R, et al. (2002). Association and linkage analyses of RGS4 polymorphisms in
schizophrenia. Human Molecular Genetics 11(12): 1373.
Choy EHS, Panayi GS (2001). Cytokine Pathways and Joint Inflammation in Rheumatoid
Arthritis. New England Journal of Medicine 344(12): 907-916.
Chrousos G (1995). The hypothalamic-pituitary-adrenal axis and immune-mediated
inflammation. The New England journal of medicine 332(20): 1351.
Cimmino M, Parisi M, Moggiana G, Mela G, Accardo S (1998). Prevalence of rheumatoid
arthritis in Italy: the Chiavari study. British Medical Journal 57(5): 315.
Citera G, Padulo LA, Fernandez G, Lazaro MA, Rosemffet MG, Maldonado Cocco JA
(2001). Influence of HLA-DR alleles on rheumatoid arthritis: susceptibility and severity in
Argentine patients. The Journal of Rheumatology 28(7): 1486.
Clair D, Blackwood D, Muir W, Walker M, Carothers A, Spowart G, Gosden C, Evans H
(1990). Association within a family of a balanced autosomal translocation with major mental
illness. The Lancet 336(8706): 13-16.
228
References
Collins R, Keech A, Peto R, Sleight P, Kjekshus J, Wilhelmsen L, MacMahon S, Shaw J,
Simes J, et al. (1992). Cholesterol and total mortality: need for larger trials. British Medical
Journal 304(6843): 1689.
Cooper GS, Stroehla BC (2003). The epidemiology of autoimmune diseases. Autoimmunity
Reviews 2(3): 119-125.
D'Haens G, Baert F, Van Assche G, Caenepeel P, Vergauwe P, Tuynman H, De Vos M, van
Deventer S, Stitt L, et al. (2008). Early combined immunosuppression or conventional
management in patients with newly diagnosed Crohn's disease: an open randomised trial. The
Lancet 371(9613): 660-667.
Dalman C, Allebeck P (2002). Paternal age and schizophrenia: further support for an
association. American Journal of Psychiatry 159(9): 1591.
Dalman C, Allebeck P, Cullberg J, Grunewald C, Koster M (1999). Obstetric complications
and the risk of schizophrenia: a longitudinal study of a national birth cohort. Archives of
General Psychiatry 56(3): 234.
David A, Malmberg A, Brandt L, Allebeck P, Lewis G (1997). IQ and risk for schizophrenia:
a population-based cohort study. Psychological Medicine 27(06): 1311-1323.
Davies G, Welham J, Chant D, Torrey E, McGrath J (2003). A systematic review and metaanalysis of Northern Hemisphere season of birth studies in schizophrenia. Schizophrenia
bulletin 29: 587-593.
Davis R (2007). Fc receptor-like molecules. Immunology 25: 525-560.
Del Rincón I, Escalante A (2001). HLA-DRB1 alleles associated with susceptibility or
resistance to rheumatoid arthritis, articular deformities, and disability in Mexican Americans.
Arthritis & Rheumatism 42(7): 1329-1338.
Delgado G, GarcÌa L (1979). Reactivity of the intradermal test with toxoplasmosis in
schizophrenic patients. Revista cubana de medicina tropical 31(3): 225.
DerSimonian R, Laird N (1986). Meta-analysis in clinical trials. Controlled clinical trials
7(3): 177-188.
Dick D, Foroud T, Flury L, Bowman E, Miller M, Rau N, Moe P, Samavedy N, El-Mallakh
R, et al. (2003). Genomewide linkage analyses of bipolar disorder: a new sample of 250
pedigrees from the National Institute of Mental Health Genetics Initiative. The American
Journal of Human Genetics 73(1): 107-114.
Dickersin K, Berlin JA (1992). Meta-analysis: state-of-the-science. Epidemiologic Reviews
14(1): 154.
229
References
Ditzel H (2004). The K/BxN mouse: a model of human inflammatory arthritis. Trends in
Molecular Medicine 10(1): 40-45.
Djurovic S, K‰hler A, Kulle B, Jˆnsson E, Agartz I, Le Hellard S, Hall H, Jakobsen K,
Hansen T, et al. (2009). A possible association between schizophrenia and GRIK3
polymorphisms in a multicenter sample of Scandinavian origin (SCOPE). Schizophrenia
Research 107(2-3): 242-248.
Done D, Crow T, Johnstone E, Sacker A (1994). Childhood antecedents of schizophrenia and
affective illness: social adjustment at ages 7 and 11. British Medical Journal 309(6956): 699.
Downward J (2004). PI 3-kinase, Akt and cell survival. Seminars in Cell and Developmental
Biology 15: 177-182.
Drosos A, Lanchbury J, Panayi G, Moutsopoulos H (1992). Rheumatoid arthritis in Greek and
British patients. A comparative clinical, radiologic, and serologic study. Arthritis Rheum 35:
745-748.
Dubucquoi S, Solau-Gervais E, Lefranc D, Marguerie L, Sibilia J, Goetz J, Dutoit V,
Fauchais AL, Hachulla E, et al. (2004). Evaluation of anti-citrullinated filaggrin antibodies as
hallmarks for the diagnosis of rheumatic diseases. Annals of the rheumatic diseases 63(4):
415.
Duerr R, Taylor K, Brant S, Rioux J, Silverberg M, Daly M, Steinhart A, Abraham C,
Regueiro M, et al. (2006). A genome-wide association study identifies IL23R as an
inflammatory bowel disease gene. Science's STKE 314(5804): 1461.
Durie BGM, Van Ness B, Ramos C, Stephens O, Haznadar M, Hoering A, Haessler J, Katz
MS, Mundy GR, et al. (2009). Genetic polymorphisms of EPHX1, Gsk3 , TNFSF8 and
myeloma cell DKK-1 expression linked to bone disease in myeloma. Leukemia 23(10): 19131919.
Easterbrook PJ, Gopalan R, Berlin JA, Matthews DR (1991). Publication bias in clinical
research. The Lancet 337(8746): 867-872.
Eaton WW, Byrne M, Ewald H, Mors O, Chen CY, Agerbo E, Mortensen PB (2006).
Association of schizophrenia and autoimmune diseases: linkage of Danish national registers.
American Journal of Psychiatry 163(3): 521.
Edwards T, Wang X, Chen Q, Wormly B, Riley B, O'Neill F, Walsh D, Ritchie M, Kendler
K, et al. (2008). Interaction between interleukin 3 and dystrobrevin-binding protein 1 in
schizophrenia. Schizophrenia Research 106(2-3): 208-217.
230
References
Egan M, Goldberg T, Kolachana B, Callicott J, Mazzanti C, Straub R, Goldman D,
Weinberger D (2001). Effect of COMT Val108/158 Met genotype on frontal lobe function
and risk for schizophrenia. Proceedings of the National Academy of Sciences of the United
States of America 98(12): 6917.
Egger M, Smith GD (1998). Meta-analysis bias in location and selection of studies. British
Medical Journal 316(7124): 61.
Egger M, Smith GD (1997). Meta-analysis: potentials and promise. British Medical Journal
315(7119): 1371.
Egger M, Smith GD, Phillips AN (1997). Meta-analysis: principles and procedures. British
Ekelund J, Hennah W, Hiekkalinna T, Parker A, Meyer J, Lˆnnqvist J, Peltonen L (2004).
Replication of 1q42 linkage in Finnish schizophrenia pedigrees. Molecular psychiatry 9(11):
1037-1041.
Elwood P (2006). The first randomized trial of aspirin for heart attack and the advent of
systematic overviews of trials. JRSM 99(11): 586.
Elwood PC, Cochrane AL, Burr ML, Sweetnam PM, Williams G, Welsby E, Hughes SJ,
Renton R (1974). A randomized controlled trial of acetyl salicyclic acid in the secondary
prevention of mortality from myocardial infarction. British Medical Journal 1(5905): 436.
Emery P, Zeidler H, Kvien T, Guslandi M, Naudin R, Stead H, Verburg K, Isakson P,
Hubbard R, et al. (1999). Celecoxib versus diclofenac in long-term management of
rheumatoid arthritis: randomised double-blind comparison. The Lancet 354(9196): 21062111.
ERAS Study Group (2000). Socioeconomic Deprivation and Rheumatoid Disease: What
Lessons for the Health Service? Annals of the Rheumatic Diseases 59: 794-799.
Escribano C, Delgado-Martin C, Rodríguez-Fernández JL (2009). CCR7-Dependent
Stimulation of Survival in Dendritic Cells Involves Inhibition of GSK3. The Journal of
Immunology 183: 0000-0000.
Fan L, Wang W, Wang Q, Zong M, Yang L, Zhang H, Sun L, Lu T, Han J (2008). A
functional haplotype and expression of the PADI4 gene associated with increased rheumatoid
arthritis susceptibility in Chinese. Tissue Antigens 72(5): 469-473.
Farago B, Talian G, Komlosi K, Nagy G, Berki T, Gyetvai A, Szekanecz Z, Nyarady Z, Kiss
C, et al. (2009). Protein tyrosine phosphatase gene C1858T allele confers risk for rheumatoid
arthritis in Hungarian subjects. Rheumatology International 29(7): 793-796.
231
References
Fernandez L, Mendoza JL, Martinez A, Urcelay E, Fernandez-Arquero M, Garcia-Paredes J,
Peña AS, Diaz-Rubio M, de la Concha EG (2004). IBD1 and IBD3 determine location of
Crohn's disease in the Spanish population. Inflammatory bowel diseases 10(6): 715-722.
Firestein GS (2003). Evolving concepts of rheumatoid arthritis. Nature 423: 356-361.
Fournier M, De Ridder D, Bensing J (2002). Optimism and adaptation to chronic disease: The
role of optimism in relation to self-care options of type 1 diabetes mellitus, rheumatoid
arthritis and multiple sclerosis. British journal of health psychology 7(4): 409-432.
Funke B, Finn C, Plocik A, Lake S, DeRosse P, Kane J, Kucherlapati R, Malhotra A (2004).
Association of the DTNBP1 locus with schizophrenia in a US population. The American
Journal of Human Genetics 75(5): 891-898.
Gabriel SE (2001). The Epidemiology of Rheumatoid Arthritis. Rheumatic Disease Clinics of
NOrth America 27(2): 269-281.
Ganguli R, Brar JS, Roy Chengappa KN, Yang ZW, Nimgaonkar VL, Rabin BS (1993).
Autoimmunity in schizophrenia: a review of recent findings. Annals of medicine 25(5): 489496.
Gattaz W, Abrah„o A, Foccacia R (2004). Childhood meningitis, brain maturation and the
risk of psychosis*. European archives of psychiatry and clinical neuroscience 254(1): 23-26.
(2000). Toxoplasma Antibody Titers in Treatment Naive First Episodes of Schizophrenia. The
Symposium on Toxoplasma Infection and Brain Disease; November 16th; Annapolis, MD.
Glass GV (1977). 9: Integrating Findings: The Meta-Analysis of Research. Review of
research in education 5(1): 351.
Glass GV (1976). Primary, secondary, and meta-analysis of research. Educational researcher
5(10): 3.
Goberdhan DCI, Wilson C (2003). PTEN: tumour suppressor, multifunctional growth
regulator and more. Human Molecular Genetics 12(Review Issue 2): R239.
Gorin A, Smyth J, Weisberg J, Affleck G, Tennen H, Urrows S, Stone A (1999). Rheumatoid
arthritis patients show weather sensitivity in daily life, but the relationship is not clinically
significant. Pain 81(1-2): 173-177.
Gottesman, II Schizophrenia Genesis: The Origins of Madness (1991) New York. NY:
Freeman.
232
References
Green E, Raybould R, Macgregor S, Gordon-Smith K, Heron J, Hyde S, Grozeva D,
Hamshere M, Williams N, et al. (2005). Operation of the schizophrenia susceptibility gene,
neuregulin 1, across traditional diagnostic boundaries to increase risk for bipolar disorder.
Archives of general psychiatry 62(6): 642.
Gregersen P (2005). Gaining insight into PTPN22 and autoimmunity. Nature genetics 37(12):
1300-1301.
Gregersen P, Olsson L (2009). Recent advances in the genetics of autoimmune disease.
Annual review of immunology 27: 363-391.
Gregersen PK, Lee HS, Batliwalla F, Begovich AB (2006). PTPN22: Setting thresholds for
autoimmunity. Seminars in Immunology 18(4): 214-223.
Gregersen PK, Silver J, Winchester RJ (1987). The shared epitope hypothesis: an approach to
understanding the molecular genetics of susceptibility to rheumatoid arthritis. Arthritis and
rheumatism 30(11): 1205-1213.
Grégoire G, Derderian F, Le Lorier J (1995). Selecting the language of the publications
included in a meta-analysis: is there a Tower of Babel bias? Journal of clinical epidemiology
48(1): 159-163.
Grootenboer-Mignot S, Nicaise-Roland P, Delaunay C, Meyer O, Chollet-Martin S, Labarre
C (2004). Second generation anti-cyclic citrullinated peptide (anti-CCP2) antibodies can
replace other anti-filaggrin antibodies and improve rheumatoid arthritis diagnosis.
Scandinavian journal of rheumatology 33(4): 218-220.
Gu Y, Yun L, Tian Y, Hu Z (2009). Association between COMT gene and Chinese male
schizophrenic patients with violent behavior. Medical science monitor: international medical
journal of experimental and clinical research 15(9).
Gunnell D, Harrison G, Rasmussen F, Fouskakis D, Tynelius P (2002). Associations between
premorbid intellectual performance, early-life exposures and early-onset schizophrenia:
Cohort study. The British Journal of Psychiatry 181(4): 298.
Gur RE, Cowell P, Turetsky BI, Gallacher F, Cannon T, Bilker W, Gur RC (1998). A followup magnetic resonance imaging study of schizophrenia: relationship of neuroanatomical
changes to clinical and neurobehavioral measures. Archives of general psychiatry 55(2): 145.
Gutiérrez-Rodríguez O (2005). Thalidomide a promising new treatment for rheumatoid
arthritis. Arthritis & Rheumatism 27(10): 1118-1121.
Hafner H, Maurer K, Loffler W, an der Heiden W, Stein A, Konnecke R, Hambrecht M
(1999). Onset and prodromal phase as determinants of the course. Search for the Causes of
Schizophrenia: Balance of the century: 35.
233
References
Hambrecht M, Hafner H (2000). Cannabis, vulnerability, and the onset of schizophrenia: an
epidemiological perspective. Australian and New Zealand Journal of Psychiatry 34(3): 468475.
Harrison B (2002). Influence of cigarette smoking on disease outcome in rheumatoid arthritis.
Current opinion in rheumatology 14(2): 93.
Harrison B, Silman A, Wiles N (2001). The association of cigarette smoking with disease
outcome in patients with early inflammatory polyarthritis. Arthritis Rheum 44: 323-330.
Harrison B, Symmons D (2000). Early inflammatory polyarthritis: results from the Norfolk
Arthritis Register with a review of the literature. II. Outcome at three years. Rheumatology
39(9): 939.
Harrison G, Fouskakis D, Rasmussen F, Tynelius P, Sipos A, Gunnell D (2003). Association
between psychotic disorder and urban place of birth is not mediated by obstetric
complications or childhood socio-economic position: a cohort study. Psychological Medicine
33(04): 723-731.
Harrison P, Pointon JJ, Farrar C, Brown MA, Wordsworth BP (2006). Effects of PTPN22
C1858T polymorphism on susceptibility and clinical characteristics of British Caucasian
rheumatoid arthritis patients. Rheumatology 45(8): 1009-1011.
Harrison PJ (1999). The neuropathology of schizophrenia: A critical review of the data and
their interpretation. Brain 122(4): 593.
Heath RG, Krupp IM (1967). Schizophrenia as an Immunologic Disorder: I. Demonstration of
Antibrain Globulins by Fluorescent Antibody Techniques. Archives of general psychiatry
16(1): 1.
Helmick CG, Felson DT, Lawrence RC, Gabriel S, Hirsch R, Kwoh CK, Liang MH, Kremers
HM, Mayes MD, et al. (2008). Estimates of the prevalence of arthritis and other rheumatic
conditions in the United States. Arthritis Rheum 58(1): 15-25.
Hemmings BA (1997). Akt signaling: linking membrane events to life and death decisions.
Science 275(5300): 628-630.
Hennah W, Varilo T, Kestila M, Paunio T, Arajarvi R, Haukka J, Parker A, Martin R,
Levitzky S, et al. (2003). Haplotype transmission analysis provides evidence of association
for DISC1 to schizophrenia and suggests sex-dependent effects. Human Molecular Genetics
12(23): 3151.
234
References
Herken H, Erdal E (2001). Catechol-O-methyltransferase gene polymorphism in
schizophrenia: evidence for association between symptomatology and prognosis. Psychiatric
Genetics 11(2): 105.
Hillman L, Cassidy J, Johnson L, Lee D, Allen S (1994). Vitamin D metabolism and bone
mineralization in children with juvenile rheumatoid arthritis1. The Journal of pediatrics
124(6): 910-916.
Hinks A, Barton A, John S, Bruce I, Hawkins C, Griffiths C, Donn R, Thomson W, Silman A,
et al. (2005). Association between the PTPN22 gene and rheumatoid arthritis and juvenile
idiopathic arthritis in a UK population: further support that PTPN22 is an autoimmunity gene.
Arthritis Care & Research 52(6): 1694-1699.
Hodgkinson C, Goldman D, Jaeger J, Persaud S, Kane J, Lipsky R, Malhotra A (2004).
Disrupted in schizophrenia 1 (DISC1): association with schizophrenia, schizoaffective
disorder, and bipolar disorder. The American Journal of Human Genetics 75(5): 862-872.
Hollis-Moffat J IDD21 and Murine Autoimmunity. Doctor of Philosophy, Univeristy of
Otago, Dunedin, 2006.
Holmdahl R, Bockermann R, Bäcklund J, Yamada H (2002). The molecular pathogenesis of
collagen-induced arthritis in mice—a model for rheumatoid arthritis. Ageing Research
Reviews 1(1): 135-147.
Howes O, McDonald C, Cannon M, Arseneault L, Boydell J, Murray R (2004). Pathways to
schizophrenia: the impact of environmental factors. The International Journal of
Neuropsychopharmacology 7(S1): 7-13.
Howes OD, Kapur S (2009). The dopamine hypothesis of schizophrenia: version III--the final
common pathway. Schizophrenia Bulletin.
Howes OD, Montgomery AJ, Asselin MC, Murray RM, Valli I, Tabraham P, Bramon-Bosch
E, Valmaggia L, Johns L, et al. (2009). Elevated striatal dopamine function linked to
prodromal signs of schizophrenia. Archives of general psychiatry 66(1): 13.
Huang W, Wang P, Liu Z, Zhang L (2009). Identifying disease associations via genome-wide
association studies. BMC bioinformatics 10(Suppl 1): S68.
Huber LC, Distler O, Tarner I, Gay RE, Gay S, Pap T (2006). Synovial fibroblasts: key
players in rheumatoid arthritis. Rheumatology 45(6): 669.
Hultman CM, P S, N T, RM M, S C (1999). Prenatal and perinatal risk factors for
schizophrenia, affective psychosis, and reactive psychosis of early onset: case-control study.
BMJ 318(7181): 421-426.
235
References
Ikari K, Kuwahara M, Nakamura T, Momohara S, Hara M, Yamanaka H, Tomatsu T,
Kamatani N, Foundation J, et al. (2005). Association between PADI4 and rheumatoid
arthritis: a replication study. Arthritis Care & Research 52(10): 3054-3057.
Ikeda M, Iwata N, Suzuki T, Kitajima T, Yamanouchi Y, Kinoshita Y, Inada T, Ozaki N
(2004). Association of AKT1 with schizophrenia confirmed in a Japanese population.
Biological Psychiatry 56(9): 698-700.
Ikeda M, Iwata N, Suzuki T, Kitajima T, Yamanouchi Y, Kinoshiya Y, Sekine Y, Iyo M,
Harano M, et al. (2005). Positive association of AKT1 haplotype to Japanese
methamphetamine use disorder. The International Journal of Neuropsychopharmacology
9(01): 77-81.
Invitrogen (2010). Akt1 Pathway Overview Vol. 2010.
Isohanni I, J‰rvelin M, Nieminen P, Jones P, Rantakallio P, Jokelainen J, Isohanni M (1998).
School performance as a predictor of psychiatric hospitalization in adult life. A 28-year
follow-up in the Northern Finland 1966 Birth Cohort. Psychological medicine 28(04): 967974.
Isohanni M, Jones P, Moilanen K, Rantakallio P, Veijola J, Oja H, Koiranen M, Jokelainen J,
Croudace T, et al. (2001). Early developmental milestones in adult schizophrenia and other
psychoses. A 31-year follow-up of the Northern Finland 1966 Birth Cohort. Schizophrenia
research 52(1-2): 1-19.
Isohanni M, Lauronen E, Moilanen K, Isohanni I, Kemppainen L, Koponen H, Miettunen J,
Maki P, Rasanen S, et al. (2005). Predictors of schizophrenia: evidence from the Northern
Finland 1966 Birth Cohort and other sources. The British Journal of Psychiatry 187(48): s4.
James JA, Kaufman KM, Farris AD, Taylor-Albert E, Lehman TJ, Harley JB (1997). An
increased prevalence of Epstein-Barr virus infection in young patients suggests a possible
etiology for systemic lupus erythematosus. Journal of Clinical Investigation 100(12): 3019.
Jarskog LF, Miyamoto S, Lieberman JA (2007). Schizophrenia: new pathological insights and
therapies.
Jia P, Wang L, Meltzer HY, Zhao Z (2010). Common variants conferring risk of
schizophrenia: a pathway analysis of GWAS data. Schizophrenia research 122(1-3): 38-42.
Jones P (1997). The early origins of schizophrenia. British medical bulletin 53(1): 135.
Jones P, Murray R, Rodgers B, Marmot M (1994). Child developmental risk factors for adult
schizophrenia in the British 1946 birth cohort. The Lancet 344(8934): 1398-1402.
236
References
Jones PB, Psych MRC, Rantakallio P, Hartikainen AL, Isohanni M, Sipila P (1998).
Schizophrenia as a Long-Term Outcome of Pregnancy, Delivery, and Perinatal
Complications: A 28-Year Follow-Up of the 1966 North Finland General Population Birth
Cohort. American Journal of Psychiatry 155: 355-364.
Joo E, Lee K, Jeong S, Roh M, Kim S, Ahn Y, Kim Y (2009). AKT1 Gene Polymorphisms
and Obstetric Complications in the Patients with Schizophrenia.
Kamiya A, Kubo K, Tomoda T, Takaki M, Youn R, Ozeki Y, Sawamura N, Park U, Kudo C,
et al. (2005). A schizophrenia-associated mutation of DISC1 perturbs cerebral cortex
development. Nature cell biology 7(12): 1167-1178.
Kane JM (1999). Pharmacologic treatment of schizophrenia. Biological Psychiatry 46(10):
1396-1408.
Kang C, Lee H, Ju H, Cho H, Kang C, Bae S (2006). A functional haplotype of the PADI4
gene associated with increased rheumatoid arthritis susceptibility in Koreans. Arthritis and
rheumatism 54(1): 90-96.
Kannan K, Ortmann RA, Kimpel D (2005). Animal models of rheumatoid arthritis and their
relevance to human disease. Pathophysiology 12(3): 167-181.
Karlson E, Chibnik L, Cui J, Plenge R, Glass R, Maher N, Parker A, Roubenoff R, Izmailova
E, et al. (2008). Associations between human leukocyte antigen, PTPN22, CTLA4 genotypes
and rheumatoid arthritis phenotypes of autoantibody status, age at diagnosis and erosions in a
large cohort study. British Medical Journal 67(3): 358.
Kastbom A, Strandberg G, Lindroos A, Skogh T (2004). Anti-CCP antibody test predicts the
disease course during 3 years in early rheumatoid arthritis (the Swedish TIRA project).
Annals of the rheumatic diseases 63(9): 1085.
Katchamart W, Trudeau J, Phumethum V, Bombardier C (2008). The efficacy and toxicity of
Methotrexate (MTX) monotherapy vs. MTX combination therapy with non-biologic diseasemodifying anti-rheumatic drugs in rheumatoid arthritis: A systematic review and
metaanalysis. British Medical Journal.
Kaye BR, Kaye RL, Bobrove A (1984). Rheumatoid nodules. Review of the spectrum of
associated conditions and proposal of a new classification, with a report of four seronegative
cases. Am J Med 76(2): 279-292.
Kessler RC, Galea S, Gruber MJ, Sampson NA, Ursano RJ, Wessely S (2008). Trends in
mental illness and suicidality after Hurricane Katrina. Molecular psychiatry 13(4): 374.
Khoury M, Bertram L, Boffetta P, Butterworth A, Chanock S, Dolan S, Fortier I, GarciaClosas M, Gwinn M, et al. (2009). Genome-Wide Association Studies, Field Synopses, and
237
References
the Development of the Knowledge Base on Genetic Variation and Human Diseases.
American Journal of Epidemiology 170(3): 269.
Kilpinen H, Ylisaukko-Oja T, Hennah W, Palo O, Varilo T, Vanhala R, Nieminen-von Wendt
T, Von Wendt L, Paunio T, et al. (2007). Association of DISC1 with autism and Asperger
syndrome. Molecular Psychiatry 13(2): 187-196.
Kim G, Jun JB, Elkon KB (2002). Necessary role of phosphatidylinositol 3-kinase in
transforming growth factor–mediated activation of Akt in normal and rheumatoid arthritis
synovial fibroblasts. Arthritis Rheum 46: 1504-1511.
Kipnis J, Cardon M, Strous RD, Schwartz M (2006). Loss of autoimmune T cells correlates
with brain diseases: possible implications for schizophrenia? Trends in Molecular Medicine
12(3): 107-112.
Kirwan J (1995). The effect of glucocorticoids on joint destruction in rheumatoid arthritis.
New England Journal of Medicine 333(3): 142.
Kitahara K, Kawai S (2007). Cyclosporine and tacrolimus for the treatment of rheumatoid
arthritis. Current opinion in rheumatology 19(3): 238.
Klareskog L, Stolt P, Lundberg K, Källberg H, Bengtsson C, Grunewald J, Rönnelid J, Harris
HE, Ulfgren AK, et al. (2006). A new model for an etiology of rheumatoid arthritis: smoking
may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified
by citrullination. Arthritis and rheumatism 54(1): 38.
Kobayashi S, Ikari K, Kaneko H, Kochi Y, Yamamoto K, Shimane K, Nakamura Y, Toyama
Y, Mochizuki T, et al. (2008). Association of STAT4 with susceptibility to rheumatoid
arthritis and systemic lupus erythematosus in the Japanese population. Arthritis and
Rheumatism 58(7): 1940-1946.
Koch AE (1998). Angiogenesis: Implications for rheumatoid arthritis. Arthritis and
rheumatism 41(6): 951-962.
Kockelkorn T, Arai M, Matsumoto H, Fukuda N, Yamada K, Minabe Y, Toyota T, Ujike H,
Sora I, et al. (2004). Association study of polymorphisms in the 5 upstream region of human
DISC1 gene with schizophrenia. Neuroscience letters 368(1): 41-45.
Koga T, Matsui Y, Asagiri M, Kodama T, de Crombrugghe B, Nakashima K, Takayanagi H
(2005). NFAT and Osterix cooperatively regulate bone formation. Nature medicine 11(8):
880-885.
Kokkonen H, Johansson M, Innala L, Jidell E, Rantapaa-Dahlqvist S (2007). The PTPN22
1858C/T polymorphism is associated with anti-cyclic citrullinated peptide antibody-positive
early rheumatoid arthritis in northern Sweden. Arthritis Research and Therapy 9(3): 56.
238
References
Korganow AS, Ji H, Mangialaio S, Duchatelle V, Pelanda R, Martin T, Degott C, Kikutani J,
Rajewsky K, et al. (1999). From Systemic T Cell Self-Reactivity to Organ-Specific
Autoimmune Disease via Immunoglobulins. IMMUNITY-CAMBRIDGE MA- 10: 451-462.
Kouskoff V, Korganow AS, Duchatelle V, Degott C, Benoist C, Mathis D (1996). Organspecific disease provoked by systemic autoimmunity. Cell 87(5): 811.
Kraus C, Liehr T, Hulsken J, Behrens J, Birchmeier W, Grzeschik KH, Ballhausen WG
(1994). Localization of the human beta-catenin gene (CTNNB1) to 3p21: a region implicated
in tumor development. Genomics 23(1): 272-274.
Kremer JM, Alarcón GS, Lightfoot Jr RW, Willkens RF, Furst DE, Williams HJ, Dent PB,
Weinblatt ME (2005). Methotrexate for rheumatoid arthritis. Arthritis & Rheumatism 37(3):
316-328.
Kurreeman FAS, Padyukov L, Marques RB, Schrodi SJ, Seddighzadeh M, StoekenRijsbergen G, Allaart CF, Verduyn W, Houwing-Duistermaat J, et al. (2007). A candidate
gene approach identifies the TRAF1/C5 region as a risk factor for rheumatoid arthritis. PLOS
Med 4(9).
Kwoh C, Venglish C, Lynn A, Whitley D, Young E, Chakravarti A (1996). Age, sex, and the
familial risk of rheumatoid arthritis. American Journal of Epidemiology 144(1): 15.
L'Abbè KA, Detsky AS, O'Rourke K (1987). Meta-analysis in clinical research. Annals of
Internal Medicine 107(2): 224.
Lahiry L, Saha B, Chakraborty J, Adhikary A, Mohanty S, Hossain DMS, Banerjee S, Das K,
Sa G, et al. (2010). Theaflavins target Fas/caspase-8 and Akt/pBad pathways to induce
apoptosis in p53-mutated human breast cancer cells. Carcinogenesis 31(2): 259.
Laruelle M, Kegeles LS, Abi-Dargham A (2003). Glutamate, Dopamine, and Schizophrenia.
Ann. NY Acad. Sci 1003: 138-158.
Lawrence J (1970). Heberden Oration, 1969. Rheumatoid arthritis--nature or nurture? British
Lee DM, Weinblatt ME (2001). Rheumatoid arthritis. The Lancet 358(9285): 903-911.
Lee K, Joo E, Jeong S, Kang U, Roh M, Kim S, Song J, Hwang J, Kim S, et al. (2009). No
association between AKT1 polymorphism and schizophrenia: A case-control study in a
Korean population and a meta-analysis. Neuroscience research.
Lewis DA, Gonzalez-Burgos G (2006). Pathophysiologically based treatment interventions in
schizophrenia. Nature Medicine 12: 1016-1022.
239
References
Lewis DA, Levitt P (2002). Schizophrenia as a Disorder of Neurodevelopment Annual Review
of Neuroscience 25(1): 409-432.
Lewis G, David A, Andreasson S, Allebeck P (1992). Schizophrenia and city life. Lancet
340(8812): 137.
Li D, Collier D, He L (2006). Meta-analysis shows strong positive association of the
neuregulin 1 (NRG1) gene with schizophrenia. Human Molecular Genetics 15(12): 1995.
Li D, He L (2007). Association study between the dystrobrevin binding protein 1 gene
(DTNBP1) and schizophrenia: a meta-analysis. Schizophrenia Research 96(1-3): 112-118.
Lieberman JA, Perkins D, Belger A, Chakos M, Jarskog F, Boteva K, Gilmore J (2001). The
early stages of schizophrenia: speculations on pathogenesis, pathophysiology, and therapeutic
approaches. Biological Psychiatry 50(11): 884-897.
Liu H, Pope RM (2003). The role of apoptosis in rheumatoid arthritis. Current Opinion in
Pharmacology 3(3): 317-322.
Liu Y, Helms C, Liao W, Zaba L, Duan S, Gardner J, Wise C, Miner A, Malloy M, et al.
(2008). A genome-wide association study of psoriasis and psoriatic arthritis identifies new
disease loci. PLoS Genetics 4(4).
Lu C, Dong J, Ma H, Jin G, Hu Z, Peng Y, Guo X, Wang X, Shen H (2009). CCND1 G870A
polymorphism contributes to breast cancer susceptibility: a meta-analysis. Breast Cancer
Research and Treatment 116(3): 571-575.
Maccioni M, Zeder-Lutz G, Huang H, Ebel C, Gerber P, Hergueux J, Marchal P, Duchatelle
V, Degott C, et al. (2002). Arthritogenic Monoclonal Antibodies from K/BxN Mice. The
Journal of Experimental Medicine 195(8): 1071.
MacGregor A, Snieder H, Rigby A, Koskenvuo M, Kaprio J, Aho K, Silman A (2001a).
Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from
twins. Arthritis & Rheumatism 43(1): 30-37.
MacGregor AJ, Snieder H, Rigby AS, Koskenvuo M, Kaprio J, Aho K, Silman AJ (2001b).
Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from
twins. Arthritis & Rheumatism 43(1): 30-37.
Majorczyk E, Jasek M (2007). Association of PTPN22 single nucleotide polymorphism with
rheumatoid arthritis but not with allergic asthma. European Journal of Human Genetics
15(10): 1043-1048.
240
References
Malaspina D, Harlap S, Fennig S, Heiman D, Nahon D, Feldman D, Susser E (2001).
Advancing paternal age and the risk of schizophrenia. Archives of general psychiatry 58(4):
361.
Mao Y, Ge X, Frank C, Madison J, Koehler A, Doud M, Tassa C, Berry E, Soda T, et al.
(2009). DISC1 regulates neural progenitor proliferation via modulation of GSK3 / -catenin
signaling. Cell 136(6): 1017.
Maradit-Kremers H, Nicola PJ, Crowson CS, Ballman KV, Gabriel SE (2005).
Cardiovascular death in rheumatoid arthritis. Arthritis Rheum 52: 722-732.
Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007). A new multipoint method for
genome-wide association studies by imputation of genotypes. Nature genetics 39(7): 906-913.
Martinez A, Varade J, Marquez A, Cenit M, Espino L, Perdigones N, Santiago J, FernandezArquero M, de la Calle H, et al. (2008). Association of the STAT4 gene with increased
susceptibility for some immune-mediated diseases. Arthritis Care & Research 58(9): 25982602.
Masdottir B, Jonsson T, Manfreethsdottir V, Vikingsson A, Brekkan A, Valdimarsson H
(2000). Smoking, rheumatoid factor isotypes and severity of rheumatoid arthritis.
Rheumatology 39(11): 1202.
Masson-Bessiere C, Sebbag M, Girbal-Neuhauser E, Nogueira L, Vincent C, Senshu T, Serre
G (2001). The Major Synovial Targets of the Rheumatoid Arthritis-Specific Antifilaggrin
Autoantibodies Are Deiminated Forms of the {{alpha}}-and {{beta}}-Chains of Fibrin. The
Journal of Immunology 166(6): 4177.
Matsumoto I, Staub A, Benoist CM (1999). Arthritis provoked by linked T and B cell
recognition of a glycolytic enzyme. Science 286(5445): 5.
McDonald C, Murray RM (2000). Early and late environmental risk factors for schizophrenia.
Brain Research Reviews 31(2-3): 130-137.
McEntegart A, Morrison E, Capell H, Duncan M, Porter D, Madhok R, Thomson E (1997).
Effect of social deprivation on disease severity and outcome in patients with rheumatoid
arthritis. British Medical Journal 56(7): 410.
Mednick S, Machon R, Huttunen M, Bonett D (1988). Adult schizophrenia following prenatal
exposure to an influenza epidemic. Archives of general psychiatry 45(2): 189.
Mellsop GW, Koadlow L, Syme J, Whittingham S (2008). Absence of Rheumatoid Arthritis
in Schizophrenia*. Internal Medicine Journal 4(3): 247-252.
241
References
Merlino LA, Curtis J, Mikuls TR, Cerhan JR, Criswell LA, Saag KG (2004). Vitamin D
intake is inversely associated with rheumatoid arthritis. Arthritis Rheum 50(1): 72-77.
Messias E, Chen C, Eaton W (2007). Epidemiology of schizophrenia: review of findings and
myths. Psychiatric Clinics of North America 30(3): 323-338.
Meyer-Lindenberg A, Miletich RS, Kohn PD, Esposito G, Carson RE, Quarantelli M,
Weinberger DR, Berman KF (2002). Reduced prefrontal activity predicts exaggerated striatal
dopaminergic function in schizophrenia. nature neuroscience 5(3): 267-271.
Mezhybovska M, Wikstrom K, Ohd J, Sjolander A (2006). The inflammatory mediator
leukotriene D4 induces beta-catenin signaling and its association with antiapoptotic Bcl-2 in
intestinal epithelial cells. Journal of Biological Chemistry 281(10): 6776.
Michou L, Lasbleiz S, Rat A, Migliorini P, Balsa A, Westhovens R, Barrera P, Alves H,
Pierlot C, et al. (2007). Linkage proof for PTPN22, a rheumatoid arthritis susceptibility gene
and a human autoimmunity gene. Proceedings of the National Academy of Sciences 104(5):
1649.
Middleton FA, Mirnics K, Pierri JN, Lewis DA, Levitt P (2002). Gene Expression Profiling
Reveals Alterations of Specific Metabolic Pathways in Schizophrenia. Journal of
Neuroscience 22(7): 2718.
Milicic A, Lee D, Brown MA, Darke C, Wordsworth BP (2002). HLA-DR/DQ haplotype in
rheumatoid arthritis: novel allelic associations in UK Caucasians. The Journal of
Rheumatology 29(9): 1821.
Moffitt T, Caspi A, Rutter M, Silva P (2001). Sex differences in antisocial behaviour:
Conduct disorder, delinquency, and violence in the Dunedin Longitudinal Study. edn.
Cambridge Univ Pr.
Mohamed SN, Merskey H, Kazarian S, Disney TF (1982). An investigation of the possible
inverse relationships between the occurrence of rheumatoid arthritis, osteoarthritis and
schizophrenia. Can J Psychiatry 27: 381-383.
Monach P, Hattori K, Huang H, Hyatt E, Morse J, Nguyen L, Ortiz-Lopez A, Wu HJ, Mathis
D, et al. (2007). The K/BxN Mouse Model of Inflammatory Arthritis.
Mortensen P, Pedersen C, Westergaard T, Wohlfahrt J, Ewald H, Mors O, Andersen P,
Melbye M (1999). Effects of family history and place and season of birth on the risk of
schizophrenia. New England Journal of Medicine 340(8): 603.
Mulcahy B, Waldron-Lynch F, McDermott M, Adams C, Amos C, Zhu D, Ward R, Clegg D,
Shanahan F, et al. (1996). Genetic variability in the tumor necrosis factor-lymphotoxin region
242
References
influences susceptibility to rheumatoid arthritis. American journal of human genetics 59(3):
676.
Muller N, Dursun SM (2010). Schizophrenia genes, epigenetics and psychoneuroimmunology
therapeutics: all make sense now? Journal of Psychopharmacology.
Mulrow CD, San Antonio T (2009). The medical review article: state of the science.
Landmark Papers in Internal Medicine: The First 80 Years of Annals of Internal Medicine
106: 5.
Munafo M, Thiselton D, Clark T, Flint J (2006). Association of the NRG1 gene and
schizophrenia: a meta-analysis. Molecular psychiatry 11(6): 539-546.
Munk-Jorgensen P (1987). First-admission rates and marital status of schizophrenics. Acta
Psychiatr Scand 76: 210-216.
Murray R, Lewis S (1987). Is schizophrenia a neurodevelopmental disorder? British medical
journal (Clinical research ed.) 295(6600): 681.
Myasoedova E, Crowson CS, Kremers HM, Therneau TM, Gabriel SE (2010). Is the
incidence of rheumatoid arthritis rising? Results from olmsted county, minnesota, 1955-2007.
Arthritis & Rheumatism 9999(999A).
Narita K, Sasaki T, Akaho R, Okazaki Y, Kusumi I, Kato T, Hashimoto O, Fukuda R,
Koyama T, et al. (2000). Human leukocyte antigen and season of birth in Japanese patients
with schizophrenia. American Journal of Psychiatry 157(7): 1173.
Naseem H, Thomson W, Silman A, Worthington J, Symmons D, Barton A (2008). The
PTPN22* C1858T functional polymorphism is associated with susceptibility to inflammatory
polyarthritis but neither this nor other variants spanning the gene is associated with disease
outcome. British Medical Journal 67(2): 251.
Need A, Ge D, Weale M, Maia J, Feng S, Heinzen E, Shianna K, Yoon W, Kasperavi i t D, et
al. (2009). A genome-wide investigation of SNPs and CNVs in schizophrenia. PLoS Genetics
5(2).
Nelson J, Hughes K, Smith A, Nisperos B, Branchaud A, Hansen J (1992). Remission of
rheumatoid arthritis during pregnancy and maternal-fetal class II alloantigen disparity.
American journal of reproductive immunology(1989) 28(3-4): 226-227.
Nelson MD, Saykin AJ, Flashman LA, Riordan HJ (1998). Hippocampal volume reduction in
schizophrenia as assessed by magnetic resonance imaging: a meta-analytic study. Archives of
general psychiatry 55(5): 433.
243
References
Newkirk MM, Duffy KNW, Leclerc J, Lambert N, Shiroky JB (1994). Detection of
Cytomegalovirus, Epstein-Barr Virus and Herpes Virus-6 in Patients with Rheumatoid
Arthritis Withor Without Sjogren's Syndrome. Rheumatology 33(4): 317-322.
Newman S, Revenson T (1993). Coping with rheumatoid arthritis. Bailliere's Clinical
Rheumatology 7: 259-280.
Newton J, Harney S, Wordsworth B, Brown M (2004). A review of the MHC genetics of
rheumatoid arthritis. Genes and Immunity 5(3): 151-157.
Norton N, Williams HJ, Dwyer S, Carroll L, Peirce T, Moskvina V, Segurado R, Nikolov I,
Williams NM, et al. (2007). Association analysis of AKT1 and schizophrenia in a UK case
control sample. Schizophrenia Research 93(1-3): 58-65.
Nurnberger Jr J, Blehar M, Kaufmann C, York-Cooler C, Simpson S, Harkavy-Friedman J,
Severe J, Malaspina D, Reich T (1994). Diagnostic Interview for Genetic Studies: rationale,
unique features, and training. Archives of general psychiatry 51(11): 849.
O'Donovan M, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, Nikolov I,
Hamshere M, Carroll L, et al. (2008). Identification of loci associated with schizophrenia by
genome-wide association and follow-up. Nat Genet 40(9): 1053-1055.
Ohmori O, Shinkai T, Kojima H, Terao T, Suzuki T, Mita T, Abe K (1998). Association study
of a functional catechol-O-methyltransferase gene polymorphism in Japanese schizophrenics.
Neuroscience letters 243(1-3): 109-112.
Ohtsuki T, Inada T, Arinami T (2004). Failure to confirm association between AKT1
haplotype and schizophrenia in a Japanese case-control population. Molecular Psychiatry 9:
981-983.
Oka M, Vainio U (1966). Effect of pregnancy on the prognosis and serology of rheumatoid
arthritis. Acta rheumatologica Scandinavica 12(1): 47.
Okamoto K, Makino S, Yoshikawa Y, Takaki A, Nagatsuka Y, Ota M, Tamiya G, Kimura A,
Bahram S, et al. (2003). Identification of I $# x003ba; BL as the Second Major
Histocompatibility Complex $# x02013; Linked Susceptibility Locus for Rheumatoid
Arthritis. American Journal of Human Genetics 72(2): 303.
Oken RJ, Schulzer M (1999). At Issue: Schizophrenia and Rheumatoid Arthritis: The
Negative Association Revisited. Schizophrenia Bulletin 25(4): 625.
Okubo Y, Suhara T, Suzuki K, Kobayashi K, Inoue O, Terasaki O, Someya Y, Sassa T, Sudo
Y, et al. (1997). Decreased prefrontal dopamine D1 receptors in schizophrenia revealed by
PET. Nature 385(6617): 634-636.
244
References
Orozco G, Alizadeh B, Delgado-Vega A, Gonz·lez-Gay M, Balsa A, Pascual-Salcedo D,
Fern·ndez-Gutierrez B, Gonz·lez-Escribano M, Petersson I, et al. (2008). Association of
STAT4 with rheumatoid arthritis: a replication study in three European populations. Arthritis
Care & Research 58(7): 1974-1980.
Orozco G, Eyre S, Hinks A, Ke X, Wilson A, Bax D, Morgan A, Emery P, Steer S, et al.
(2009a). Association of CD40 with rheumatoid arthritis confirmed in a large UK case-control
study. British Medical Journal.
Orozco G, Hinks A, Eyre S, Ke X, Gibbons L, Bowes J, Flynn E, Martin P (2009b).
Combined effects of three independent SNPs greatly increase the risk estimate for RA at
6q23. Human Molecular Genetics 18(14): 2693.
Orozco G, Sanchez E, Gonz·lez-Gay M, Lopez-Nevot M, Torres B, Caliz R, Ortego-Centeno
N, JimÈnez-Alonso J, Pascual-Salcedo D, et al. (2005). Association of a functional singlenucleotide polymorphism of PTPN22, encoding lymphoid protein phosphatase, with
rheumatoid arthritis and systemic lupus erythematosus. Arthritis Care & Research 52(1):
219-224.
Österberg E (1978). Schizophrenia and rheumatic disease. Acta Psychiatrica Scandinavica
58(4): 339-359.
Owen C, Kelly H, Eden J, Merriman M, Pearce S, Merriman T (2007). Analysis of the Fc
receptor-like-3 (FCRL3) locus in Caucasians with autoimmune disorders suggests a complex
pattern of disease association. Journal of Clinical Endocrinology & Metabolism 92(3): 1106.
Pajak B, Songin M, Strosznajder JB, Gajkowska B (2009). Alzheimer's disease genetic
mutation evokes ultrastructural alterations: correlation to an intracellular Abeta deposition and
the level of GSK-3beta-P(Y216) phosphorylated form. Neurotoxicology 30(4): 581-588.
Palo O, Antila M, Silander K, Hennah W, Kilpinen H, Soronen P, Tuulio-Henriksson A,
Kieseppa T, Partonen T, et al. (2007). Association of distinct allelic haplotypes of DISC1
with psychotic and bipolar spectrum disorders and with underlying cognitive impairments.
Human Molecular Genetics 16(20): 2517.
Pantelis C, Velakoulis D, McGorry PD, Wood SJ, Suckling J, Phillips LJ, Yung AR,
Bullmore ET, Brewer W, et al. (2003). Neuroanatomical abnormalities before and after onset
of psychosis: a cross-sectional and longitudinal MRI comparison. The Lancet 361(9354):
281-288.
Park C, Choi S, Kang S, Kang J, Ahn B, Kim H, Jung G, Choi K, Hwang S (2009).
Nonstructural 5A protein activates -catenin signaling cascades: Implication of hepatitis C
virus-induced liver pathogenesis. Journal of Hepatology 51(5): 853-864.
245
References
Park T, Yoon K, Kim J, Park W, Hirvonen A, Kang D (2002). Functional catechol-Omethyltransferase gene polymorphism and susceptibility to schizophrenia. European
Neuropsychopharmacology 12(4): 299-303.
Pascual M, Nieto A, Lopez-Nevot MA, Ramal L, Mataran L, Caballero A, Alonso A, Martin
J, Zanelli E (2001). Rheumatoid arthritis in southern Spain: toward elucidation of a unifying
role of the HLA class II region in disease predisposition. Arthritis & Rheumatism 44(2): 307314.
Pearson K (1904). Report on certain enteric fever inoculation statistics. The British Medical
Journal: 1243-1246.
Pearson TA, Manolio TA (2008). How to interpret a genome-wide association study. Jama
299(11): 1335.
Pedersen C, Mortensen P (2001). Family history, place and season of birth as risk factors for
schizophrenia in Denmark: a replication and reanalysis. The British Journal of Psychiatry
179(1): 46.
Peto R (1980). Aspirin after myocardial infarction. Lancet 1: 1172-1173.
Pickard B, Malloy M, Christoforou A, Thomson P, Evans K, Morris S, Hampson M, Porteous
D, Blackwood D, et al. (2006). Cytogenetic and genetic evidence supports a role for the
kainate-type glutamate receptor gene, GRIK4, in schizophrenia and bipolar disorder.
Molecular psychiatry 11(9): 847-857.
Pikwer M, Bergstrom U, Nilsson J, Jacobsson L, Berglund G, Turesson C (2009). Breast
feeding, but not use of oral contraceptives, is associated with a reduced risk of rheumatoid
arthritis. Annals of the Rheumatic Diseases 68(4): 526.
Pilkington TL (1956). The coincidence of rheumatoid arthritis and schizophrenia. The Journal
of Nervous and Mental Disease 124(6): 604.
Pincus T, Callahan L (1985). Formal education as a marker for increased mortality and
morbidity in rheumatoid arthritis* 1,* 2. Journal of Chronic Diseases 38(12): 973-984.
Pincus T, Callahan L (1988). Formal education level as a significant marker of clinical status
in rheumatoid arthritis. Arthritis Rheum 31: 1346ñ1357.
Plenge R, Cotsapas C, Davies L, Price A, de Bakker P, Maller J, Pe'er I, Burtt N, Blumenstiel
B, et al. (2007a). Two independent alleles at 6q23 associated with risk of rheumatoid arthritis.
Nature genetics 39(12): 1477.
246
References
Plenge R, Seielstad M, Padyukov L, Lee A, Remmers E, Ding B, Liew A, Khalili H,
Chandrasekaran A, et al. (2007b). TRAF1-C5 as a risk locus for rheumatoid arthritis--a
genomewide study. New England Journal of Medicine 357(12): 1199.
Plenge RM, Padyukov L, Remmers EF, Purcell S, Lee AT, Karlson EW, Wolfe F, Kastner
DL, Alfredsson L, et al. (2005). Replication of Putative Candidate-Gene Associations with
Rheumatoid Arthritis in >4,000 Samples from North America and Sweden: Association of
Susceptibility with PTPN22, CTLA4, and PADI4. American Journal of Human Genetics
77(6): 1044-1060.
Plyte SE, Hughes K, Nikolakaki E, Pulverer BJ, Woodgett JR (1992). Glycogen synthase
kinase-3: functions in oncogenesis and development. Biochimica et biophysica acta 1114(23): 147.
Poulton R, Caspi A, Moffitt T, Cannon M, Murray R, Harrington H (2000). Children's selfreported psychotic symptoms and adult schizophreniform disorder: a 15-year longitudinal
study. Archives of General Psychiatry 57(11): 1053.
Pratt JG, Rhine JB, Smith BM, Stuart CE, Greenwood JA (1940). Extra-sensory perception
after sixty years: A critical appraisal of the research in extra-sensory perception. edn. H. Holt
and Co.
Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P,
Ruderfer DM, McQuillin A, et al. (2009). Common polygenic variation contributes to risk of
schizophrenia and bipolar disorder. Nature 460(7256): 748-752.
Puzanowska B, Prokopowicz D, Ziarko S, Radziwon P, Lapinski TW (2003). The incidence
of HLA DRB1*0103 in ulcerative colitis patients in north-eastern Poland.
Hepatogastroenterology 50(53): 1436-1438.
Qiuying L, Xiaonian L, Li L (1999). The control study of schizophrenia and affective
disorders and Toxoplasma infections. Journal of Hubei Medical University.
Rang HP, Dale MM, Ritter JM, Moore PK, Lamb P (2003). Pharmacology. edn. Churchill
Livingstone New York.
Ranganathan P (2009). Genetics of bone loss in rheumatoid arthritis--role of vitamin D
receptor polymorphisms. Rheumatology (Oxford) 48(4): 342-346.
Rao JK, Mihaliak K, Kroenke K, Bradley J, Tierney WM, Weinberger M (1999). Use of
Complementary Therapies for Arthritis among Patients of Rheumatologists. Annals of
Internal Medicine 131(6): 409-416.
247
References
Raychaudhuri S, Remmers EF, Lee AT, Hackett R, Guiducci C, Burtt NlP, Gianniny L,
Korman BD, Padyukov L, et al. (2008). Common variants at CD40 and other loci confer risk
of rheumatoid arthritis. Nature Genetics 40(10): 1216-1223.
Remmers E, Plenge R, Lee A, Graham R, Hom G, Behrens T, de Bakker P, Le J, Lee H, et al.
(2007). STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus. The
New England journal of medicine 357(10): 977.
Riley B, Kuo P, Maher B, Fanous A, Sun J, Wormley B, O'Neill F, Walsh D, Zhao Z, et al.
(2009a). The dystrobrevin binding protein 1 (DTNBP1) gene is associated with schizophrenia
in the Irish Case Control Study of Schizophrenia (ICCSS) sample. Schizophrenia Research.
Riley B, Thiselton D, Maher B, Bigdeli T, Wormley B, McMichael G, Fanous A, Vladimirov
V, O'Neill F, et al. (2009b). Replication of association between schizophrenia and ZNF804A
in the Irish CaseñControl Study of Schizophrenia sample. Molecular psychiatry 15(1): 29-37.
Rodriguez OC, Lai EW, Vissapragada S, Cromelin C, Avetian M, Salinas P, Ramos H,
Kallakury B, Casimiro M, et al. (2009). A Reduction in Pten Tumor Suppressor Activity
Promotes ErbB-2-Induced Mouse Prostate Adenocarcinoma Formation through the Activation
of Signaling Cascades Downstream of PDK1. American Journal of Pathology 174(6): 2051.
Rogers DP, Goldsmith CAW (2009). Treatment of schizophrenia in the 21st Century: beyond
the neurotransmitter hypothesis. Expert review of neurotherapeutics 9(1): 47-54.
Rohatiner AZS, Gregory WM, Peterson B, Borden E, Solal-Celigny P, Hagenbeek A, Fisher
RI, Unterhalt M, Arranz R, et al. (2005). Meta-analysis to evaluate the role of interferon in
follicular lymphoma. Journal of clinical oncology 23(10): 2215.
Rollema H, Lu Y, Schmidt AW, Zorn SH (1997). Clozapine increases dopamine release in
prefrontal cortex by 5-HT1A receptor activation. European journal of pharmacology 338(2):
R3-R5.
Ross C (2009). A Point Of Disruption. Nature 458(7241): 976-977.
Ross WD, Hay J, McDowall MF (1950). The incidence of certain vegetative disturbances in
relation to psychosis. Psychosomatic Medicine 12(3): 179.
Rosso I, Cannon T, Huttunen T, Huttunen M, Lˆnnqvist J, Gasperoni T (2000). Obstetric risk
factors for early-onset schizophrenia in a Finnish birth cohort. American Journal of
Psychiatry 157(5): 801.
Rubinstein G (1997). Schizophrenia, rheumatoid arthritis and natural resistance genes.
Schizophrenia Research 25(3): 177-181.
248
References
Saag K, Cerhan J, Kolluri S, Ohashi K, Hunninghake G, Schwartz D (1997). Cigarette
smoking and rheumatoid arthritis severity. British Medical Journal 56(8): 463.
Saag K, Kolluri S, Koehnke R, Georgou T, Rachow J, Hunninghake G, Schwartz D (1996).
Rheumatoid arthritis lung disease. Determinants of radiographic and physiologic
abnormalities. Arthritis & Rheumatism 39(10).
Salvarani C, Macchioni P, Mantovani W, Rossi F, Veneziani M, Boiardi L, Lodi L, Portioli I
(1992). Extraarticular manifestations of rheumatoid arthritis and HLA antigens in northern
Italy. The Journal of rheumatology 19(2): 242.
Sartorius N (2007). Stigma and mental health. The Lancet 370(9590): 810-811.
Sawamukai N, Saito K, Yamaoka K, Nakayamada S, Ra C, Tanaka Y (2007). Leflunomide
inhibits PDK1/Akt pathway and induces apoptosis of human mast cells. The Journal of
Immunology 179(10): 6479.
Scatizzi JC, Hutcheson J, Pope RM, Firestein GS, Koch AE, Mavers M, Smason A, Agrawal
H, Haines Iii GK, et al. (2010). Bim--Bcl-2 Homology 2 Mimetic Therapy in Effective at
Suppressing Inflammatory Arthritis Through the Activation of Myeloid Cell Apoptosis.
Schaffer BAJ, Bertram L, Miller BL, Mullin K, Weintraub S, Johnson N, Bigio EH, Mesulam
M, Wiedau-Pazos M, et al. (2008). Association of GSK3B with Alzheimer disease and
frontotemporal dementia. Archives of Neurology 65(10): 1368.
Schumacher J, Laje G, Jamra R, Becker T, Muhleisen T, Vasilescu C, Mattheisen M, Herms
S, Hoffmann P, et al. (2009). The DISC locus and schizophrenia: evidence from an
association study in a central European sample and from a meta-analysis across different
European populations. Human Molecular Genetics 18(14): 2719.
Schwab S, Hoefgen B, Hanses C, Hassenbach M, Albus M, Lerer B, Trixler M, Maier W,
Wildenauer D (2005). Further evidence for association of variants in the AKT1 gene with
schizophrenia in a sample of European sib-pair families. Biological Psychiatry 58(6): 446450.
Scott L, Mohlke K, Bonnycastle L, Willer C, Li Y, Duren W, Erdos M, Stringham H, Chines
P, et al. (2007). A genome-wide association study of type 2 diabetes in Finns detects multiple
susceptibility variants. Science 316(5829): 1341.
SE K, Grodstein F (2004). Do breast-feeding and other reproductive factors influence future
risk of rheumatoid arthritis?: Results from the Nurses' Health Study. Arthritis and
Rheumatism 50(11).
249
References
Selten J, Cantor-Graae E (2005). Social defeat: risk factor for schizophrenia? The British
Journal of Psychiatry 187(2): 101.
Shapiro JA, Koepsell TD, Voigt LF, Dugowson CE, Kestin M, Nelson JL (1996). Diet and
rheumatoid arthritis in women: a possible protective effect of fish consumption. Epidemiology
7(3): 256-263.
Sherrer YS, Bloch DA, Mitchell DM, Young DY, Fries JF (2005). The development of
disability in rheumatoid arthritis. Arthritis & Rheumatism 29(4): 494-500.
Shi J, Levinson D, Duan J, Sanders A, Zheng Y, Peíer I, Dudbridge F, Holmans P,
Whittemore A, et al. (2009a). Common variants on chromosome 6p22. 1 are associated with
schizophrenia. Nature.
Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe‚ I (2009b). Common variants on
chromosome 6p22. 1 are associated with schizophrenia. Nature 460(7256): 753-757.
Shi Y, He L (2005). SHEsis, a powerful software platform for analyses of linkage
disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell
Research 15(2): 97-98.
Shifman S, Bronstein M, Sternfeld M, Pisanté-Shalom A, Lev-Lehman E, Weizman A,
Reznik I, Spivak B, Grisaru N, et al. (2002). A highly significant association between a
COMT haplotype and schizophrenia. The American Journal of Human Genetics 71(6): 12961302.
Silagy C, Lancaster T, Stead L, Mant D, Fowler G (2004). Nicotine replacement therapy for
smoking cessation (Review). Cochrane Database Syst Rev 3.
Silman A, Pearson J (2002). Epidemiology and genetics of rheumatoid arthritis. Arthritis Res
4(Suppl 3): S265-S272.
Simpson EH (1951). The interpretation of interaction in contingency tables. Journal of the
Royal Statistical Society. Series B (Methodological) 13(2): 238-241.
Sitara D, Aliprantis AO (2009). Transcriptional regulation of bone and joint remodeling by
NFAT. Immunological Reviews 233(1): 286-300.
Slavin RE (1984). Meta-analysis in education: How has it been used? Educational researcher
13(8): 6.
Smith GD, Egger M (1998). Meta-analysis: unresolved issues and future developments.
British Medical Journal 316(7126): 221.
250
References
Smolen J, Aletaha D, Koeller M, Weisman M, Emery P (2007). New therapies for treatment
of rheumatoid arthritis. The Lancet 370(9602): 1861-1874.
Söderlin MK, Kastbom A, Kautiainen H, Leirisalo-Repo M, Strandberg G, Skogh T (2004).
Antibodies against cyclic citrullinated peptide (CCP) and levels of cartilage oligomeric matrix
protein (COMP) in very early arthritis: relation to diagnosis and disease activity.
Scandinavian journal of rheumatology 33(3): 185-188.
Song G, Ouyang G, Bao S (2007). The activation of Akt/PKB signaling pathway and cell
survival. Journal of Cellular and Molecular Medicine 9(1): 59-71.
Spauwen J, Krabbendam L, Lieb R, Wittchen H, van Os J (2004). Does urbanicity shift the
population expression of psychosis? Journal of psychiatric research 38(6): 613-618.
Spector TD, Blake DR (1988). Effect of cigarette smoking on Langerhans' cells. Lancet
2(8618): 1028.
Spector TD, Silman AJ (2004). Does the Negative Association Between Rheumatoid Arthritis
and Schizophrenia Provide Clues to the Aetiology of Rheumatoid Arthritis? Vol. 26, pp 307310: Br Soc Rheumatology.
Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP, Li Y, Kurreeman
FAS, Zhernakova A, et al. (2010). Genome-wide association study meta-analysis identifies
seven new rheumatoid arthritis risk loci. Nature genetics.
Stambolic V, Suzuki A, De la Pompa JL, Brothers GM, Mirtsos C, Sasaki T, Ruland J,
Penninger JM, Siderovski DP, et al. (1998). Negative regulation of PKB/Akt-dependent cell
survival by the tumor suppressor PTEN. CELL-CAMBRIDGE MA- 95: 29-39.
Stastny P (1976). Mixed lymphocyte cultures in rheumatoid arthritis. J Clin Invest 57(5):
1148-1157.
Steer S, Abkevich V, Gutin A, Cordell HJ, Gendall KL, Merriman ME, Rodger RA, Rowley
KA, Chapman P, et al. (2007). Genomic DNA pooling for whole-genome association scans in
complex disease: empirical demonstration of efficacy in rheumatoid arthritis. Genes and
Immunity 8: 57-68.
Steere AC, Klitz W, Drouin EE, Falk BA, Kwok WW, Nepom GT, Baxter-Lowe LA (2006).
Antibiotic-refractory Lyme arthritis is associated with HLA-DR molecules that bind a
Borrelia burgdorferi peptide. Journal of Experimental Medicine 203(4): 961.
Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, Werge T,
Pietil‰inen OPH, Mors O, et al. (2009). Common variants conferring risk of schizophrenia.
Nature 460(7256): 744-747.
251
References
Stefansson H, Sarginson J, Kong A, Yates P, Steinthorsdottir V, Gudfinnsson E,
Gunnarsdottir S, Walker N, Petursson H, et al. (2003). Association of neuregulin 1 with
schizophrenia confirmed in a Scottish population. The American Journal of Human Genetics
72(1): 83-87.
Stolt P, Bengtsson C, Nordmark B, Lindblad S, Lundberg I, Klareskog L, Alfredsson L
(2003). Quantification of the influence of cigarette smoking on rheumatoid arthritis: results
from a population based case-control study, using incident cases Vol. 62, pp 835-841: BMJ.
Strous RD, Shoenfeld Y (2006). Schizophrenia, autoimmunity and immune system
dysregulation: a comprehensive model updated and revisited. Journal of autoimmunity 27(2):
71-80.
Strusberg I, Mendelberg R, Serra H, Strusberg A (2002). Influence of weather conditions on
rheumatic pain. Journal of Rheumatology 29(2): 335-338.
Struthers G, Scott D, Delamere J, Sheppeard H, Kitt M (1981). Smoking and rheumatoid
vasculitis. Rheumatology International 1(3): 145-146.
Suarez B, Duan J, Sanders A, Hinrichs A, Jin C, Hou C, Buccola N, Hale N, Weilbaecher A,
et al. (2006). Genomewide linkage scan of 409 European-ancestry and African American
families with schizophrenia: suggestive evidence of linkage at 8p23. 3-p21. 2 and 11p13. 1q14. 1 in the combined sample. The American Journal of Human Genetics 78(2): 315-333.
Sullivan P, Lin D, Tzeng J, van den Oord E, Perkins D, Stroup T, Wagner M, Lee S, Wright
F, et al. (2008). Genomewide association for schizophrenia in the CATIE study: results of
stage 1. Molecular psychiatry 13(6): 570-584.
Sullivan PF (2005). The genetics of schizophrenia. PLoS Medicine 2(7).
Sullivan PF, Kendler KS, Neale MC (2003). Schizophrenia as a complex trait: evidence from
a meta-analysis of twin studies. Archives of general psychiatry 60(12): 1187.
Susser E, Lin S (1992). Schizophrenia after prenatal exposure to the Dutch Hunger Winter of
1944-1945. Archives of general psychiatry 49: 983-983.
Suzuki A, Yamada R, Chang X, Tokuhiro S, Sawada T, Suzuki M, Nagasaki M, NakayamaHamada M, Kawaida R, et al. (2003). Functional haplotypes of PADI4, encoding
citrullinating enzyme peptidylarginine deiminase 4, are associated with rheumatoid arthritis.
Nature genetics 34(4): 395-402.
Suzuki A, Yamada R, Kochi Y, Sawada T, Okada Y, Matsuda K, Kamatani Y, Mori M,
Shimane K, et al. (2008). Functional SNPs in CD244 increase the risk of rheumatoid arthritis
in a Japanese population. Nature genetics 40(10): 1224-1229.
252
References
Symmons D (2003). Environmental factors and the outcome of rheumatoid arthritis. Best
Practice & Research Clinical Rheumatology 17(5): 717-727.
Takahata R, Moghaddam B (2002). Target-specific glutamatergic regulation of dopamine
neurons in the ventral tegmental area. Journal of neurochemistry 75(4): 1775-1778.
Takata Y, Inoue H, Sato A, Tsugawa K, Miyatake K, Hamada D, Shinomiya F, Nakano S,
Yasui N, et al. (2008). Replication of reported genetic associations of PADI4, FCRL3,
SLC22A4 and RUNX1 genes with rheumatoid arthritis: results of an independent Japanese
population and evidence from meta-analysis of East Asian studies. Journal of Human
Genetics 53(2): 163-173.
Talkowski M, Seltman H, Bassett A, Brzustowicz L, Chen X, Chowdari K, Collier D,
Cordeiro Q, Corvin A, et al. (2006). Evaluation of a susceptibility gene for schizophrenia:
genotype based meta-analysis of RGS4 polymorphisms from thirteen independent samples.
Biological Psychiatry 60(2): 152-162.
Tan HY, Nicodemus KK, Chen Q, Li Z, Brooke JK, Honea R, Kolachana BS, Straub RE,
Meyer-Lindenberg A, et al. (2008). Genetic variation in AKT1 is linked to dopamineassociated prefrontal cortical structure and function in humans. The Journal of clinical
investigation 118(6): 2200.
Taneja V, Behrens M, Mangalam A, Griffiths MM, Luthra HS, David CS (2007). New
humanized HLA-DR4-transgenic mice that mimic the sex bias of rheumatoid arthritis.
Tang F, Yue W, Ruan Y, Lu T, Liu Z, Zhang H, Han Y, Zhang D, Wang F (2007). Positive
association of the Disrupted-in-Schizophrenia-1 gene (DISC1) with schizophrenia in the
Chinese Han population. American Journal of Medical Genetics Part B: Neuropsychiatric
Genetics 144(3): 266-270.
Tarnawski AS, Pai R, Tanigawa T, Matysiak-Budnik T, Ahluwalia A (2010). PTEN silencing
reverses aging-related impairment of angiogenesis in microvascular endothelial cells.
Biochem Biophys Res Commun.
Tashiro E, Tsuchiya A, Imoto M (2007). Functions of cyclin D1 as an oncogene and
regulation of cyclin D1 expression. Cancer science 98(5): 629-635.
Teagarden JR (1989). Meta-analysis: whither narrative review? Pharmacotherapy 9(5): 274.
Thakur N, Hussain S, Kohaar I, Tabassum R, Nasare V, Tiwari P, Batra S, Bhambhani S, Das
BC, et al. (2009). Genetic variant of CCND1: Association with HPV-mediated cervical
cancer in Indian population. Biomarkers 14(4): 219-225.
253
References
The GAIN Collaborative Research Group (2007). New models of collaboration in genomewide association studies: the Genetic Association Information Network. Nature Genetics
39(9): 1045.
Thirunavukkarasu K, Pei Y, Moore TL, Wang H, Yu X, Geiser AG, Chandrasekhar S (2006).
Regulation of the human ADAMTS-4 promoter by transcription factors and cytokines.
Biochemical and Biophysical Research Communications 345(1): 197-204.
Thomson P, Wray N, Millar J, Evans K, Hellard S, Condie A, Muir W, Blackwood D,
Porteous D (2005). Association between the TRAX/DISC locus and both bipolar disorder and
schizophrenia in the Scottish population. Molecular psychiatry 10(7): 616-616.
Thomson W, Barton A, Ke X, Eyre S, Hinks A, Bowes J, Donn R, Symmons D, Hider S, et
al. (2007). Rheumatoid arthritis association at 6q23. Nature genetics 39(12): 1431.
Thomson W, Harrison B, Ollier B, Wiles N, Payton T, Barrett J, Symmons D, Silman A
(2001). Quantifying the exact role of HLA-DRB1 alleles in susceptibility to inflammatory
polyarthritis: results from a large, population-based study. Arthritis & Rheumatism 42(4):
757-762.
Tilling L, Townsend S, David J (2006). Methotrexate and Hepatic Toxicity in Rheumatoid
Arthritis and Psoriatic Arthritis. Clinical drug investigation 26(2): 55-62.
Tomairek H, Saeid M, Morsy T, Michael S (1982). Toxoplasma gondii as a cause of
rheumatoid arthritis. Journal of the Egyptian Society of Parasitology 12(1): 17.
Tomppo L, Hennah W, Lahermo P, Loukola A, Tuulio-Henriksson A, Suvisaari J, Partonen
T, Ekelund J, Lˆnnqvist J, et al. (2009). Association Between Genes of Disrupted in
Schizophrenia 1 (DISC1) Interactors and Schizophrenia Supports the Role of the DISC1
Pathway in the Etiology of Major Mental Illnesses. Biological Psychiatry 65(12): 1055-1062.
Torrey E, Miller J, Rawlings R, Yolken R (1997). Seasonality of births in schizophrenia and
bipolar disorder: a review of the literature. Schizophrenia Research 28(1): 1-38.
Torrey E, Rawlings R, Yolken RH (2000). The antecedents of psychoses: a case-control study
of selected risk factors. Schizophrenia Research 46(1): 17-23.
Torrey E, Yolken R (2003). Toxoplasma gondii and schizophrenia. Emerging infectious
diseases 9(11): 1375-1380.
Torrey EF, Yolken RH (1995). Could schizophrenia be a viral zoonosis transmitted from
house cats. Schizophr Bull 21(2): 167-171.
Torrey EF, Yolken RH (2001). The Schizophrenia–Rheumatoid Arthritis Connection:
Infectious, Immune, or Both? Brain Behavior and Immunity 15(4): 401-410.
254
References
Tsuang M (2000). Schizophrenia: genes and environment. Biological Psychiatry 47(3): 210220.
Tuokko J, Pushnova E, Yli-Kerttula U, Toivanen A, Ilonen J (1998). TAP2 alleles in
inflammatory arthritis. Scandinavian journal of rheumatology 27(3): 225-229.
Tuomi T, Heliovaara M, Palosuo T, Aho K (1990). Smoking, lung function, and rheumatoid
factors. British Medical Journal 49(10): 753.
Turesson C, Jarenros A, Jacobsson L (2004). Increased incidence of cardiovascular disease in
patients with rheumatoid arthritis: results from a community based study Vol. 63, pp 952-955:
BMJ.
Udalova I, Richardson A, Ackerman H, Wordsworth P, Kwiatkowski D (2002). Association
of accelerated erosive rheumatoid arthritis with a polymorphism that alters NF-{kappa} B
binding to the TNF promoter region. Rheumatology 41(7): 830.
Van Den Bogaert A, Schumacher J, Schulze T, Otte A, Ohlraun S, Kovalenko S, Becker T,
Freudenberg J, Jˆnsson E, et al. (2003). The DTNBP1 (dysbindin) gene contributes to
schizophrenia, depending on family history of the disease. The American Journal of Human
Genetics 73(6): 1438-1443.
van den Heuvel APJ, de Vries-Smits AMM, van Weeren PC, Dijkers PF, de Bruyn KMT,
Riedl JA, Burgering BMT (2002). Binding of protein kinase B to the plakin family member
periplakin. Journal of Cell Science 115(20): 3957.
Van Os J, Bak M, Hanssen M, Bijl R, De Graaf R, Verdoux H (2002). Cannabis use and
psychosis: a longitudinal population-based study. American Journal of Epidemiology 156(4):
319.
Vasishta A (2002). Diagnosing early-onset rheumatoid arthritis: the role of anti-CCP
antibodies. American Clinical Laboratory 21(7): 34-36.
(1997). Efficacy of multidisciplinary team care programs in rheumatoid arthritis*. Elsevier.
pp 110-122.
Wakitani S, Murata N, Toda Y, Ogawa R, Kaneshige T, Nishimura Y, Ochi T (1997). The
relationship between HLA-DRB1 alleles and disease subsets of rheumatoid arthritis in
Japanese. Rheumatology 36(6): 630.
Waldron-Lynch F, Adams C, Amos C, Zhu D, McDermott M, Shanahan F, Molloy M,
OíGara F (2001). Tumour necrosis factor 5 promoter single nucleotide polymorphisms
influence susceptibility to rheumatoid arthritis(RA) in immunogenetically defined multiplex
RA families. Genes and Immunity 2(2): 82-87.
255
References
Watanabe Y, Nunokawa A, Kaneko N, Muratake T, Arinami T, Ujike H, Inada T, Iwata N,
Kunugi H, et al. (2009). Two-stage case–control association study of polymorphisms in
rheumatoid arthritis susceptibility genes with schizophrenia. Journal of human genetics 54(1):
62-65.
Weinberger D (1987). Implications of normal brain development for the pathogenesis of
schizophrenia. Archives of General Psychiatry 44(7): 660-669.
Weiser M, Reichenberg A, Rabinowitz J, Kaplan Z, Caspi A, Yasvizky R, Mark M, Knobler
H, Nahon D, et al. (2003). Self-reported drug abuse in male adolescents with behavioral
disturbances, and follow-up for future schizophrenia. Biological psychiatry 54(6): 655-660.
Wells G, Haguenauer D, Shea B, Suarez-Almazor ME, Welch VA, Tugwell P (2001).
Cyclosporine for treating rheumatoid arthritis (Cochrane Review). The Cochrane Library 4.
West AE, Griffith EC, Greenberg ME (2002). Regulation of transcription factors by neuronal
activity. Nature Reviews Neuroscience 3(12): 921-931.
Wiedholz LM, Owens WA, Horton RE, Feyder M, Karlsson RM, Hefner K, Sprengel R,
Celikel T, Daws LC, et al. (2007). Mice lacking the AMPA GluR1 receptor exhibit striatal
hyperdopaminergia and ‘schizophrenia-related’behaviors. Molecular psychiatry 13(6): 631640.
Williams N, Preece A, Morris D, Spurlock G, Bray N, Stephens M, Norton N, Williams H,
Clement M, et al. (2004a). Identification in 2 independent samples of a novel schizophrenia
risk haplotype of the dystrobrevin binding protein gene (DTNBP1). Archives of general
psychiatry 61(4): 336.
Williams N, Preece A, Spurlock G, Norton N, Williams H, McCreadie R, Buckland P,
Sharkey V, Chowdari K, et al. (2004b). Support for RGS4 as a susceptibility gene for
schizophrenia. Biological Psychiatry 55(2): 192-195.
Wilson K, Goldsmith C (1999). Does smoking cause rheumatoid arthritis? Journal of
rheumatology 26(1): 1-3.
Winslow MM, Pan M, Starbuck M, Gallo EM, Deng L, Karsenty G, Crabtree GR (2006).
Calcineurin/NFAT signaling in osteoblasts regulates bone mass. Developmental cell 10(6):
771-782.
Wolfe F (2000). The effect of smoking on clinical, laboratory, and radiographic status in RA.
Journal of Rheumatology 27(3): 630-637.
Wordsworth BP, Bell JI (1992). The immunogenetics of rheumatoid arthritis. Springer
Seminars in Immunopathology 14(1): 59-78.
256
References
Wordsworth BP, Lanchbury JS, Sakkas LI, Welsh KI, Panayi GS, Bell JI (1989). HLA-DR4
subtype frequencies in rheumatoid arthritis indicate that DRB1 is the major susceptibility
locus within the HLA class II region. Proc Natl Acad Sci US A 86(24): 10049-10053.
World Health Organisation (1992). International statistical classification of diseases and
related health problems. Tenth revision. World Health Stat Q 41(1): 32-36.
Wright P, Nimgaonkar VL, Donaldson PT, Murray RM (2001). Schizophrenia and HLA: a
review. Schizophrenia Research 47(1): 1-12.
Wright P, Psych MRC, Takei N, Rifkin L, Murray RM, Sc D, Psych FRC (1995). Maternal
influenza, Obstetric Complications, and Schizophrenia. American Journal of Psychiatry.
WTCCC (2007). Genome-wide association study of 14,000 cases of seven common diseases
and 3,000 shared controls. Nature 447(7145): 661-678.
Xavier RJ, Rioux JD (2008). Genome-wide association studies: a new window into immunemediated diseases. Nature Reviews Immunology 8(8): 631.
Xiromerisiou G, Hadjigeorgiou G, Papadimitriou A, Katsarogiannis E, Gourbali V, Singleton
A (2008). Association between AKT1 gene and Parkinson's disease: A protective haplotype.
Neuroscience Letters 436(2): 232-234.
Xu MQ, Xing QH, Zheng YL, Li S, Gao JJ, He G, Guo TW, Feng GY, Xu F, et al. (2007).
Association of AKT1 gene polymorphisms with risk of schizophrenia and with response to
antipsychotics in the Chinese population. J Clin Psychiatry 68(9): 1358-1367.
Yaykasli KO, Oohashi T, Hirohata S, Hatipoglu OF, Inagawa K, Demircan K, Ninomiya Y
(2009). ADAMTS9 activation by interleukin 1 via NFATc1 in OUMS-27 chondrosarcoma
cells and in human chondrocytes. Molecular and Cellular Biochemistry 323(1): 69-79.
Yolken R, Bachmann S, Rouslanova I, Lillehoj E, Ford G, Torrey E, Schroeder J (2001).
Antibodies to Toxoplasma gondii in individuals with first episode schizophrenia. Clinical
Infectious Diseases 32: 842-844.
Yoo SA, Park BH, Park GS, Koh HS, Lee MS, Ryu SH, Miyazawa K, Park SH, Cho CS, et
al. (2006). Calcineurin is expressed and plays a critical role in inflammatory arthritis. The
Journal of Immunology 177(4): 2681.
Yusuf S, Peto R, Lewis J, Collins R, Sleight P (1985). Beta blockade during and after
myocardial infarction: an overview of the randomized trials. Progress in cardiovascular
diseases 27(5): 335.
257
References
Zai C, Romano-Silva M, RUDI H, Zai G, DeLuca V, M¸ller D, King N, Voineskos A,
Meltzer H, et al. (2008). Genetic study of eight AKT1 gene polymorphisms and their
interaction with DRD2 gene polymorphisms in tardive dyskinesia. Schizophrenia Research
106(2-3): 248-252.
Zammit S, Allebeck P, Andreasson S, Lundberg I, Lewis G (2002). Self reported cannabis use
as a risk factor for schizophrenia in Swedish conscripts of 1969: historical cohort study.
British Medical Journal 325(7374): 1199.
Zammit S, Allebeck P, Dalman C, Lundberg I, Hemmingson T, Owen M, Lewis G (2003).
Paternal age and risk for schizophrenia. The British Journal of Psychiatry 183(5): 405.
Zan Y, Haag JD, Chen KS, Shepel LA, Wigington D, Wang YR, Hu R, Lopez-Guajardo CC,
Brose HL, et al. (2003). Production of knockout rats using ENU mutagenesis and a yeastbased screening assay. Nature biotechnology 21(6): 645-651.
Zanelli E, Breedveld FC, de Vries RRP (2000). Hla class II association with rheumatoid
arthritis:: Facts and interpretations. Human Immunology 61(12): 1254-1261.
Zeggini E, Scott L, Saxena R, Voight B, Marchini J, Hu T, de Bakker P, Abecasis G,
Almgren P, et al. (2008). Meta-analysis of genome-wide association data and large-scale
replication identifies additional susceptibility loci for type 2 diabetes. Nature genetics 40(5):
638.
Zervou M, Sidiropoulos P, Petraki E, Vazgiourakis V, Krasoudaki E, Raptopoulou A,
Kritikos H, Choustoulaki E, Boumpas D, et al. (2008). Association of a TRAF1 and a STAT4
gene polymorphism with increased risk for rheumatoid arthritis in a genetically homogeneous
population. Human immunology 69(9): 567-571.
Zhang F, Sarginson J, Crombie C, Walker N, StClair D, Shaw D (2006a). Genetic association
between schizophrenia and the DISC1 gene in the Scottish population. AMERICAN
JOURNAL OF MEDICAL GENETICS. PART B 141(2): 155.
Zhang J, Schweers B, Dyer MA (2006b). The first knockout mouse model of retinoblastoma.
American Journal of Ophthalmology 142(3): 535-535.
Zhang X, Tochigi M, Ohashi J, Maeda K, Kato T, Okazaki Y, Kato N, Tokunaga K, Sawa A,
et al. (2005). Association study of the DISC1/TRAX locus with schizophrenia in a Japanese
population. Schizophrenia research 79(2-3): 175-180.
Zhao X, Shi Y, Tang J, Tang R, Yu L, Gu N, Feng G, Zhu S, Liu H, et al. (2004). A case
control and family based association study of the neuregulin1 gene and schizophrenia. British
258
References
Zhernakova A, Alizadeh B, Bevova M, van Leeuwen M, Coenen M, Franke B, Franke L,
Posthumus M, van Heel D, et al. (2007). Novel association in chromosome 4q27 region with
rheumatoid arthritis and confirmation of type 1 diabetes point to a general risk locus for
autoimmune diseases. The American Journal of Human Genetics 81(6): 1284-1288.
Zhou XY, Tomatsu S, Fleming RE, Parkkila S, Waheed A, Jiang J, Fei Y, Brunt EM, Ruddy
DA, et al. (1998). HFE gene knockout produces mouse model of hereditary hemochromatosis.
Proceedings of the National Academy of Sciences of the United States of America 95(5):
2492.
Zimmermann G, Favrod J, Trieu VH, Pomini V (2005). The effect of cognitive behavioral
treatment on the positive symptoms of schizophrenia spectrum disorders: A meta-analysis.
Schizophrenia Research 77(1): 1-9.
Zornberg G, Buka S, Tsuang M (2000). Hypoxic-ischemia-related fetal/neonatal
complications and risk of schizophrenia and other nonaffective psychoses: a 19-year
longitudinal study. American Journal of Psychiatry 157(2): 196.
259
Appendices
APPENDICES APPENDIXA:PrimersequencesandAssayconditions
APPENDIXB:WTCCCandGAINGenotyping
APPENDIXC: HardyͲWeinbergequilibriumanalysis
APPENDIXD:LDplotofAKT1SNPs
APPENDIXE:LDplotofDISC1SNPs
APPENDIXF:LDPlotofNFATSNPS
260
Appendices
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Table A.84: Primer sequences for mouse microsatellite markers
Marker
Chromosome
Position
)(cM)
Forward Primer
Reverse Primer
5’ to 3’
3’ to 5’
B6
NOD
Amplicon Size (bp)
D1Mit373
1
26.5
AGATAGCCACTCAGTTGAATACCC
CTTTACAGGTCTTGAAAGCATGG
124
138
D1Mit236
1
45.4
ATACCCACCTAGCCTTTGTATAGG
GGAAGAAGGCTCAGCAAGTG
143
142
D1Mit490
1
106
ATCTGAATACCTACAAGGACATACACA
GAACGTACTTTAAAAAGGAGCAGG
120
124
D1Mit102
1
149.1
AAATACCAGCAAAACAATAAAGGC
AAATACCAGCAAAACAATAAAGGC
120
122
D1Mit209
1
193.3
TCCATCCATACTCCTGTCTGC
CAAGGACTAGGGCTGTCACTG
105
99
D2Mit1
2
3.8
CTTTTTCGTATGTGGTGGGG
AACATTGGGCCTCTATGCAC
124
120
D2Mit56
2
70.5
ATACTTGAGCAGATGTCTCAGGC
CACAGCAGCATTATGAAGATACTG
122
114
D2Mit305
2
128.5
CTCAGAAAACATGCAATTGAGG
ATGAGTGCAAACCAAATAAAATTG
137
125
D2Mit343
2
169.1
GTGTGTATTGGGGTACATGGG
GATGGTTCTTGAGGAACACTCC
183
173
D2Mit296
2
31.2
CAACTGTAAATCCAGTCGTAGGG
CTCTGCTGAGGTTACTGTGGG
154
146
D2Mit147
2
172.2
CATCCCTAAGACAAGCAACTCC
GTCACAATGTCCTTCTCCATCA
117
121
D3Mit46
3
29.9
ATCCCCCACCCAACTCTAAC
TTCCCCAGGGAAATCTCTCT
166
162
D3Mit25
3
56.6
GTCTGGGTCGTCAGTGGC
TGGAGGCTACCATCTCCAAG
134
132
D3Mit230
3
82.4
GAATGGCCAGGGTAAAATCA
TTGAACTCCAAACTTGAGACCA
152
146
D3Mit84
3
143.4
CTGCTCAGAGAACAGCAGATG
TCGAGTCTGTGATTGCTTGG
164
156
D3Mit147
3
148.4
TCTGCCTCTGTTAGATAGATATCCG
TTGTTCATCTATCCTCTGAAGTTCC
132
144
D4Mit111
4
5.3
TGAGATGTTGGCATACTGTGTG
TTGTAAGCCTAACTTTATCCACCC
126
126
D4Mit26
4
42.5
CTGATGGTAGCAGAATCAGGC
CGGTAGTTTTTCAAAGATCGTG
202
194
D4Mit166
4
93.5
AGTTTCCTTTCTCTTCTACTTGTGTG
AGGGCATAGGAAACTTTCAGG
199
185
D4Mit204
4
133
CTGCTGCAGCGATTCTCTC
TCAGGCACCTAAGTACATGTGC
104
118
D4Mit251
4
136.5
AAAAATCGTTCTTTGACTTCTACATG
TTTAAAAGGGTTTCTTTATCCTGTG
116
134
D4Mit254
4
153.1
AATACAATTATCATTTGCATTGTGG
TCGTGGTGGACCTTATCTCC
135
137
D5Mit145
5
73.3
TATCAGCAATACAGACTCAGTAGGC
TGCCCCTTAAATTCATGGTC
149
97
261
Appendices
D5Mit164
5
131.0
TCAAGAAAGAACTACTGATGTCAACC
GGAATAGAAAAGTAACTGAGACAGGC
142
123
D5Mit80
5
47.8
TTTCACCATCATGGTTGATCA
CAGGTGCCCTGCATTACC
166
162
D5Mit94
5
112.7
ACCCACTGTCCATCTACAAACC
GATTTCTCTGCTCTGGGTGG
126
128
D5Mit200
5
63.9
CACAGAGAAGAATCTGCGAGC
TTGCAAAGTGTTTTAATCAGTATTTG
107
108
D5Mit239
5
107.8
ATTGCAGACATAAAGGATATTTTGG
GCCAGCCTGGCTTACATAAG
150
152
D5Mit292
5
141.2
GTCTTACTCCTGCCTGAACCC
TTACCTTACACTGAGACCAGAAAGC
114
112
D6Mit86
6
4.41
GACCAAACCAGAAGCCCCT
GGAATGTAGCCCTAAGTTGGA
130
114
D6Mit188
6
75.4
GACCAAACCAGAAGCCCCT
GGAATGTAGCCCTAAGTTGGA
130
114
D6Mit105
6
107.7
CTTTAGTCATTATTAGGATTGCCTATG
TGGGATAGCATTGGAAACGT
130
139
D6Mit109
6
118.2
CACCATACACAAATGCCTGC
TGAATACGGTGGAAAGTGAGC
132
119
D6Mit31
6
92.7
GTAGCAAATTTCAGGACAGCG
ACATGGTGTAAACCCTGTTTCC
198
192
D6Mit314
6
48.6
CCAATCAGTCACTTATTTCATCTCA
GGCTATTGTGTGACTTTGAGAGA
200
178
D6Mit14
6
145.6
ATGCAGAAACATGAGTGGGG
CACAAGGCCTGATGACCTCT
160
174
D7Mit178
7
3.5
ACCTCTGATTTCAGAACCCTTG
TAGAGAGCCACTAGCATATCATAACC
201
165
D7Mit228
7
47.2
ATTCTTGGCCTTTTCTTGTAACA
AAACCTCCACACTGACTTCCA
148
142
D7Mit44
7
137.1
TTCTGGCCTCTGTGAAGTAGTG
GTGAAACCATGGTGCAGATG
190
190
D7Mit40
7
124
GTCAACAGTCAGGAAAGCTGG
CAGATGCTTGTATTTGCAAAGC
204
204
D8Mit291
8
33.4
GTTTAGTGTGTTTGTATGAGCATGC
AACAAGAATGAAGTGTTCATGCA
96
92
D8Mit242
8
104.3
TGTGCAACCAATTTCTTCCA
CCCATGATTTATTCAGACTGAGG
166
196
D8Mit24
8
35.7
ACCTCCACACGATTACAGTGG
ATGATGTCACCAGCAACTTCC
160
166
D8Mit226
8
37.7
TTTTTCATCTGGAAAAGCCG
TCCCATAAAACCAACACTACCC
125
107
D8Mit225
8
37.6
CCCTCTTCTTCCCTTCCACT
TTTGTTGTTGCTTGCTTTGG
104
102
D8Mit113
8
111.4
GGTCACATAATAAGAAAGCCCG
AACCCGTTAGGAGGACCG
138
160
D8Mit125
8
35.9
ATCGCTCTATCTACTCATCTATTCACA
GACCCTGACTCTTAATCCTAGTGC
133
155
D8Mit204
8
20.0
GAACAGAACTTATGGAATGAACACA
CAGCATTGTTGGCCACAG
148
150
D8Mit223
8
27.8
TGTGTATGTTTCTGTCTCTCTCTAATG
ATGCTGAGGGATCTGACACC
126
124
D8Mit64
8
33.9
CTAGAACCATGTGTGCGCAT
TGCATGATCTATAGAAACCCCT
166
164
D8Mit249
8
83.5
AAACTCACACACACAGAGACAACA
GCCCCAGTGTGACTAAGGAG
148
176
D9Mit205
9
37.1
AATAGCCTACTCTGGATTCACAGG
TACCTTCTCCTCTTTGGTTTTTG
194
180
262
Appendices
D9Mit196
9
85.8
GCCTTCTGTTCAGAACTTTCTG
TCTGTATTTAAGCATGCATGTGC
148
156
D9Mit104
9
65.8
TTAATTGAGACGCACTTTGGG
AGGGGTCATTAGAGTTGGGG
125
121
D9Mit191
9
46.5
GTAAGTATGCCTGCGGAGGA
CAGAAGACCGCTTTTCAAGG
115
99
D9Mit182
9
101.4
GTGAAATTGGTTATGTAAATGTCTGA
GAGATGACTAGGGTGAACTGGG
99
115
D10Mit213
10
20.1
CTCCTCCTACTGATTGTCCCC
GGGACAAACTTTTAAAAATTGCA
150
156
D10Mit109
10
41.3
GACCTGGATTCAGTCCTCCA
CCAAAATAAAACGGAAGAGTGG
147
-1
D10Mit95
10
92
CCAGCCTAGAAAACCAAGCA
ACAGTGCTTCCGGAAAAATG
201
167
D10Mit180
10
117.6
GACCTTCCTTTATACACAAGTCATAGC
GTGGTACAGAACTTAGGTGTTTAATTG
134
156
D11Mit51
11
36.2
CCAAACAGGGTCTGTTTTATTC
TAACAGGGTGAGTTTAGTGAAACA
140
124
D11Mit349
11
55.6
AGTATCAGAAGATCCAGTTGGAGG
GTAGAAAAAGATACCCAGTGTCAGC
118
78
D11Mit98
11
96.7
CAATTGGAGGAAAGCAGGAG
TGTTAACTTATTACAGGGACGTGC
130
112
D11Mit4
11
68.4
CAGTGGGTCATCAGTACAGCA
AAGCCAGCCCAGTCTTCATA
246
244
D11Mit212
11
88.7
CTCTGGTCTCTCTGTATACATGTGC
AGCAACTGGGGCATTTAATG
148
166
D11Mit116
11
76.8
CACCAGCACCCACAAAAATA
ACCACTACCAATTTCAGAAGTATGC
138
142
D11Mit333
11
108.6
CATGTGGTTATTTTCTAGCCCC
AGGCATCAATAACTATTTTTCAGTG
126
108
D11Mit77
11
17.7
GTATTCAAATGACTTCTGCCTGG
TTGAAATGGTCTTCAAGTGGC
152
162
D11Mit263
11
55.6
GAAACCATTTTAAAATATACAGTTCGG
CCAGGGTTAGGTAGGTATGGC
144
148
D11Mit112
11
58.4
TCCTTCCCTTTCATCAATGG
CCCAAATGCCTCTCATGAGT
83
92
D11Mit60
11
69.5
AGAGAGGCAAAAATTCCAAGC
CTTCCTGATGGTAGGATTTAGGC
316
308
D11Mit79
11
19.6
TTCTTGGTCGTAGCCCTCAC
GACACACAACACCTCGCG
152
146
D11Mit36
11
83.7
CCAGAACTTTTGCTGCTTCC
GTGAGCCCTAGGTCCAGTGA
234
236
D12Mit172
12
47.2
AACTGAAATCGCATTACAAAACC
TAATATTGCGAGTTAGAAATGACCA
199
185
D12Mit77
12
100.3
TCCAGGTTCACTGAGAGACAA
GCAGCACCACGTCATGAC
191
190
D13Mit3
13
20.5
TCAGGCTCATCCCAGATACC
TTTTGCAGAGAACACACACC
159
164
D13Mit76
13
111.4
ATGCACCTGTCTAAATGTGTGC
AGAGGGACTGTGGGACTGTG
106
90
D14Mit14
14
30.5
GCACATTCCAAAACACATGC
GGGATGGTGTCAATCAATCC
270
-1
D14Mit37
14
63
GTCGATGGATGACTGCTGC
CATGGGGACTCAGGAGATTG
136
94
D14Mit160
14
40
CAAAATTAGCAAGGATTCCAGG
AGTTTAATCTCTTGGCTTTTGGG
141
141
D14Mit265
15
101.7
TCAAGAAATGACTCTTATCTACACACA
AACAGCAAGATGTCAGCAAGA
148
150
263
Appendices
D15Mit107
15
84.2
CAACACTTATACACTTGTGTCAGGG
TCATGGTTGGAACAGCAGAC
151
145
D15Mit12
15
3.2
ATGGACACCTGACACTGCAA
AAGGGCTTTTACCTGGGAAT
150
-
D15Mit209
15
61.5
TTGTGCTTCACTAGATGTAGACCA
TTTTATAGTTGCACATAAGCAGCA
127
121
D15Mit193
15
97.8
TTGTGTAAGCCAATCTAATAAAGCC
GTTGCTGTGCTCATGGTGTC
130
110
D16Mit146
16
23.7
ACCAATCTGGAGATATGTTACAAGG
TGGAAGACACATACTCTCTCTCTCA
116
112
D16Mit106
16
98.1
GTTGTTGGTCCACCATACCC
GGGTATGAGGTACAATGCTTTACC
148
140
D16Mit163
16
7.33
ACCTATTTCCAGGTTAAAATAAAAGTG
ATGTTGACTGATCGTGTATGTGC
117
113
D16Mit87
16
9.8
AGAGCACAGAGTCCACTTACTGG
GGTGCAGGCATAGGCAATAT
139
141
D16Mit131
16
7.32
TGGTGGTGGTGTTGATGGTA
AAGACCATTTCTAATAAACAACACCC
144
180
D16Mit9
16
6.7
TCTTGCTCTGGTATCAACTACAGG
CCTCCTTGCCCAGCTAAAC
146
-
D16Mit152
16
85.8
AGAGACCTCTGGGGTGGG
TTCAAGATAGACTATTCTGGAAAAAGC
106
-1
D17Mit49
17
45.4
TCTTAGAACTCACATCAATGCCA
TCCAGGGACCTTTTGTCTTG
250
256
D17Mit113
17
12.2
TCTGTCTCCTCCGTACTGGG
GTCAATAAGTTCAATCACTGAACACA
127
137
D17Mit139
17
52.8
AGACATGTGAGTACTGCACAGACA
ATGATGACATACCTCCTAGTAGTCCC
136
138
D17Mit221
17
90.5
AACCAGATCATTAACAGTAATAAAGCA
TTGTGGCAAAAACAACCAAA
139
135
D18Mit111
18
21.7
TGCTAGCACTTCCTCTGGAA
TAGAAATCCCTCATTTGCGG
117
105
D18Mit60
18
32.6
ACCTGACACCATTTTCAGGC
ATCCTTGAGCCTGTTAAAAGACA
186
188
D18Mit37
18
42.6
GTGGGGGGGAGAATTGTC
ATGAGAGAGCCTATCTCAAACCC
172
180
D18Mit124
18
57.6
CCCAAATGGGGTGTCTTTTA
CTGCCACACATTTGTGTGTATG
151
139
D18Mit184
18
67
CACACATGTGTAGGTAGGTAGGTAGG
CGCACAAGGACTACTGAAACA
172
157
D18Mit207
18
68.8
TGGTTGACTGATAGAAAATATGAACC
GTGCCAGCATGCATACATG
122
134
D18Mit154
18
75.8
GATGCATCTGATGCCTTGC
AAACACCAAAACAACAACAAACC
154
160
D18mit106
18
77.7
CCAGGCCAGAAAATCAAAGA
GTTGTGGTGGTGGTATGTGC
115
135
D19Mit68
19
3.6
CCAATACAAATCAGACTCAATAGTCG
AGGGTCTCCCCATCTTCCTA
136
122
D19Mit53
19
45.2
GCACGCCACAACTCAGAG
AGAAAAGGTTCTCCTACCTCTCG
110
100
D19Mit106
19
95.2
CCTTTTTTTTTTTAACCAGACAGG
ATCAATGAATGAAGAACAAATAGTTTC
124
124
264
Appendices
Table A.85: Primer sequences for SNP markers
Locus
Forward Primer
Reverse Primer
5’ to 3’
3’ to 5’
Amplicon Size
(bp)
rs2494731
AACAAGGTGCACAGGATGAG
GACCAGAGACCTTGCTAATT
256
rs1130214
AAACGGGAGTCCAGAGCCCTCCAGCGCAAGT
CATGTTGGCCAAATGAATGAACCAGATTCAG
119
rs7146030
GGAGTCCCCTTTAGAGGGTTAGAGCAT
CTTTACAGGGAGCTCATGTGGTGGACAAGC
212
rs1013781
TAACAGTTCCTACATCAGCTCTAA
TTATTTACAGAACATATTGGCATTGGTTCA
157
rs7146661
TTCAGCTGTGTCTTGAATACAGCATGTAGA
AAGGGCCTTAACAGGGCACTAATCCTGTT
231
rs4983386
ACTGATGCCAATGATGGCAGCCT
AAGCAGAGGAACCGAGTAGCTGA
278
rs4658966
TTCTACCAGGAAAATTGCTGTCTGGGAGCCG
TATCACGGCCCCAGCCCCACATGTATTAGT
240
rs701158
AAACTGTGGGGGTTGGAGAATAGATTTTCG
ATGTGAGCCTGGTTTTGAAATAATCAGCCTG
257
rs872625
GGTTTCCAAGTGTCAGGAACTGCTCTAAGA
GGACTTCGTGTTAGGCTGGAGTCATTCATT
187
rs872624
GTCTTCCCTCTTGGAATGTCAGCACATAC
GCACTATTCTAACAGCTAGAGCTATGACGG
191
rs9431714
CTGAAAATGTGAATACATATATTCAAGCCG
ACATAAATGCAGTAGCATGAGAAGGTA
145
rs821577
ACCTGTGCTTTAAGGTCATCCTGATTTCCT
CTCACCCATACATCCACCTAAGGTTAGAT
161
Rs821585
CGCTCTGAGAATGACTGCTGC
ACTGGGGCCACAGAGGTGAAT
143
NFATC1
rs2002311
TTTCCTCTGCTTGGGGGTTTCAGGAAGAAT
ACCCCAGGAGCGAGTTTGGGACATATTTAT
163
NFATC2
rs8119787
TAGTCTCGAACTCCTGACCTCAAGTGATCTG
TTTTAGGTGGTCCACCAGTGAGGACGTTGTA
182
AKT1
AKT1 downstream
DISC1
Marker
265
Appendices
Table A.86: Assay conditions for mouse microsatellite markers contained in the assay.
Locus
Mg2+ (mM)
conditions
Tm (oC) Conditions
Locus
Mg2+ (mM)
conditions
Tm (oC) Conditions
D1Mit373
2
60
D10Mit180
4
60
D1Mit490
3
55
D11Mit263
2
65
D1Mit102
2
55
D11Mit4
4
60
D1Mit209
3
60
D11Mit333
4
60
D2Mit1
3
55
D12Mit172
2
60
D2Mit56
3
60
D12Mit77
3
55
D2Mit305
4
55
D13Mit3
3
50
D2Mit343
4
60
D13Mit76
3
55
D2Mit147
3
60
D14Mit160
3
50
D3Mit25
2
60
D14Mit265
3
45
D3Mit84
3
60
D15Mit107
2
60
D3Mit147
2
60
D15Mit193
4
55
D4Mit166
3
50
D15Mit12
4
60
D4Mit204
2
60
D15Mit209
4
60
D4Mit254
3
60
D16Mit152
3
60
D5it80
2
65
D16Mit163
3
60
D5Mit145
3
60
D16Mit131
3
60
D6Mit86
3
55
D16Mit106
3
60
D6Mit105
2
50
D17Mit49
2
60
D7Mit178
4
50
D17Mit221
2
60
D7Mit40
2
55
D18Mit60
2
60
D8Mit242
4
60
D18Mit124
2
60
D8Mit113
2
60
D18Mit184
2
60
266
Appendices
D9Mit205
4
60
D18Mit207
4
60
D9Mit104
4
60
D18Mit154
4
65
D10Mit213
3
55
D19Mit53
2
55
D10Mit109
3
55
D19Mit68
2
60
D10Mit95
5
60
Table A.87: Assay conditions for human SNP markers
Locus
Marker
Chromosome
Position
Mg2+ (mM)
conditions
Tm (oC)
Conditions
Restriction Enzyme
AKT1
rs2494731
14
104,308,725
5
60
Dpn I
rs1130214
14
104,330,779
3
55
RSA1
rs7146030
14
104,138,418
3
60
HhaI
rs1013781
14
104,159,654
-
-
Pm1I
rs7146661
14
104,161,600
4
55
Mbo1
rs4983386
14
104,281,252
4
55
BstUI
rs4658966
1
230,145,237
2
60
Aci I
rs701158
1
230,139,751
3
55
Msp I
rs872625
1
230,141,596
5
55
TSP509I
rs872624
1
230,141,523
2
55
Nla III
rs9431714
1
230,008,871
3
55
Bsr I
rs821577
1
232,067,057
3
60
Dpn II
Rs821585
1
232,087,449
-
-
Nla III
NFATC1
rs2002311
18
75,274,967
5
55
Taq I
NFATC2
rs8119787
20
49,580,478
5
55
Cvi QI
AKT1 downstream
DISC1
!
267
Appendices
!
"##$%&'(!)*
+,---!"%&!."'%!.$%/,0#'%.!!
Table B.88: BC|SNPmax genotyping information for DISC1 SNPs in the WTCCC and GAIN cohorts. SNPs of interest for this thesis are highlighted in blue. Where known, the RS
numbers were listed.
COHORT
SNP
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
RS4658966
RS1341552
RS701158
RS821577
RS1341555
RS9431714
RS872625
RS821585
RS872624
RS16854914
RS16855239
RS7416743
RS11122348
RS4658958
RS12735632
RS16854756
RS11583715
RS16854785
RS928099
RS2793091
RS16855725
RS843979
RS2806465
RS16855254
Alleles
Minor
Major
C
T
C
T
G
A
G
T
T
C
A
G
G
A
T
C
A
G
G
A
T
G
T
C
T
C
G
A
T
G
C
T
C
T
C
T
T
C
A
G
T
G
G
C
G
C
C
T
Cases
0.101
0.003
0.420
0.421
0.417
0.375
0.205
0.010
0.139
0.007
0.203
0.031
0.204
0.204
0.203
0.000
0.317
0.031
0.277
0.264
0.000
0.336
0.441
0.019
MAF
Controls
0.130
0.009
0.445
0.445
0.440
0.398
0.224
0.015
0.153
0.010
0.190
0.026
0.192
0.192
0.191
0.001
0.331
0.026
0.289
0.276
0.000
0.325
0.429
0.022
268
P
OR
0.000
0.000
0.017
0.022
0.027
0.031
0.032
0.036
0.062
0.121
0.123
0.135
0.142
0.146
0.152
0.168
0.171
0.172
0.182
0.205
0.209
0.255
0.259
0.282
0.747
0.296
0.904
0.908
0.910
0.908
0.896
0.660
0.894
0.690
1.084
1.207
1.080
1.079
1.078
0.000
0.938
1.187
0.939
0.942
NA
1.052
1.049
0.852
CI
L95
0.644
0.150
0.832
0.836
0.838
0.831
0.810
0.447
0.795
0.430
0.978
0.943
0.974
0.974
0.973
0.000
0.857
0.928
0.857
0.858
NA
0.964
0.966
0.636
U95
0.867
0.584
0.982
0.986
0.989
0.991
0.991
0.976
1.006
1.106
1.202
1.545
1.197
1.196
1.196
NA
1.028
1.519
1.030
1.033
NA
1.148
1.140
1.141
Appendices
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
RS12406771
RS11122325
RS4366301
RS980989
RS11122381
RS1754607
RS16856152
RS6668739
RS7533169
RS12091717
RS12757857
RS16854957
RS9432013
RS1754606
RS11590192
RS12027635
RS4658883
RS6687236
RS821645
RS12730369
RS1015101
RS16855860
RS1009587
RS2356710
RS821627
RS9803690
RS1407601
RS1538974
RS9431708
RS1407600
RS11122374
RS17748239
RS17817356
RS821628
T
A
C
T
G
A
T
A
A
G
C
A
C
C
C
A
C
T
A
T
G
A
A
A
T
G
G
T
G
G
G
T
A
T
C
G
G
G
A
G
C
G
G
C
T
C
T
T
T
G
T
C
G
C
A
G
G
G
C
A
T
G
A
A
A
C
G
C
0.051
0.195
0.395
0.240
0.223
0.362
0.050
0.001
0.193
0.172
0.439
0.164
0.056
0.284
0.468
0.215
0.185
0.212
0.314
0.197
0.401
0.000
0.401
0.212
0.357
0.419
0.141
0.319
0.473
0.141
0.110
0.314
0.417
0.284
0.046
0.203
0.405
0.249
0.214
0.352
0.045
0.000
0.202
0.179
0.449
0.157
0.061
0.275
0.477
0.223
0.192
0.219
0.306
0.190
0.393
0.000
0.393
0.219
0.350
0.412
0.146
0.325
0.479
0.145
0.113
0.318
0.422
0.280
269
0.295
0.295
0.296
0.301
0.302
0.308
0.312
0.321
0.323
0.338
0.340
0.342
0.346
0.351
0.357
0.357
0.364
0.388
0.393
0.415
0.425
0.426
0.436
0.443
0.472
0.495
0.497
0.544
0.553
0.576
0.599
0.634
0.644
0.646
1.107
0.946
0.956
0.951
1.054
1.046
1.104
3.165
0.949
0.949
0.961
1.056
0.919
1.045
0.962
0.954
0.952
0.957
1.040
1.047
1.035
0.000
1.034
0.962
1.032
1.029
0.960
0.973
0.975
0.967
0.966
0.979
0.981
1.022
0.915
0.853
0.879
0.864
0.954
0.959
0.911
0.287
0.856
0.851
0.884
0.944
0.770
0.953
0.886
0.864
0.857
0.866
0.951
0.938
0.951
0.000
0.951
0.870
0.947
0.947
0.854
0.891
0.898
0.860
0.847
0.896
0.902
0.933
1.341
1.049
1.040
1.046
1.164
1.140
1.338
34.920
1.053
1.057
1.043
1.180
1.096
1.145
1.045
1.054
1.058
1.058
1.137
1.169
1.125
NA
1.125
1.063
1.125
1.119
1.080
1.063
1.059
1.088
1.100
1.069
1.066
1.119
Appendices
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
GAIN
RS16854783
RS866596
RS7512173
RS6668845
RS821589
RS2793093
RS884158
RS1934909
RS11585959
RS7523846
RS821598
RS11580219
RS12029109
RS2793092
RS4575071
RS9432024
RS2793098
RS821592
RS6671423
RS11588799
RS12090070
RS1998406
RS4459095
RS12044355
RS11122324
RS6677638
RS10864698
RS16854779
RS16856202
RS6695678
RS16855598
RS9651122
RS16855881
RS2793092
A
G
T
G
T
A
G
T
C
G
T
C
G
G
A
C
G
A
C
A
A
G
A
C
A
T
A
C
G
A
0
0
C
G
G
A
C
A
C
G
A
C
T
A
C
T
A
A
G
T
A
G
T
G
G
A
G
A
G
C
G
T
T
G
T
G
T
A
0.348
0.284
0.104
0.104
0.082
0.224
0.012
0.154
0.315
0.104
0.298
0.398
0.054
0.226
0.363
0.342
0.224
0.288
0.103
0.399
0.127
0.244
0.127
0.347
0.349
0.127
0.105
0.343
0.020
0.000
0.000
0.000
0.000
0.250
0.353
0.281
0.101
0.101
0.080
0.227
0.013
0.157
0.318
0.102
0.295
0.395
0.053
0.228
0.365
0.340
0.226
0.286
0.102
0.400
0.127
0.244
0.127
0.346
0.349
0.127
0.106
0.344
0.020
0.000
0.000
0.000
0.000
0.218
270
0.671
0.675
0.677
0.690
0.701
0.714
0.742
0.746
0.752
0.760
0.775
0.779
0.799
0.806
0.819
0.840
0.841
0.846
0.879
0.919
0.965
0.965
0.965
0.966
0.972
0.979
0.981
0.983
0.992
NA
NA
NA
NA
0.008
0.981
1.020
1.029
1.028
1.030
0.982
0.939
0.982
0.986
1.021
1.013
1.012
1.024
0.988
0.990
1.009
0.990
1.009
1.011
0.996
0.997
1.002
0.997
1.002
0.999
1.002
0.998
0.999
0.998
NA
NA
NA
NA
1.195
0.899
0.931
0.899
0.898
0.886
0.890
0.645
0.876
0.903
0.892
0.926
0.931
0.854
0.895
0.909
0.925
0.897
0.921
0.882
0.916
0.882
0.910
0.881
0.919
0.916
0.885
0.873
0.916
0.739
NA
NA
NA
NA
1.047
1.071
1.117
1.179
1.177
1.198
1.083
1.367
1.099
1.077
1.169
1.109
1.101
1.228
1.090
1.078
1.100
1.092
1.105
1.158
1.083
1.128
1.104
1.128
1.092
1.088
1.133
1.141
1.090
1.349
NA
NA
NA
NA
1.363
Appendices
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
RS2793098
RS12076286
RS2793093
RS12091717
RS1073179
RS1015101
RS11122331
RS1009587
RS4658883
RS11122325
RS11585959
RS4366301
RS17748239
RS12044355
RS1407599
RS11122330
RS823163
RS980394
RS2812385
RS1538979
RS11122322
RS12027635
RS12143549
RS12406771
RS11122386
RS9432023
RS9432024
RS17817356
RS11122318
RS11583715
RS2812388
RS16855854
RS12060283
RS1538974
C
G
A
C
C
C
T
A
G
A
C
C
A
C
A
C
G
A
G
A
G
A
A
A
T
G
G
T
G
C
C
T
G
A
T
A
G
G
T
T
A
G
A
G
T
G
G
A
C
T
A
G
T
G
T
G
G
G
C
A
A
C
A
T
T
C
C
C
0.247
0.207
0.244
0.194
0.372
0.411
0.125
0.414
0.205
0.215
0.323
0.416
0.321
0.350
0.357
0.127
0.352
0.346
0.364
0.134
0.352
0.205
0.258
0.043
0.044
0.362
0.329
0.401
0.342
0.323
0.226
0.045
0.046
0.323
0.220
0.183
0.218
0.172
0.345
0.385
0.143
0.388
0.185
0.195
0.300
0.392
0.299
0.328
0.334
0.143
0.330
0.325
0.342
0.150
0.331
0.224
0.277
0.053
0.053
0.383
0.350
0.422
0.322
0.303
0.209
0.054
0.055
0.304
271
0.022
0.028
0.028
0.038
0.042
0.053
0.057
0.065
0.070
0.072
0.078
0.086
0.087
0.089
0.089
0.096
0.098
0.104
0.105
0.106
0.109
0.110
0.113
0.114
0.115
0.123
0.125
0.130
0.131
0.141
0.141
0.144
0.145
0.148
1.164
1.168
1.158
1.163
1.126
1.117
0.854
1.112
1.137
1.134
1.113
1.103
1.109
1.106
1.106
0.871
1.103
1.102
1.100
0.878
1.100
0.896
0.904
0.811
0.812
0.914
0.913
0.917
1.094
1.093
1.106
0.826
0.827
1.091
1.022
1.017
1.016
1.008
1.004
0.998
0.726
0.993
0.990
0.989
0.988
0.986
0.985
0.985
0.985
0.741
0.982
0.980
0.980
0.749
0.979
0.783
0.798
0.625
0.627
0.816
0.813
0.820
0.974
0.971
0.967
0.640
0.640
0.969
1.326
1.343
1.321
1.341
1.263
1.251
1.005
1.244
1.306
1.300
1.253
1.234
1.250
1.243
1.242
1.025
1.239
1.239
1.235
1.028
1.235
1.025
1.024
1.052
1.052
1.025
1.026
1.026
1.230
1.231
1.263
1.068
1.068
1.229
Appendices
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
RS11122381
RS2793091
RS11122324
RS16854779
RS821592
RS4658972
RS12029109
RS1417584
RS9431708
RS16856152
RS9431714
RS16854783
RS6674099
RS12130935
RS9308481
RS2487453
RS16854914
RS1535530
RS2738888
RS11580219
RS11590192
RS821600
RS4266866
RS16856275
RS16856199
RS821589
RS1322783
RS1407600
RS872624
RS11588595
RS16854957
RS821585
RS4658933
RS973607
C
A
T
C
A
C
C
C
C
A
A
A
G
A
T
T
G
C
C
G
G
A
G
C
A
T
T
C
T
G
A
T
G
A
T
G
C
T
G
T
T
T
T
G
G
G
A
G
C
C
A
T
T
A
A
G
C
T
T
C
C
T
C
A
C
C
A
G
0.205
0.286
0.355
0.349
0.272
0.045
0.052
0.441
0.466
0.045
0.365
0.349
0.427
0.093
0.269
0.355
0.010
0.321
0.147
0.387
0.468
0.351
0.186
0.037
0.027
0.087
0.146
0.138
0.153
0.041
0.147
0.020
0.193
0.121
0.221
0.268
0.336
0.330
0.290
0.053
0.061
0.422
0.485
0.053
0.383
0.331
0.444
0.083
0.255
0.341
0.013
0.334
0.157
0.399
0.482
0.364
0.195
0.033
0.023
0.094
0.154
0.145
0.145
0.045
0.155
0.017
0.185
0.128
272
0.152
0.155
0.156
0.157
0.160
0.161
0.166
0.170
0.178
0.184
0.196
0.197
0.209
0.210
0.250
0.274
0.280
0.321
0.332
0.347
0.351
0.352
0.383
0.397
0.405
0.415
0.418
0.426
0.431
0.433
0.437
0.441
0.443
0.463
0.906
1.093
1.087
1.088
0.916
0.832
0.844
1.081
0.927
0.841
0.928
1.079
0.931
1.132
1.078
1.067
0.749
0.942
0.927
0.947
0.949
0.947
0.939
1.138
1.163
0.923
0.938
0.938
1.064
0.897
0.941
1.176
1.057
0.939
0.792
0.967
0.968
0.968
0.810
0.644
0.663
0.967
0.830
0.651
0.828
0.961
0.833
0.932
0.949
0.950
0.443
0.838
0.795
0.846
0.850
0.844
0.816
0.843
0.815
0.762
0.804
0.800
0.911
0.682
0.806
0.778
0.918
0.795
1.037
1.236
1.221
1.222
1.035
1.076
1.073
1.209
1.035
1.086
1.040
1.213
1.041
1.374
1.224
1.198
1.267
1.060
1.081
1.060
1.060
1.062
1.081
1.537
1.658
1.119
1.095
1.099
1.243
1.178
1.098
1.776
1.216
1.110
Appendices
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
RS1040944
RS2812391
RS1322785
RS11122373
RS12137417
RS11122374
RS821627
RS12023889
RS11588799
RS4325116
RS1411775
RS2082552
RS821645
RS821597
RS4658966
RS16854723
RS12123082
RS843979
RS12730369
RS11122348
RS6677638
RS10864698
RS16855254
RS4658949
RS1934909
RS16855239
RS6678723
RS9431997
RS1998406
RS12090070
RS2759338
RS2738864
RS12087793
RS4459095
G
T
G
A
A
C
T
A
A
G
T
C
T
A
G
A
C
G
A
T
T
A
G
A
A
A
A
C
C
A
A
A
T
T
C
C
A
G
G
T
C
G
G
A
C
T
C
G
A
C
G
C
G
C
C
G
A
C
G
C
G
G
T
G
G
G
C
C
0.120
0.278
0.120
0.119
0.158
0.123
0.353
0.032
0.387
0.387
0.130
0.181
0.314
0.352
0.159
0.009
0.194
0.333
0.195
0.200
0.125
0.108
0.020
0.333
0.145
0.197
0.192
0.111
0.250
0.126
0.047
0.240
0.256
0.126
0.127
0.269
0.126
0.125
0.152
0.129
0.362
0.035
0.379
0.379
0.136
0.174
0.322
0.361
0.153
0.011
0.200
0.341
0.201
0.206
0.130
0.103
0.023
0.327
0.150
0.203
0.187
0.106
0.245
0.130
0.049
0.234
0.261
0.130
273
0.470
0.486
0.504
0.518
0.523
0.524
0.527
0.528
0.531
0.536
0.539
0.543
0.547
0.549
0.555
0.561
0.581
0.584
0.593
0.598
0.601
0.605
0.621
0.623
0.630
0.633
0.634
0.635
0.638
0.639
0.648
0.657
0.664
0.667
0.940
1.045
0.944
0.946
1.051
0.947
0.964
0.906
1.037
1.036
0.950
1.046
0.964
0.965
1.047
0.846
0.962
0.968
0.963
0.964
0.957
1.049
0.908
1.030
0.963
0.967
1.035
1.045
1.031
0.961
0.942
1.031
0.973
0.964
0.795
0.923
0.798
0.798
0.903
0.802
0.859
0.667
0.926
0.925
0.805
0.905
0.857
0.861
0.899
0.481
0.837
0.861
0.839
0.840
0.811
0.876
0.621
0.916
0.824
0.843
0.898
0.872
0.908
0.815
0.727
0.902
0.858
0.818
1.112
1.183
1.117
1.121
1.224
1.119
1.081
1.231
1.161
1.161
1.120
1.208
1.085
1.083
1.219
1.488
1.105
1.088
1.106
1.106
1.129
1.254
1.330
1.158
1.124
1.110
1.192
1.253
1.172
1.134
1.219
1.178
1.103
1.137
Appendices
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
RS2812393
RS2356606
RS4658958
RS4575071
RS12735632
RS16854954
RS16854785
RS872625
RS821596
RS16856202
RS17766890
RS7512173
RS866596
RS2356710
RS821598
RS821628
RS1094401
RS6668845
RS7511740
RS17819668
RS7416743
RS7533169
RS10158776
RS821577
RS9701655
RS967244
RS1754608
RS1341555
RS6687236
RS1341553
RS821662
RS9432013
RS1475514
RS6671423
G
C
C
T
T
C
G
C
A
G
T
A
C
T
A
A
C
C
A
A
A
T
A
C
C
C
C
A
A
C
G
G
C
G
C
T
T
C
G
T
A
T
G
T
C
G
T
C
G
G
A
T
T
G
G
C
C
A
T
T
T
G
G
G
A
A
T
A
0.430
0.221
0.200
0.352
0.202
0.285
0.026
0.223
0.302
0.029
0.061
0.104
0.289
0.216
0.304
0.288
0.236
0.104
0.104
0.363
0.027
0.191
0.029
0.444
0.202
0.180
0.290
0.441
0.218
0.418
0.341
0.055
0.419
0.103
0.436
0.225
0.204
0.347
0.206
0.288
0.028
0.226
0.299
0.031
0.062
0.102
0.287
0.218
0.301
0.286
0.238
0.103
0.103
0.360
0.028
0.189
0.028
0.446
0.200
0.178
0.289
0.443
0.219
0.420
0.343
0.054
0.421
0.103
274
0.682
0.707
0.714
0.725
0.728
0.780
0.805
0.811
0.816
0.817
0.818
0.836
0.838
0.839
0.841
0.850
0.865
0.869
0.869
0.870
0.879
0.880
0.886
0.893
0.894
0.909
0.911
0.912
0.921
0.922
0.922
0.931
0.933
0.940
0.977
0.975
0.975
1.021
0.976
0.983
0.958
0.984
1.014
0.963
0.973
1.019
1.013
0.986
1.012
1.012
0.989
1.015
1.015
1.010
0.974
1.011
1.025
0.992
1.009
1.008
1.007
0.994
0.993
0.994
0.994
1.011
0.995
1.007
0.873
0.854
0.850
0.910
0.851
0.870
0.682
0.862
0.900
0.697
0.774
0.851
0.897
0.863
0.898
0.896
0.868
0.847
0.847
0.900
0.694
0.878
0.735
0.888
0.880
0.873
0.892
0.889
0.869
0.889
0.885
0.793
0.890
0.840
1.093
1.113
1.118
1.146
1.119
1.110
1.346
1.123
1.144
1.330
1.225
1.222
1.144
1.127
1.141
1.143
1.127
1.217
1.217
1.132
1.366
1.163
1.428
1.109
1.158
1.165
1.137
1.110
1.135
1.112
1.117
1.289
1.113
1.207
Appendices
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
RS12757857
RS7418900
RS1754606
RS1754605
RS7523846
RS701158
RS2806465
RS16855093
RS9803690
RS821663
RS16855090
C
C
C
C
C
C
C
T
C
T
T
T
T
T
T
T
T
G
C
T
C
C
0.454
0.103
0.289
0.289
0.102
0.444
0.434
0.046
0.405
0.341
0.047
0.453
0.103
0.289
0.289
0.103
0.445
0.434
0.047
0.405
0.341
0.046
0.941
0.948
0.951
0.951
0.952
0.958
0.963
0.973
0.976
0.988
0.997
1.004
1.006
1.004
1.004
0.994
0.997
1.003
0.996
1.002
0.999
1.001
0.899
0.839
0.889
0.889
0.828
0.892
0.897
0.766
0.895
0.890
0.770
1.122
1.206
1.134
1.134
1.194
1.114
1.120
1.294
1.121
1.122
1.300
!
Table B.89: BC|SNPmax genotyping information for NFATC2 SNPs in the WTCCC and GAIN cohorts. SNPs of interest for this thesis are highlighted in blue. Where known the
RS numbers were listed.
COHORT
SNP
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
RS8119787
RS16996025
RS16996066
RS17199748
RS6021254
RS4811172
RS747062
RS6021191
RS4396773
RS6067758
RS6013202
RS6013172
RS1570161
RS761376
RS959996
RS880324
RS754285
Alleles
Minor
Major
G
A
T
C
T
C
T
C
C
A
G
A
C
T
T
A
T
C
T
C
A
G
A
G
A
C
T
A
A
G
A
G
T
C
Cases
0.486
0.041
0.042
0.000
0.000
0.088
0.166
0.001
0.389
0.090
0.043
0.000
0.002
0.153
0.357
0.218
0.160
MAF
Controls
0.506
0.034
0.049
0.000
0.000
0.096
0.157
0.000
0.398
0.096
0.047
0.000
0.001
0.158
0.363
0.212
0.164
275
P
OR
0.057
0.089
0.151
0.209
0.209
0.217
0.263
0.321
0.348
0.349
0.385
0.426
0.426
0.533
0.541
0.547
0.592
0.923
1.205
0.864
NA
NA
0.914
1.066
3.159
0.961
0.934
0.916
0.000
1.578
0.965
0.974
1.031
0.970
CI
L95
0.850
0.972
0.708
NA
NA
0.793
0.953
0.286
0.883
0.811
0.751
0.000
0.508
0.861
0.894
0.933
0.867
U95
1.003
1.493
1.055
NA
NA
1.054
1.193
34.850
1.045
1.077
1.117
NA
4.895
1.081
1.061
1.140
1.085
Appendices
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
WTCCC
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
RS6021185
RS3787187
RS6021247
RS6067804
RS4809846
RS3787195
RS6123041
RS6063655
RS6067803
RS6021276
RS3787186
RS2426311
RS6123045
RS761375
RS2273642
RS6067803
RS6067814
RS6067804
RS228839
RS3787193
RS3787187
RS6021247
RS8119787
RS910156
RS12481623
RS3787195
RS761376
RS6067774
RS4809846
RS6013195
RS747064
RS16996060
RS6013184
RS6067773
T
G
G
G
T
C
A
G
G
T
T
C
T
A
T
T
G
A
T
G
T
C
C
G
C
G
T
C
A
C
A
C
T
A
C
A
A
A
C
T
T
A
T
C
C
T
C
G
C
G
A
G
A
C
C
T
T
C
A
A
A
T
G
T
G
T
C
G
0.112
0.493
0.473
0.493
0.154
0.105
0.480
0.479
0.493
0.370
0.371
0.367
0.277
0.365
0.291
0.510
0.306
0.506
0.128
0.476
0.509
0.457
0.487
0.202
0.302
0.091
0.158
0.157
0.167
0.342
0.172
0.050
0.091
0.091
0.115
0.497
0.477
0.497
0.151
0.107
0.484
0.483
0.496
0.367
0.368
0.364
0.279
0.363
0.269
0.485
0.329
0.482
0.113
0.499
0.486
0.479
0.508
0.186
0.283
0.102
0.146
0.146
0.156
0.328
0.162
0.044
0.099
0.098
276
0.672
0.684
0.685
0.703
0.707
0.713
0.731
0.750
0.758
0.759
0.772
0.788
0.839
0.848
0.077
0.079
0.085
0.085
0.096
0.097
0.101
0.116
0.125
0.140
0.149
0.200
0.228
0.246
0.257
0.291
0.334
0.338
0.354
0.365
0.972
0.983
0.983
0.984
1.022
0.975
0.986
0.987
0.987
1.013
1.013
1.012
0.991
1.008
1.117
1.104
0.901
1.102
1.156
0.910
1.097
0.915
0.917
1.111
1.093
0.885
1.099
1.095
1.090
1.065
1.076
1.136
0.915
0.917
0.854
0.906
0.905
0.906
0.912
0.853
0.908
0.909
0.909
0.931
0.930
0.929
0.904
0.926
0.988
0.989
0.800
0.987
0.975
0.815
0.982
0.820
0.822
0.966
0.969
0.734
0.943
0.939
0.939
0.948
0.928
0.875
0.758
0.759
1.107
1.067
1.067
1.068
1.146
1.115
1.070
1.071
1.072
1.104
1.103
1.102
1.086
1.098
1.263
1.233
1.014
1.230
1.370
1.017
1.224
1.022
1.024
1.277
1.234
1.067
1.281
1.277
1.266
1.197
1.247
1.475
1.104
1.107
Appendices
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
GAIN
RS747062
RS6021233
RS16996047
RS228851
RS3787186
RS6021185
RS228832
RS6021276
RS6021197
RS6021190
RS228830
RS11696959
RS4396773
RS2426311
RS2426299
RS6021220
RS4809847
RS6126248
RS6013202
RS754285
RS11086340
RS6021232
RS4811189
RS16996025
RS2179746
RS3787205
RS6021183
RS6021264
RS6063655
RS880324
RS6123041
RS6067758
C
T
A
A
A
A
T
T
G
A
A
T
T
C
T
C
T
A
A
T
T
T
G
T
T
T
G
C
G
A
T
A
T
G
G
C
G
G
C
C
T
G
G
C
C
T
C
T
G
G
G
C
C
C
A
C
C
C
A
T
A
G
A
G
0.172
0.429
0.053
0.481
0.370
0.121
0.249
0.369
0.204
0.028
0.364
0.303
0.406
0.364
0.092
0.299
0.114
0.176
0.052
0.152
0.162
0.356
0.215
0.034
0.236
0.029
0.096
0.063
0.480
0.244
0.480
0.097
0.163
0.417
0.048
0.492
0.361
0.115
0.241
0.360
0.212
0.025
0.356
0.311
0.398
0.357
0.096
0.293
0.111
0.172
0.049
0.156
0.158
0.352
0.212
0.033
0.234
0.030
0.098
0.064
0.479
0.243
0.480
0.096
!
277
0.371
0.406
0.407
0.459
0.493
0.502
0.519
0.527
0.529
0.534
0.550
0.556
0.574
0.617
0.620
0.624
0.672
0.693
0.711
0.719
0.727
0.775
0.789
0.814
0.846
0.850
0.854
0.909
0.917
0.925
0.962
0.976
1.070
1.048
1.113
0.959
1.041
1.060
1.043
1.038
0.957
1.114
1.036
0.965
1.033
1.030
0.953
1.031
1.038
1.030
1.049
0.972
1.028
1.017
1.019
1.037
1.013
0.969
0.983
0.987
1.006
1.006
1.003
1.003
0.923
0.938
0.864
0.859
0.928
0.894
0.918
0.925
0.836
0.792
0.923
0.856
0.923
0.918
0.789
0.913
0.872
0.891
0.816
0.835
0.881
0.906
0.890
0.764
0.890
0.699
0.816
0.787
0.901
0.885
0.898
0.832
1.240
1.172
1.432
1.071
1.167
1.258
1.186
1.163
1.097
1.567
1.162
1.087
1.156
1.155
1.151
1.163
1.236
1.191
1.348
1.133
1.199
1.141
1.165
1.408
1.153
1.343
1.184
1.238
1.123
1.145
1.120
1.209
Appendices
!""#$%&'()*
+%("+,-(,.(!"#$(/$"/(
Black squares:r2 =1
White squares:r2 = 0
Figure C.85: Haploview LD plot (r2) generated from HapMap genotyping of all validated AKT1 SNPs. Outlined
areas indicated separate haplotype blocks (numbers 1-3).
278
Appendices
!""#$%&'(%)
*%("*+,(+-(!"#$%(.$".(
Black squares:r2 =1
(
Figure D.86: Haploview LD plot (r2) generated from HapMap genotyping of all validated DISC1 SNPs.
Outlined areas indicated separate haplotype blocks (numbers 17-21). The whole DISC1 gene was unable to be
presented here as it includes hundreds of SNPs, too many for Haploview to export as an image. Information on
the SNPs of interest in this region is available in section 3.1.1.3.
Black squares:r2 =1
(
Figure D.87: Haploview LD plot (r2) generated from HapMap genotyping of one block of DISC1 SNPs that was
found to include the majority of SNPs associated with RA in genome-wide association analysis. Information on
the SNPs of interest in this region is available in section 3.1.1.3.
279
Appendices
!
!
"##$%&'(!$)
*&!#*+,!+-!!"#$!.%#.!
Black squares:r2 =1
Figure E.88: Haploview LD plot (r2) generated from HapMap genotyping of all validated NFAT SNPs. Outlined
areas indicated separate haplotype blocks (numbers 1-11). Information on the SNPs of interest in this region
(NFATC1 – rs2002311 , NFATC2 – rs8119787) is available in section 3.1.1.2.
280

- Otago University Research Archive

Transcription

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