Slides 5.49 MB - UC Irvine International Imaging Genetics Conference

New approaches to
explaining missing
heritability in AD
Keoni Kauwe
Brigham Young University
Lambert et al, Nature Genetics 2013
Karch and Goate, Biol Psych 2015
Ridge et al, PLoS ONE 2013
Updated- all known
variants, 1KG imputation
Ridge et al. Under Review
Karch and Goate, Biol Psych 2015
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Phenotypic
heterogeneity
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Practical limits
on sample size
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Epistasis
If bigger isn’t working…
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Endophenotypes?
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imaging, cerebrospinal fluid, psychometrics
Other genetic features?
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Mitochondrial genome
Epigenetics
Creative study designs?
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deeper phenotyping
public data
mtDNA and imaging data
temporal pole thickness
whole brain volume
temporal pole thickness
hippocampal atrophy
Ridge PG, Koop A, Maxwell TJ, Bailey MH, Swerdlow RH, Kauwe JS, Honea RA, Alzheimer's Disease Neuroimaging I:
Mitochondrial haplotypes associated with biomarkers for Alzheimer's disease. PloS one 2013, 8(9):e74158.
mtDNA Genome Assembly,
Mapping, and Variant Detection
Map Reads
Realign Around InDels
Recalibrate Base Quality Scores
Joint Call Variants
Convert multi-sample VCF to Fasta
mtDNA next steps
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Full analysis of all variants for known AD and
other disease variants
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Full annotation of genomes and variants
(requires software development)
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Post annotated genomes for download
through the ADNI Genetics Core
Population-based analysis of AD risk
alleles: epistasis
• CD33 : MS4A4E
(p < .003, SF = 5.31)
• CLU : MS4A4E
(p < .02, SF = 3.81)
Ebbert et al. Biol. Psychiatry. (2013).
CD33-MS4A4E interaction fails to
replicate by meta-analysis
Study
ACT1
ADC2
ADNI
LOAD
TARC1
UMVUMSSM_a
UMVUMSSM_b
UMVUMSSM_c
Cache
SF p−val
0.9 0.43
1.76 0.33
7.99 0.08
0.66 0.21
1.27 0.44
1.11 0.44
0.59
0.4
0.41 0.32
5.31 0.003
Meta (no Cache) 0.94
Meta (w\ Cache) 1.4
N
1858
681
371
2965
388
1058
390
271
2419
0.81 7982
0.28 10401
0.3
2.0
4.0
Synergy Factor
Ebbert et al. Alz & Dem. (2015).
6.0 8.0
CLU-MS4A4E Meta-analysis supports
statistical epistasis
Study
ADC2
ADNI
LOAD
TARC1
UMVUMSSM_a
UMVUMSSM_b
Cache
SF p−val
N
1.07
0.47 681
2.81
0.18 371
1.9
0.07 2965
0.39
0.19 395
2.57
0.08 1067
3.16
0.18 390
3.81
0.02 2419
Meta (no Cache) 1.79 0.008 5869
Meta (w/ Cache) 2.23 4e−04 8288
0.3
1.0
2.0
Synergy Factor
Ebbert et al. Alz & Dem. (2015).
3.0 4.0 5.0
Finding the “right” genes
• Loss of protein function that protects from
disease constitutes a desirable and tractable
therapeutic target
• PCSK9 C697X and cholesterol
• First reported in 2005, confirmed in 2007
• Drugs have already been approved and are
in use
• AD resilient individuals in UPDB and Cache County
• Large pedigrees (with full medical records)
• Selection of AD risk pedigrees (excess AD mortality)
• Linkage (SNP array data) and Variant Analysis (WGS)
!3#
Posi,on#
4.478747#
9.791228#
14.210349#
18.261529#
21.988689#
25.532309#
28.998589#
32.175229#
35.798089#
39.915389#
43.673349#
47.248849#
50.989889#
54.622928#
58.766889#
62.201669#
66.777849#
69.915269#
73.380569#
76.891789#
80.389969#
84.651609#
88.887589#
93.313089#
97.037689#
101.632889#
105.153289#
108.687289#
112.252689#
116.023889#
119.715489#
122.966689#
126.554689#
130.217889#
134.154889#
138.360889#
142.681689#
145.973489#
149.757089#
154.763889#
157.969288#
161.565889#
166.578889#
170.601089#
LOD$
Chr$10$*$583803$lods$
4#
3#
2#
1#
0#
583803#
!1#
!2#
!3#
Posi+on#
4.936747#
11.276549#
17.249469#
24.485309#
31.149269#
38.455869#
44.784069#
49.500488#
54.497749#
59.605069#
64.878929#
70.612989#
75.781709#
80.424429#
85.503569#
90.589369#
96.748609#
102.215669#
107.133069#
113.624069#
118.530069#
124.059869#
128.947269#
134.863269#
140.152069#
146.803669#
151.703669#
156.450469#
161.957267#
167.730069#
172.966869#
178.933869#
184.356069#
189.495869#
194.544069#
200.680668#
206.128869#
211.056269#
216.558469#
223.616069#
228.653469#
233.856469#
239.214469#
245.124468#
250.746069#
257.774468#
Chr$2$&$546137$lods$
3#
2#
1#
0#
546137#
!1#
!2#
Variant Analysis
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Quality Scores, removal of SNPs in highly variable portions of
the genome
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Function, heterozygosity
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Experimentally observed to be associated with a phenotype
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Are within 2 hops upstream and that are known or predicted to
affect susceptibility to late-onset Alzheimer disease or genes
within 1 hop downstream of them
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Variants were excluded that are observed with an allele
frequency greater than or equal to 3.0% of the genomes in
public databases
Validation: Results
405 Cases, 801 Controls (TREM2)
SNP
Gene
p-value
OR
All Samples MAF
(controls/cases)
rs142787485
RAB10
0.01836
0.5853
0.04135 / 0.02759
rs7653
SAR1A
0.004947
0.3534
0.03 / 0.01
Validation: Results
544 Cases, 3605 Controls (Cache)
SNP
Gene
rs142787485 RAB10
rs7653
SAR1A
p-value
OR
0.028
0.69
All Samples
MAF
(cont/cases)
cases
0.045 / 0.031
0.26
0.87
0.025 / 0.021
Validation: Results
Gene-based (SKAT-O)
132 cases, 359 controls (ADNI)
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RAB10 (p=1E-04)
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SAR1A (P=1)
RAB10
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RAB10 is a member of the RAS oncogene family. It is involved in
regulation of membrane trafficking and movement of proteins from the
golgi apparatus to the membrane (Mitra, 2011; Hutagalung, 2011;
Bao, 1998).
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RAB10 is known to bind APP {Olah, 2011 #2748} and RNAi silencing of
RAB10 is known to decrease AB levels without affecting sAPPBeta
levels, possibly by altering gamma secretase cleavage or changing
secretion/degradation of AB (Udayar, 2013).
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Rs142787485 results in altered miRNA regulation via the miRNAs
MIR374A and MIR374B (Garcia, 2011; Liu, 2012).
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Together this information suggests that the rs142787485 variant may
result in a change in RAB10 expression via miRNA regulation.
Reduced expression could result in decreased abeta 42, a known
mechanism for reducing AD risk (Jonsson, 2012).
Increased expression of RAB10
in AD vs. control neurons
_at (RAB10) - CC
ease Status
RAB10 Expression (log10)
22980_at(
log222981_s_at (RAB10)- CC
AD
222981_s_at(
P=0.03
3.5
3.0
2.5
2.0
1.5
Control
Disease Status
AD
Modulation of RAB10 expression
does not alter full length APP
RAB10 expression alters Aβ
Conclusions
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RAB10 variants may impact AD risk
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Suggests RAB10 may be a useful therapeutic
target for AD
Knockdown of RAB10 results in favorable
AB42/AB40 ratio
AD DREAM Challenge
Training
Q1: N=767 (ADNI)
Q2: N=176 (ADNI)
Q3: N=628 (ADNI)
Leaderboard
model
development
Q1: N=588 (ROS/MAP)
Q2: N=129 (ROS/MAP)
Q3: N=94 (ANM)
Q1:100maxsubmissions
Q2/Q3:50maxsubmissions
▪AD#1 Challenge Scientific Advisory Board
▪Co-Chairs
▪Peter St. George Hyslop (Cambridge/Toronto)
▪Robert Green (Harvard)
▪Members
▪Alan Evans (McGill University)
▪Chris Gaiteri (Allen Institute for Brain Science)
▪David Bennett (Rush)
▪George Vradenburg (USAgainstAlzheimer's)
▪Gil Rabinovici (UCSF)
▪Gustavo Stolovitzky (IBM/DREAM)
▪Kaj Blennow (Goteborg University)
▪Keoni Kauwe (BYU)
▪Kristine Yaffe (UCSF)
▪Nolan Nichols (University of Washington)
▪Paul Thompson (UCLA)
▪Reisa Sperling (Harvard)
▪Scott Small (Columbia)
▪Ex Officio
▪Maria Carillo (Alzheimer's Association)
▪Mike Weiner (UCSF, ADNI PI)
▪Neil Buckholz (NIH-NIA)
Final Scoreboard
model
evaluation
Q1: N=587 (ROS/MAP)
Q2: N=128 (ROS/MAP)
Q3: N=88 (ANM)
Question 1: Predict the change
in MMSE scores 24 months after
initial assessment using clinical
data and SNP array data
Question 2: Predict the set of
cognitively normal individuals
whose biomarkers are
suggestive of amyloid
perturbation using clinical data
and SNP array data
Question 3: Classify individuals
into diagnostic groups and to
predict MMSE scores using
processed MRI data
AD DREAM Challenge
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No compelling scientific results
Lessons learned
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Proper harmonization/documentation
results in broad use of data (527)
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“truly open dataset”
Need engagement of more scientists
with domain expertise
Sample size or depth/quality of
phenotyping?
Summary
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Great progress in AD Genetics
Further progress will require:
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Innovative use of existing data
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Studies that address complexity in
genetic architecture
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Evaluation of other genetic features
Innovative use of samples with high
quality phenotyping (even with lower N)
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structural variants, epigenetics
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Excellent Undergraduates
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Extensive research experience
Training in Computer Science, Statistics,
Biology
Fantastic candidates for PhD programs
Acknowledgments
• Perry Ridge, Mark Ebbert, Kauwe Lab Undergraduates (BYU)
• Lisa Cannon-Albright (Univ. of Utah)
• Carlos Cruchaga, Celeste Karch (WUSM)
• Alison M. Goate (MSSM)
• The Cache County Study on Memory in Aging
• Mary Lou Fulton Supercomputing Center at BYU
• Alzheimer’s Disease Genetics Group
• the Alzheimer Disease Genetic Consortium (ADGC)
• Alzheimer’s Disease Neuroimaging Initiative (ADNI)
• the GERAD Consortium
http://www.assayprotocol.com/uploads/
APP%E5%89%AA
%E5%88%87.jpg
In silico inhibitor screen
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Two interaction pockets
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4.7 million small molecules
Small Molecule Inhibitors
APP
APOE e4
TREM2
PLD3
APOE e2
GWAS SNPs
Ridge et al and Kauwe, PLoS ONE 2013
Follow-up Discussion
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Additional genetic data- gene based tests,
allele specific expression, etc.
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Gene based test using coding variants only?
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Drug redirection?
Methods for evaluating the individual variant
impact