Genetic Determinants of Major Blood Lipids in Pakistanis Compared

Transcription

Genetic Determinants of Major Blood Lipids in Pakistanis
Compared With Europeans
Downloaded from http://circgenetics.ahajournals.org/ by guest on November 19, 2016
Danish Saleheen, MBBS, MPhil; Nicole Soranzo, BSc, PhD; Asif Rasheed, MBBS;
Hubert Scharnagl, PhD; Rhian Gwilliam, PhD; Myriam Alexander, MSc, MPhil; Michael Inouye, PhD;
Moazzam Zaidi, MBBS; Simon Potter, PhD; Philip Haycock, MSc, MPhil; Suzanna Bumpstead, BSc;
Stephen Kaptoge, PhD; Emanuele Di Angelantonio, MD, MSc, PhD;
Nadeem Sarwar, MRPharmS, PhD; Sarah E. Hunt, PhD; Nasir Sheikh, MSc; Nabi Shah, B-Pharmacy;
Maria Samuel, BSc, MSc; Shajjia Razi Haider, MSc; Muhammed Murtaza, MBBS;
Alexander Thompson, PhD; Reeta Gobin, MBBS, MPhil; Adam Butterworth, PhD, MSc;
Usman Ahmad, MBBS; Abdul Hakeem, MBBS; Khan Shah Zaman, MBBS, MRCP, FRCP, MRCS;
Assadullah Kundi, MBBS, FCPS; Zia Yaqoob, MBBS, FACC; Liaquat Ali Cheema, MBBS, PhD;
Nadeem Qamar, MBBS, FACC; Azhar Faruqui, FACC, FRCP, FCPS, FAHA;
Nadeem Hayat Mallick, MBBS, MRCP; Muhammad Azhar, MBBS, MRCP; Abdus Samad, MD, FACC;
Muhammad Ishaq, MBBS, MRCP, FRCP, FACC; Syed Zahed Rasheed, MD, FESC, FRCP;
Rashid Jooma, MBBS; Jawaid Hassan Niazi, MBBS, FCPS; Ali Raza Gardezi, MBBS, MRCP;
Nazir Ahmed Memon, MBBS, FRCP, FACC, FACVS; Abdul Ghaffar, MBBS, FCPS;
Fazal-ur Rehman, MBBS; Michael Marcus Hoffmann, PhD; Wilfried Renner, PhD; Marcus E. Kleber, PhD;
Tanja B. Grammer, MD; Jonathon Stephens, BSc; Anthony Attwood; Kerstin Koch, PhD;
Mustafa Hussain, MBBS; Kishore Kumar, MBBS; Asim Saleem, MBBS; Kishwar Kumar, MBBS;
Muhammad Salman Daood, MBBS; Aftab Alam Gul, MBBS; Shahid Abbas, MBBS; Junaid Zafar, MBBS;
Faisal Shahid, MBBS; Shahzad Majeed Bhatti, MBBS; Syed Saadat Ali, MBBS;
Muhammad Fahim, MBBS; Gurdeep Sagoo, BSc, MSc, PhD; Sarah Bray, MA, PhD;
Ralph McGinnis, PhD; Frank Dudbridge, PhD; Bernhard R. Winkelmann, PhD; Bernhard Böehm, MD, PhD;
Simon Thompson, DSc; Willem Ouwehand, MD, PhD, FRCPath; Winfried März, MD;
Philippe Frossard, PhD, DSc; John Danesh, DPhil, FRCP, FFPH; Panos Deloukas, PhD
Background—Evidence is sparse about the genetic determinants of major lipids in Pakistanis.
Methods and Results—Variants (n⫽45 000) across 2000 genes were assessed in 3200 Pakistanis and compared with 2450
Germans using the same gene array and similar lipid assays. We also did a meta-analysis of selected lipid-related
variants in Europeans. Pakistani genetic architecture was distinct from that of several ethnic groups represented in
international reference samples. Forty-one variants at 14 loci were significantly associated with levels of HDL-C,
Received September 3, 2009; accepted May 5, 2010.
From the Center for Non-Communicable Diseases (D.S., A.F., M.Z., N.Shah, M.S., S.R.H., M.M., U.A., K.Kumar, A.H., M.H., A.S., Kishore Kumar,
Kishwar Kumar, M.S.D., A.A.G., S.S., J.Z., F.S., S.M.B., S.S.A., S.M.B., S.S.Ali, M.F., P.F.) Karachi, Pakistan; Department of Public Health and Primary
Care (D.S., M.A., P.H., S.K., E.D.A., N.Sarwar, N.Sheikh, A.T., R.G., A.B., J.D.), University of Cambridge, United Kingdom; Wellcome Trust Sanger
Institute (N.S., R.G., M.I., S.P., S.B., P.D., S.E.H., R.G.), Hinxton, Cambridge, United Kingdom; Department of Twin Research and Genetic
Epidemiology (N.S., W.O.), King’s College London, St Thomas’ Hospital Campus, London, United Kingdom; Clinical Institute of Medical and Chemical
Laboratory Diagnostics (H.S., W.R.), Medical University Graz, Graz, Austria; National Institute of Cardiovascular Diseases (K.S.Z., A.K., Z.Y., L.A.C.,
N.Q., A.F.), Karachi, Pakistan; Punjab Institute of Cardiology (N.H., M.A.), Lahore, Pakistan; Karachi Institute of Heart Diseases (A.S., M.I., S.Z.R.),
Karachi, Pakistan; Jinnah Postgraduate Medical Centre (R.J., J.H.N.), Karachi, Pakistan; Multan Institute of Cardiology (A.R.G.), Multan, Pakistan; Civil
Hospital (N.A.M., A.G.), Hyderabad, Pakistan; Red Crescent Institute of Cardiology (F.u.R.), Hyderabad, Pakistan; Division of Clinical Chemistry
(M.M.H.), Department of Medicine, Albert Ludwig University, Freiburg Germany; LURIC Nonprofit LLC (M.E.K.), Freiburg, Germany; Synlab Center
of Laboratory Diagnostics Heidelberg (T.B.G., W.M.), Heidelberg, Germany; Department of Haematology (J.S., A.A., W.O., K.Koch), University of
Cambridge and NHS Blood and Transplant, Cambridge, United Kingdom; PHG Foundation (G.S.), Strangeways Research Laboratories, United Kingdom;
MRC Biostatistics Unit (S.B., F.D., S.T.), Cambridge, United Kingdom; Division of Endocrinology and Diabetes and Institute of Public Health (B.R.W.,
W.M.), Social Medicine and Epidemiology, Medical Faculty Mannheim, University of Heidelberg, Germany, Graduate School Molecular Endocrinology
and Diabetes, University of Ulm, Ulm Germany; and Cardiology Group Frankfurt (B.B.), Frankfurt, Germany.
Drs Saleheen, Soranzo, Danesh, and Deloukas contributed equally to this work.
The online-only Data Supplement is available at http://circgenetics.ahajournals.org/cgi/content/full/CIRCGENETICS.109.906180/DC1.
Correspondence to Danish Saleheen, MBBS, Center for Non-Communicable Diseases (CNCD), Karachi, Pakistan, and Department of Public Health
and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK. E-mail [email protected]
© 2010 American Heart Association, Inc.
Circ Cardiovasc Genet is available at http://circgenetics.ahajournals.org
348
DOI: 10.1161/CIRCGENETICS.109.906180
Saleheen et al
Genetic Loci for Major Lipids in Pakistan
349
triglyceride, or LDL-C. The most significant lipid-related variants identified among Pakistanis corresponded to genes
previously shown to be relevant to Europeans, such as CETP associated with HDL-C levels (rs711752; P⬍10⫺13),
APOA5/ZNF259 (rs651821; P⬍10⫺13) and GCKR (rs1260326; P⬍10⫺13) with triglyceride levels; and CELSR2 variants
with LDL-C levels (rs646776; P⬍10⫺9). For Pakistanis, these 41 variants explained 6.2%, 7.1%, and 0.9% of the
variation in HDL-C, triglyceride, and LDL-C, respectively. Compared with Europeans, the allele frequency of rs662799
in APOA5 among Pakistanis was higher and its impact on triglyceride concentration was greater (P-value for difference
⬍10⫺4).
Conclusions—Several lipid-related genetic variants are common to Pakistanis and Europeans, though they explain only a
modest proportion of population variation in lipid concentration. Allelic frequencies and effect sizes of lipid-related
variants can differ between Pakistanis and Europeans. (Circ Cardiovasc Genet. 2010;3:348-357.)
Key Words: lipids 䡲 HDL-C 䡲 LDL-C 䡲 triglyceride 䡲 Pakistan 䡲 gene 䡲 population structure 䡲 GWAS
䡲 IBC-array 䡲 meta-analysis
L
evels of major blood lipids—that is, concentrations of
low- and high-density lipoprotein cholesterol (LDL-C and
HDL-C) and triglyceride—are each strongly, log-linearly, and
positively (or, in the case of HDL-C, inversely) associated with
the risk of coronary heart disease (CHD).1,2 Linkage and twin
based studies suggest that more than 50% of the variation in
these circulating lipids is determined by genetic factors.3–5
Several genetic variants have been established in the regulation of lipid metabolism in people of European continental
ancestry, including 40 genomic loci (represented by 152
SNPs) identified in genome-wide association studies.5–16 In
contrast with considerable evidence available on people of
European ancestry, data on genetic regulation of major blood
lipids in Pakistanis are limited. For example, the previous
largest relevant study reported on 5 genetic markers in
relation to a few hundred participants.17
Clinical Perspective on p 357
We report the first large-scale study of the genetic determinants of LDL-C, HDL-C and triglyceride concentrations in
people living in Pakistan, a country of ⬎180 million people
with a high burden of cardiovascular disease. We have
assayed over 45 000 single nucleotide polymorphisms (SNPs)
across 2000 candidate genes using the ITMAT-Broad-CARe
(IBC) array18 in 3200 participants from the Pakistan Risk of
Myocardial Infarction Study (PROMIS).19 We compared
association signals observed in PROMIS with those in 2450
participants of German ancestry from the Ludwigshafen Risk
and Cardiovascular Health (LURIC) prospective study, which
used the same gene array.20 To place the German findings in
the context of data from other populations of European
ancestry, we did a meta-analysis of published studies.
Materials and Methods
Participants
This report follows the reporting recommendations of STREGA.21
PROMIS is a case-control study of acute myocardial infarction (MI)
in 6 centers in urban Pakistan.20 MI cases had symptoms within 24
hours of hospital presentation, typical ECG changes, and a positive
troponin-I test. Control subjects were individuals without a history of
cardiovascular disease. They were frequency-matched to cases by
sex and age (in 5-year bands) and concurrently identified in the same
hospitals as index cases because they were either (1) visitors of
patients attending the outpatient department, (2) patients attending
the outpatient department for routine noncardiac complaints, or (3)
non– blood-related visitors of index MI cases. People with recent
illnesses or infections were not eligible. Information was recorded on
personal and parental ethnicity, spoken language, dietary intake,
lifestyle factors, and other characteristics. Nonfasting blood samples
(with the time since last meal recorded) were drawn from each
participant and centrifuged within 45 minutes of venepuncture.
Serum samples were stored at ⫺80°C. Total cholesterol, HDL-C,
and triglyceride concentrations were measured using enzymatic
methods (Roche Diagnostics, USA) at the Center for NonCommunicable Diseases, Pakistan. LDL-C concentration was calculated using the Friedewald formula.22
LURIC is a prospective study of cardiovascular death in individuals of German ancestry resident in southwest Germany who
underwent elective coronary angiography and left ventriculography
between June 1997 and January 2000.21 CHD in the current analyses
was defined by troponin-confirmed MI (ie, acute ST- or non–
ST elevation MI or based on past medical records) or presence of
visible luminal narrowing of ⱖ50% in at least 1 coronary vessel.
Individuals with ⱖ20% but ⬍50% stenosis were excluded from the
analyses. Individuals with stenosis ⬍20% were regarded as control
subjects. Fasting blood samples collected before angiography were
kept frozen at ⫺80°C between the day of blood draw and the day of
analysis for total cholesterol, HDL-C and triglycerides (all
determined enzymatically).
The studies were approved by relevant ethics committees, and
participants gave informed consent.
Genotyping
All genotyping was performed at the Wellcome Trust Sanger
Institute using the “IBC” array of about 2000 candidate genes.18
Variants on the array were selected on the basis of (1) genes with
known associations for various cardiovascular, pulmonary, and sleep
related disorders, (2) information from pathway-based tools for the
identification of biologically plausible candidate genes, (3) unpublished functional experiments in mice, (4) findings from various
genome-wide scans, and (5) priority SNPs identified by IBC consortium investigators.18 SNPs (n⫽45 237) in version 1 of this array
were genotyped in the PROMIS participants and were called using
the Illuminus algorithm.23 Markers were excluded from analysis if
the call rate was ⬍95% (372 SNPs); there was evidence of departure
from Hardy-Weinberg Equilibrium at a probability value of ⬍10⫺3
(1750 SNPs); or the minor allele frequency (MAF) was ⬍1%
(11 931 SNPs, with most such omissions due to genetic markers
relevant in Africans being uninformative in Pakistanis and Europeans). LURIC participants were typed with version 2 of the IBC array
and underwent the same calling and quality control procedures.
Because version 2 has 4050 additional SNPs, these SNPs were
excluded from the current analysis. After quality control, 31 883
SNPs in 3197 Pakistanis and 35 533 SNPs in 2452 Germans were
available for analyses.
Statistical Methods
To compare the genetic structure of Pakistanis with that of several
major ethnic groups, we received permission from HapMap3 inves-
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Circ Cardiovasc Genet
August 2010
Table. Characteristics of the Participants From PROMIS and
LURIC Studies
Characteristics
PROMIS
(n⫽3195)
Age, y
LURIC
(n⫽2452)
53.2 (10)
62 (10)
Women, %
17.5
29.5
Self-reported history of diabetes
mellitus, %
17.2
32.4
Family history of MI, %
15.4%
10%
2
Body mass index, kg/m
25.2 (4.3)
Total cholesterol, mmol/L
27.4 (4.0)
4.6 (1.3)
5.0 (1.0)
Low density lipoprotein cholesterol,
mmol/L
2.70 (1.20)
2.96 (0.85)
High density lipoprotein cholesterol,
mmol/L
0.82 (0.24)
0.99 (0.27)
0.56 (0.22–0.95)
0.49 (0.21–0.81)
Triglycerides, mmol/L
Data are mean (SD), median (IQR), or %.
tigators to conduct principal components analyses on 1124 participants in HapMap3. We selected 19 931 SNPs in common with the
PROMIS sample, and excluded 11 952 A/T and C/G SNPs to avoid
possible strand alignment bias because it is difficult to infer the
minor allele for A/T or C/G SNPs for non-HapMap populations.8 To
investigate genetic substructure, we classified Pakistani participants
into 8 self-identified ethnic and linguistic groups and calculated
principal components on the matrix of identity-by-state sharing of all
pairs of individuals. Quantile-quantile plots were produced by
plotting the observed ⫺log10 probability value for each lipid against
the expected ⫺log10 probability value. The association between each
lipid measure and genetic variants was tested using linear regression.
Additive models calculated the change in lipid level per copy of the
minor allele. 〉-coefficients have been reported using the common
allele as the reference allele in PROMIS. All analyses were done
using models adjusting for age and sex, the first 2 principal
components and case-control status. Effect estimates in LURIC were
reported for the same allele taken as reference in PROMIS.
The Bonferroni correction for the 32 000 SNPs for 3 traits is 10⫺7,
assuming 96 000 independent tests with no prior information. We
chose a cutoff 10⫺6 owing to the likely higher prior odds of
association because the array involves candidate genes and because
there is a high degree of correlation between the tested SNPs. To
reduce potential biases, lipid analyses were stratified by case-control
status and excluded participants on lipid-lowering medication at the
time of baseline examination. Analyses used PLINK 1.06, R version
2.9.1, and STATA 10.0.
Meta-Analysis
We sought genetic association studies of lipid-related variants in
people of European ancestry published between January 1970 and
January 2009. We focused on SNPs (ie, rs1800775, rs708272,
rs646776, and rs662799) identified as top signals in the Pakistan
study to enable comparison of their impact in Europeans (with the
exception of rs780093, for which there was minimal previous data,
owing to its completely recent discovery). Electronic searches
involved MEDLINE, EMBASE, BIOSIS, and Science Citation index
and combined search terms related to genes (eg, cholesteryl ester
transfer protein [CETP]) and lipids (eg, HDL-C) without language
restriction. These searches were supplemented by scanning reference
lists, hand-searching relevant journals, and correspondence with
authors. Two investigators independently extracted the following
information: mean and SD of lipid levels by genotype; proportion of
males; fasting status; and assay methods. Analyses involved only
within-study comparisons. Mean levels of lipids (and differences in
mean levels in comparison with the common homozygotes) were
calculated using both fixed and random-effects models (as the latter
Figure 1. A, Scatterplot of the first 2 principal components identified by principal component analysis of the identity-by-state matrix.
The colors of points refer to the self-reported ethnicities in PROMIS control participants and HAPMAP (these ethnicities were not used
in the PCA). B, Scatterplot of the first 2 principal components and self-reported ethnicities in PROMIS control participants. A and B,
PAK indicates Pakistani from the PROMIS control subjects; YRI, Yoruba in Ibadan, Nigeria; LWK, Luhya in Webuye, Kenya; ASW, African ancestry in Southwest United States; MKK, Maasai in Kinyawa, Kenya; GIH, Gujrati Indians in Houston, Tex; CEU, Utah residents
with Northern and Western European ancestry from the CEPH collection; TSI, Toscani in Italy; MEX, Mexican ancestry in Los Angeles,
Calif; JPT, Japanese in Tokyo, Japan; CHD, Chinese in Metropolitan Denver, Colo; CHB Han Chinese in Beijing, China; C1, first principal component; C2, second principal component; and PCA, principal components analysis.
Saleheen et al
351
Figure 2. Q-Q plots for PROMIS and LURIC in association with major lipids. Lambda: Genomic inflation factor.
makes allowances for between-study heterogeneity). Probability
values for difference between the effect estimates obtained in
PROMIS and European participants were calculated through a ␹2 test
of heterogeneity.
Results
The main characteristics of the Pakistani and German participants in this study are summarized in the Table. Comparison
with HapMap3 population panels shows that the Pakistani
population clustered distinctly from 11 other major ethnic
groups, indicated by the separate clustering on the scatterplot
of principal components (Figure 1). Pakistanis appear genetically closest to, but still clearly distinct from, Gujarati
Indians living in the United States, a group that is known to
differ genetically from Indians living in India.24 Analysis of
the 8 ethnic and linguistic groups in the Pakistani study
suggested the possibility of relatively minor population substructure; the different ethnicities could not be demarcated
discretely on the scatter plots involving different principal
components (Figure 1 and the online-only Data Supplement
Figure 1). Compared with Germans, the Pakistani participants
were about a decade younger and had broadly similar LDL-C
and triglyceride values but lower HDL-C (Table).
Variants With Highly Significant Associations
Under an additive model, linear regression analysis for each lipid
measure identified several SNPs deviating from the expected ␹2
values as shown by the quantile-quantile plots in Figure 2. A
total of 25 variants in 4 genomic regions were associated with
lipid levels in Pakistanis (Pⱕ10⫺6), including 16 variants for
HDL-C, 8 variants for triglycerides, and 1 variant for LDL-C.
All 16 HDL-C–related variants were on the cholesteryl ester
transfer protein (CETP) gene (10⫺14⬍P⬍10⫺6; Figure 3A and
online-only Data Supplement Table 1). Each copy of the minor
allele of rs711752, the lead SNP, was associated with
0.048 mmol/L (95% CI, 0.04 to 0.06; P⬍10⫺14) higher HDL-C
levels. MAFs and effect sizes of the CETP variants in Pakistanis
were broadly similar to those observed in this German population (Figure 3A), with overlapping genetic association signals
and a similar pattern of linkage disequilibrium (LD) in this
region (Figure 4). Subsidiary analyses in PROMIS cases and
control participants for these variants revealed qualitatively
similar results (online-only Data Supplement Figures 2a to 2c).
To further explore LD patterns in Europeans, subsidiary analyses were conducted in CEU HapMap2 data, which revealed a
similar pattern of LD in the CEU HapMap2 population and
LURIC participants (data available on request). As shown in
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August 2010
Figure 3. Association with A, HDL-C and B,
log-triglyceride in PROMIS and LURIC participants of SNPs signficantly associated in
PROMIS (P⬍10⫺6). Estimates represent the
per-minor allele increase in lipid levels,
adjusted for age, sex, the first 2 principal
components, and case-control status. The
probability value for difference between
studies corresponds to a test of nullity of
interaction term between study and the
SNP of interest. Size of boxes are proportional to the inverse of the variance of study
estimates. Chr indicates chromosome; SNP,
single nucleotide polymorphism; and MAF,
minor allele frequency.
Figure 5, meta-analyses of the 2 most extensively studied CETP
variants in Europeans yielded overall increases in HDL-C
concentration of 0.063 mmol/L (0.055 to 0.071; I2⫽67%, 55%
to 77%) per copy of the A allele of the Taq1B variant (rs708272;
46 studies, 65 640 participants) and 0.071 mmol/L (0.066 to
0.075; I2⫽10%, 0% to 43%) per copy of the A allele of the
C-629A variant (rs1800775; 26 studies, 80 184 participants).
Associations of the Taq1B variant appeared of similar size in the
2 studies; the Taq1B variant was in strong LD with rs711752
(r2⫽0.99), the lead variant in the Pakistani population. By
Saleheen et al
353
Figure 4. Genomic location of all the
genotyped variants in CETP and APOA5
and a comparison of linkage disequilibrium in PROMIS and LURIC participants.
A, PROMIS (blue) and LURIC (red); B,
LD plot (D⬘) LURIC; C, LD plot (D⬘) PROMIS. LD plots have been drawn using
1595 PROMIS control and 1175 LURIC
control participants. Similar analyses for
CELSR2 gene in PROMIS and LURIC
were not possible because the current
gene array used contains only few SNPs
in this gene.
contrast, the association of the C-629A variant with HDL-C
appeared somewhat stronger in Europeans than in Pakistanis (␹2
test for difference, P⫽2⫻10⫺4; Figure 5 and online-only Data
Supplement Figure 3a-1b).
Eight variants in 2 genomic regions were highly significantly
associated with log triglyceride concentration in the Pakistani
participants. The most significant SNP (rs662799; P⫽1.25⫻
10⫺14) localized to the APOA5 gene (Figure 3B and online-only
Data Supplement Table 1). Each copy of the rs662799-C allele
at this locus was associated with a 0.14 mmol/L higher log
triglyceride concentration (Figures 3B and 4), with MAF about
2 times higher in the Pakistani than German participants (0.17
versus 0.07). This variant was in strong LD with several other
variants in APOA5 and nearby ZNF259 that were also significantly associated with triglyceride concentration but apparently
not in LD with any of the variants in APOA1, APOC3 or
APOA4. Overall, APOA5 variants appeared to have stronger LD
and associations with triglyceride concentration in Pakistani than
in German participants (Figure 4). Meta-analysis of rs662799 in
available European studies yielded 0.20 mmol/L (0.14 to 0.26)
higher triglyceride per each copy of the minor allele (18 studies,
20 963 participants: Figure 5 and online-only Data Supplement
Figure 3D), an effect size that was lower than that observed in
the Pakistani participants (␹2 for difference, P⫽7⫻10⫺4; Figure
5). Three variants in the glucokinase regulatory protein (GCKR)
gene highly significantly associated with triglyceride in Pakistanis (P⬍10⫺6) had broadly similar-sized effects in Germans
(Figure 3B).
Only rs646776 in the cadherin, EGF LAG 7-pass G-type
receptor 2 (CELSR2) gene was highly significantly associated
with LDL-C concentration in the Pakistani participants
(P⫽1.25⫻10⫺10) and was associated with a 0.16 mmol/L
(⫺0.23 to ⫺0.08) lower LDL-C concentration per copy of the
minor allele. This variant was not significantly associated
with LDL-C concentration in the German participants
(n⫽1175), perhaps owing to limited statistical power. Analyses conducted earlier in a larger LURIC study population
(n⫽3189) for the same locus yielded a similar association
with LDL-C levels to that observed in Pakistanis.25 The
current meta-analysis of rs646776, however, established this
variant’s relevance more reliably in Europeans, yielding an
overall 0.15 mmol/L (⫺0.17 to ⫺0.14) lower LDL-C per
each copy of the minor allele (14 studies, 48 445 participants;
Figure 5), an effect size comparable to that observed in
Pakistanis (␹2 test for difference, P⫽0.84; Figure 5 and
online-only Data Supplement Figure 3c).
No significant interactions were observed on an additive
scale of the 25 top variants with lipid measures by ghee or
tobacco consumption or by sex (online-only Data Supplement
Figure 4). Qualitatively similar results were observed in
analyses adjusted for time since onset of MI symptoms in 875
cases in PROMIS with relevant information (available on
request).
Variants With Nominally Significant Associations
Of the 152 lipid-related SNPs discovered through previous
genome-wide association studies in European populations, 49
were covered by the gene array used in the current study (23
for HDL-C, 17 for LDL-C, and 17 for triglycerides with a few
SNPs associated with 2 or all 3 traits). At a prespecified
nominal value of P⬍0.01, 12 of the 23 established HDL-C–
related variants were associated with HDL-C concentration
(including 7 variants described earlier in CETP and 5 other
variants in LIPG, LIPC, and DPEP2); 10 of the established
17 triglyceride-related variants were associated with triglyceride concentration (including 3 variants described earlier in
APOA5 and GCKR and 7 other variants in DOCK7, TBL2,
LPL, BAZ1b, and APOB); and 5 of the 17 established
LDL-C-related variants were associated with LDL-C concen-
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August 2010
Figure 5. Comparison of associations with lipid traits observed in PROMIS with previously published studies in participants of European descent. Estimates represent the per-minor allele increase in lipid levels. PROMIS estimates are derived fitting a regression,
adjusting for age, sex, case-control status, and the first 2 components of PCA. Estimates in whites are derived from a random-effects
meta-analysis of additive estimates. Individual plots for each meta-analysis are presented in online Figures 2a through 2d. The probability value of heterogeneity derives from a heterogeneity test between the overall estimates in whites and the estimate in PROMIS. Size
of boxes are proportional to the inverse of the variance of study estimates. The mean difference is in mmol/L. Scales differ between
lipids.
tration (including 1 variant in CELSR2 described above and 4
other variants in FADS1, FADS2 and CELSR2: online-only
Data Supplement Figure 5). Hence, we identified a total of 41
different variants significantly related to major lipid levels in
Pakistanis (ie, 25 variants at P⬍10⫺6 and a further 16
variants at P⬍10⫺2). Analyses of these genes in PROMIS
and LURIC participants revealed a similar pattern of LD,
with somewhat stronger LD blocks in APOB and LPL genes
in Pakistanis than in Europeans (online-only Data Supplement Figure 6). Collectively, in the Pakistani participants,
these variants explained 6.2%, 7.1%, and 0.9% of the variation in HDL-C, triglyceride, and LDL-C, respectively,
whereas corresponding analyses in the German participants
explained 5.9%, 7.2%, and 0.71% of the variation in these
lipids, respectively.
Subsidiary analyses yielded odds ratio for MI in Pakistanis with each of the 41 principal SNPs that were
compatible with the direction of associations of each of
these variants with lipid concentration, although the current study was underpowered for reliable gene-MI analyses
(online-only Data Supplement Figure 7).
Discussion
The current study has identified a total of 41 variants at 14 loci
that were significantly associated with levels of HDL-C, triglyceride or LDL-C in Pakistanis. The most highly significant
lipid-related variants identified among Pakistanis corresponded
to genes previously shown to be relevant to lipid metabolism in
Europeans, such as CETP, APOA5, and CELSR2. Even collectively, however, the top variants explained only 6.2%, 7.1%, and
0.9% of the population variation in HDL-C, triglyceride, and
LDL-C levels in Pakistanis, respectively (a similar proportion of
lipid variation was explained by the top signals in our parallel
analysis of Germans). The current study has also suggested some
differences in allelic frequencies and magnitude of association
with lipids for variants in APOA5 in Pakistanis compared with
Saleheen et al
Europeans. As discussed below, however, further studies are
needed to confirm whether such differences are mainly related to
ethnicity rather than other characteristics.
Most of the highly significant lipid-related loci identified
in Pakistani participants were related to HDL-C and triglyceride rather than LDL-C concentration, a finding that is
consistent with a lower yield of genetic loci associated with
LDL-C in previous GWA studies in Europeans.5–16 For
HDL-C, our most highly significant findings related to the
CETP gene.26 HDL is believed to exert atheroprotective
effects through several mechanisms, including transfer of
cholesterol from peripheral tissues to liver.26,27 CETP facilitates this process by exchanging cholesterol esters from HDL
with triglyceride in apolipoprotein B– containing particles.26
Deficiency of this protein leads to higher HDL-C levels and
other lipoprotein abnormalities.25,26 Our meta-analysis focused on the Taq1B and C-629A variants in CETP, which
alter CETP mass and activity and, consequently, increase
HDL-C concentration.27
For triglyceride, our most highly significant findings related to
variants in APOA5, which is part of the APOA1/C3/A4/A5 gene
cluster localized to chromosome 11q23.28,29 It has been proposed
that APOAV regulates lipoprotein lipase-mediated hydrolysis of
triglycerides contained in VLDL particles.28 Further triglyceride-related variants were found in GCKR,30 which regulates
activity of glucokinase, a key enzyme responsible for the first
rate-limiting step in the glycolysis pathway, deficiency of which
alters glucose and lipoprotein metabolism.31 For LDL-C, the
sole highly significant finding related to a variant in CELSR2,32
a gene that expresses itself along with PSRC1 and SORT1 within
a transcriptional network proposed to regulate metabolic profile
and atherosclerosis,32,33 although precise mechanisms remain
unknown.
Compared with the German participants we studied, the
frequency of the rs662799-C allele in the APOA5 locus was
higher in Pakistanis and appeared to have a greater impact on
triglyceride concentration. However, as at least part of these
differences could have been due to nonethnic factors (eg,
differences in sample size and/or population sampling frameworks used), further study is needed. Evidence of ethnic-related
differences is emerging from other contexts, such as suggestions
that total cholesterol is a stronger risk factor among South Asians
than Europeans34 and that the LTA4H haplotype has higher odds
ratios for myocardial infarction in Africans than Europeans.35
The value of large ethnic-specific studies has also been illustrated by the discovery of the strongest common susceptibility
locus (KCNQ1) yet for T2D,36 –38 identified in East Asians but
not initially in Europeans because the allele frequency in East
Asians is much higher (40% versus 5%) despite similar odds
ratios in both populations.36 –38
For reasons of feasibility, we used existing genetic tools based
on catalogues of genetic variation mostly discovered in Europeans, East Asians, and West Africans, even though we were
aware that these tools may not adequately capture genetic
variation in Pakistanis (or other South Asians).39,40 For example,
the recent discovery of a 7-fold relative risk for heart failure with
the 25-bp deletion allele in the MYBPC3 gene would have
remained undetected using conventional platforms because this
variant is present only in South Asians.41 Further study in
355
Pakistanis is therefore needed involving better populationspecific tools for genetic mapping. Larger replication studies
should also help to quantify and control any overestimation in
hypothesis-generating estimates. Such studies should aim to
involve fine-mapping of relevant loci (eg, APOA5) and functional studies.42 Future studies may also yield stronger (or novel)
genetic signals by direct assay of LDL-C rather than, as in the
current study, calculation of LDL-C using the Friedewald
formula. However, as a large prospective study has shown that
associations of major lipids with CHD risk are at least as
extreme in nonfasted participants as in fasted participants,41 use
of nonfasting samples in the current study seems unlikely to
have influenced materially the findings here.
Sources of Funding
Epidemiological field work in PROMIS was supported by unrestricted grants to investigators at the University of Cambridge and in
Pakistan. Genotyping for this study was funded by the Wellcome
Trust and the EU Framework 6 –funded Bloodomics Integrated
Project (LSHM-CT-2004-503485). The British Heart Foundation has
supported some biochemical assays. The Yousef Jameel Foundation
supports Dr Saleheen. The cardiovascular disease epidemiology
group of Dr Danesh is underpinned by programme grants from the
British Heart Foundation and the UK Medical Research Council.
Disclosures
Dr Saleheen received research funding from the Fogarty International Center, National Heart, Lung and Blood Institute, National
Institute of Neurological Disorders and Stroke, and the Wellcome
Trust. Dr Danesh reports having received research funding from the
British Heart Foundation, BUPA Foundation, diaDexus, European
Union, Evelyn Trust, Fogarty International Center, GlaxoSmithKline,
Medical Research Council, Merck, National Heart, Lung and Blood
Institute, National Institute of Neurological Disorders and Stroke,
Novartis, Pfizer, Roche, UK Biobank, and the Wellcome Trust.
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CLINICAL PERSPECTIVE
Levels of the major blood lipids, LDL-C, HDL-C, and triglyceride are each strongly associated with the risk of coronary
heart disease (CHD). Several genetic variants have been established in the regulation of lipid metabolism in people of
European continental ancestry; however there are few data available on the genetic determinants of these lipid traits in
South Asians a population with a high burden of cardiometabolic conditions. We investigated 45 000 variants across 2000
genes in 3200 Pakistanis, and 2450 Germans using the same gene array. A total of 41 variants at 14 loci, were found to
be significantly associated with major lipid traits in Pakistanis, explaining 6.2%, 7.1%, and 0.9% of the variation in HDL-C,
triglyceride, and LDL-C, respectively. The most significant lipid-related variants identified among Pakistanis corresponded
to genes previously shown to be relevant to Europeans, such as CETP associated with HDL-C levels; APOA5/ZNF259 and
GCKR with triglyceride levels; and CELSR2 variants with LDL-C levels. However, differing allelic frequencies and lipid
effects for variants in APOA5 were observed in Pakistanis compared with Europeans. This study suggests that several
lipid-related genetic variants are common to Pakistanis and Europeans, though they explain only a modest portion of
population variation in lipid concentration. Allelic frequencies and the effect sizes of lipid-related variants can differ
between Pakistanis and Europeans.
Genetic Determinants of Major Blood Lipids in Pakistanis Compared With Europeans
Danish Saleheen, Nicole Soranzo, Asif Rasheed, Hubert Scharnagl, Rhian Gwilliam, Myriam
Alexander, Michael Inouye, Moazzam Zaidi, Simon Potter, Philip Haycock, Suzanna
Bumpstead, Stephen Kaptoge, Emanuele Di Angelantonio, Nadeem Sarwar, Sarah E. Hunt,
Nasir Sheikh, Nabi Shah, Maria Samuel, Shajjia Razi Haider, Muhammed Murtaza, Alexander
Thompson, Reeta Gobin, Adam Butterworth, Usman Ahmad, Abdul Hakeem, Khan Shah
Zaman, Assadullah Kundi, Zia Yaqoob, Liaquat Ali Cheema, Nadeem Qamar, Azhar Faruqui,
Nadeem Hayat Mallick, Muhammad Azhar, Abdus Samad, Muhammad Ishaq, Syed Zahed
Rasheed, Rashid Jooma, Jawaid Hassan Niazi, Ali Raza Gardezi, Nazir Ahmed Memon, Abdul
Ghaffar, Fazal-ur Rehman, Michael Marcus Hoffmann, Wilfried Renner, Marcus E. Kleber,
Tanja B. Grammer, Jonathon Stephens, Anthony Attwood, Kerstin Koch, Mustafa Hussain,
Kishore Kumar, Asim Saleem, Kishwar Kumar, Muhammad Salman Daood, Aftab Alam Gul,
Shahid Abbas, Junaid Zafar, Faisal Shahid, Shahzad Majeed Bhatti, Syed Saadat Ali, Fahim
Muhammad, Gurdeep Sagoo, Sarah Bray, Ralph McGinnis, Frank Dudbridge, Bernhard R.
Winkelmann, Bernhard Böehm, Simon Thompson, Willem Ouwehand, Winfried März, Philippe
Frossard, John Danesh and Panos Deloukas
Circ Cardiovasc Genet. 2010;3:348-357; originally published online June 22, 2010;
doi: 10.1161/CIRCGENETICS.109.906180
Circulation: Cardiovascular Genetics is published by the American Heart Association, 7272 Greenville Avenue,
Dallas, TX 75231
Copyright © 2010 American Heart Association, Inc. All rights reserved.
Print ISSN: 1942-325X. Online ISSN: 1942-3268
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requested is located, click Request Permissions in the middle column of the Web page under Services. Further
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"SUPPLEMENTAL MATERIAL."
1
Supplemental Figure 1: Scatter plot of additional principal components and self reported ethnicities in PROMIS control participants
2 Supplemental Table 1: Association of major lipid traits in PROMIS and comparison with the LURIC participants of SNPs significantly
associated in PROMIS (P < 10-6)
Chr snp bp Association with HDL‐C levels (mmol/l) 16 rs711752 55553712 16 rs708272 55553789 16 rs17231506 55552029 16 rs3764261 55550825 16 rs11508026 55556829 16 rs1532625 55562802 16 rs1800775 55552737 16 rs1532624 55562980 rs1864163 55554734 16 16 rs7499892 55564091 16 rs11076175 55563879 16 rs5880 55572592 16 rs12720922 55558386 16 rs9939224 55560233 16 rs12708967 55550712 16 rs11076176 55564947 PROMIS LURIC gene a1 N maf beta se p N maf beta se p P‐value for difference between studies CETP CETP CETP CETP CETP CETP CETP CETP CETP CETP CETP CETP CETP CETP CETP CETP T A A A A T G T A A G G T A G C 3023 3023 3023 3023 3021 2996 3024 3023 3024 3023 3022 3024 3024 3021 3021 3021 0.47 0.47 0.33 0.33 0.46 0.48 0.40 0.48 0.22 0.22 0.19 0.08 0.20 0.22 0.22 0.21 0.048 0.048 0.049 0.049 0.046 0.045 ‐0.045 0.044 ‐0.043 ‐0.042 ‐0.044 ‐0.064 ‐0.042 ‐0.039 ‐0.039 ‐0.038 0.006 0.006 0.007 0.007 0.006 0.006 0.006 0.006 0.008 0.008 0.008 0.012 0.008 0.008 0.008 0.008 4.67E‐14 4.77E‐14 5.54E‐13 1.17E‐12 1.47E‐12 2.27E‐12 3.00E‐12 4.06E‐12 1.56E‐08 3.82E‐08 5.04E‐08 7.86E‐08 1.29E‐07 2.74E‐07 3.85E‐07 6.71E‐07 2451 2450 2451 2451 2452 2428 2451 2448 2443 2452 2451 2451 2451 2452 2452 2450 0.42 0.42 0.32 0.32 0.42 0.43 0.53 0.43 0.27 0.18 0.18 0.05 0.18 0.21 0.20 0.18 0.058 0.058 0.059 0.058 0.058 0.058 ‐0.062 0.057 ‐0.063 ‐0.063 ‐0.063 ‐0.068 ‐0.060 ‐0.062 ‐0.036 ‐0.063 0.007 0.007 0.008 0.008 0.007 0.007 0.007 0.007 0.009 0.010 0.010 0.017 0.010 0.009 0.009 0.010 5.05E‐15 4.77E‐15 7.50E‐14 1.08E‐13 3.40E‐15 8.28E‐15 3.69E‐17 8.69E‐15 2.40E‐13 8.52E‐11 1.27E‐10 5.25E‐05 1.02E‐09 1.49E‐11 1.16E‐04 9.67E‐11 0.45 0.76 1 0.63 0.55 0.63 0.11 0.25 0.16 0.11 0.36 0.69 0.48 0.25 0.26 0.04 Association with LDL‐C levels (mmol/l) 109620053 CELSR2 1 rs646776++ Association with log‐triglyceride levels (mmol/l) 11 rs662799 116168917 APOA5 11 rs651821 116167789 APOA5 11 rs2072560 116167036 APOA5 11 rs2266788 116165896 APOA5 11 rs2075290 116158506 ZNF259/APOA5 27584444 GCKR 2 rs1260326++ 2 rs780093 27596107 GCKR 2 rs780094 27594741 GCKR G 5576 0.25 ‐0.158 0.025 7.19E‐10 1175 0.239 ‐0.014 0.040 0.7224 0.05 C C A G G T T T 3195 3195 3195 3195 3189 5500 3194 3185 0.17 0.17 0.16 0.20 0.19 0.26 0.26 0.26 0.142 0.142 0.142 0.129 0.132 0.078 0.075 0.074 0.018 0.018 0.019 0.017 0.018 0.012 0.016 0.016 1.25E‐14 1.47E‐14 2.13E‐14 6.94E‐14 8.77E‐14 1.09E‐10 2.35E‐06 2.63E‐06 2452 2452 2450 2452 2452 2451 2445 2449 0.07 0.07 0.07 0.07 0.07 0.44 0.44 0.44 0.080 0.083 0.077 0.073 0.077 0.078 0.080 0.080 0.026 0.026 0.026 0.025 0.025 0.014 0.014 0.014 2.22E‐03 1.49E‐03 3.34E‐03 3.83E‐03 2.15E‐03 7.19E‐09 3.41E‐09 3.86E‐09 0.07 0.15 0.11 0.17 0.19 0.86 0.64 0.69 ++Genotyping was done in further 2555 PROMIS individuals for variants associated with lipid traits at a P < 10-5
Chr: chromosome, a1: minor allele, N: number of individuals, maf: minor allele frequency, beta: per-minor allele increase in lipid levels, adjusted for age, sex, the first two principal components and
case-control status. For LDL, the LURIC dataset was restricted to participants not on lipid lowering drugs.
The P-value for difference between studies corresponds to a test of nullity of interaction term between study and the SNP of interest.
3 Supplemental Figure 2(a): Association with HDL-C in PROMIS cases and controls for SNPs significantly associated
with HDL-C levels in all PROMIS participants (P < 10-6)
SNP_id/
Chr.
rs11076175
16
rs11076176
16
rs11508026
16
rs12708967
16
rs12720922
16
rs1532624
16
rs1532625
16
rs17231506
16
rs1800775
16
rs1864163
16
rs3764261
16
rs5880
16
rs708272
16
rs711752
16
rs7499892
16
rs9939224
16
Status
Mean difference (95% CI)
P-value het.
case
control
-0.05 (-0.07, -0.02)
-0.08 (-0.11, -0.05)
.128
case
control
-0.04 (-0.06, -0.01)
-0.06 (-0.08, -0.03)
.288
case
control
0.05 (0.03, 0.07)
0.06 (0.04, 0.09)
.554
case
control
-0.06 (-0.08, -0.03)
-0.06 (-0.09, -0.03)
.869
case
control
-0.05 (-0.07, -0.02)
-0.08 (-0.11, -0.05)
.118
case
control
0.05 (0.03, 0.07)
0.06 (0.04, 0.09)
.491
case
control
0.05 (0.03, 0.08)
0.07 (0.04, 0.09)
.502
case
control
0.06 (0.04, 0.09)
0.07 (0.05, 0.10)
.609
case
control
-0.04 (-0.06, -0.02)
-0.07 (-0.10, -0.05)
.049
case
control
-0.05 (-0.07, -0.02)
-0.07 (-0.10, -0.04)
.196
case
control
0.06 (0.04, 0.09)
0.07 (0.05, 0.10)
.570
.57
case
control
-0.08 (-0.12, -0.04)
-0.07 (-0.11, -0.02)
.609
case
control
0.05 (0.03, 0.08)
0.07 (0.05, 0.09)
.327
case
control
0.06 (0.03, 0.08)
0.07 (0.05, 0.09)
.362
case
control
-0.05 (-0.07, -0.02)
-0.07 (-0.10, -0.04)
.327
case
control
-0.04 (-0.07, -0.02)
-0.07 (-0.10, -0.05)
.119
-.1
-.05
0
mmol/l
.05
.1
4
Supplemental Figure 2(b): Association with log-triglyceride in PROMIS cases and controls for SNPs
significantly associated with triglyceride levels in all PROMIS participants (P < 10-6)
SNP_id/
chr
rs1260326
2
rs2072560
11
rs2075290
11
rs2266788
11
rs651821
11
rs662799
11
rs780093
2
rs780094
2
Status
Mean Difference
(95% CI)
P-value het.
case
control
0.08 (0.04, 0.12)
0.08 (0.03, 0.12)
.908
case
control
0.15 (0.10, 0.20)
0.13 (0.08, 0.18)
.608
case
control
0.14 (0.09, 0.19)
0.12 (0.07, 0.17)
.561
case
control
0.13 (0.08, 0.18)
0.13 (0.08, 0.18)
.972
case
control
0.15 (0.10, 0.20)
0.13 (0.08, 0.19)
.732
case
control
0.15 (0.10, 0.20)
0.13 (0.08, 0.19)
.734
case
control
0.07 (0.03, 0.11)
0.08 (0.03, 0.12)
.909
case
control
0.07 (0.03, 0.11)
0.08 (0.03, 0.12)
.838
0
.05
.1
.15
.2
log mmol/l
5
Supplemental Figure 2(c): Association with LDL-C in PROMIS cases and controls for SNPs significantly
associated with LDL-C levels in all PROMIS participants
SNP_id/
chr
Status
Mean Difference
(95% CI)
rs646776
1
case
-0.05 (-0.09, -0.02) .584
control
-0.07 (-0.11, -0.03)
-.1
-.05
0
P_value het.
.05
mmol/l
Supplemental Figures 2 (a-c): Estimates represent the per-minor allele increase in lipid levels, adjusted for age, sex,
the first two principal components. P_value het. Is the P-value for heterogeneity for effect estimates obtained in cases
and controls. Chr: chromosome.
6
Supplemental Figure 3(a): Meta-analysis of previously published studies in Europeans for the association of
rs1800775 (C-629A) variant, located in the CETP gene, with HDL-C levels Suppl1-20
Author (Name of Study)
Year
Number of
participants
Rotterdam study
2007
Aulchenko (ENGAGE consortium)
2009
Barzilai N (Longevity)
% Weight
ES (95% CI)
(D+L)
1435
0.08 (0.06, 0.11)
2.94
5840
0.06 (0.05, 0.08)
9.33
2003
743
0.07 (0.03, 0.11)
1.17
Bauerfeind
2002
185
0.07 (-0.01, 0.15)
0.32
Bernstein MS
2003
1720
0.05 (0.02, 0.09)
1.50
Blankenberg S (AtheroGene)
2004
574
0.08 (0.03, 0.13)
0.84
Chasman (WGHS)
2008
6195
0.09 (0.08, 0.10)
8.28
Dachet C (ECTIM)
1999
668
0.08 (0.04, 0.12)
1.11
Dullaart (PREVEND)
2007
8141
0.06 (0.05, 0.08)
9.87
Eiriksdottir Reykjavik)
2001
745
0.08 (0.05, 0.11)
2.38
Freeman DJ (WOSCOPS)
2003
1107
0.06 (0.04, 0.08)
4.20
1187
0.07 (0.04, 0.10)
2.24
Girelli (Verona Heart Project)
Heidema (CDRFMP)
2007
1071
0.07 (0.04, 0.10)
2.14
Horne (IHCS)
2007
1309
0.05 (-0.00, 0.11)
0.59
Kakko (OPERA)
2001
481
0.05 (0.01, 0.09)
1.17
Kathiresan (FIINRISK97)
2008
7940
0.06 (0.05, 0.08)
7.79
Kathiresan (MDC-CC)
2008
5519
0.08 (0.07, 0.09)
7.79
Kathiresan (DGI)
2008
2758
0.07 (0.05, 0.09)
4.43
Kathiresan (NORDIL)
2008
5095
0.08 (0.05, 0.10)
3.88
McCaskie (CUDAS/BPHS/CUPID)
2007
1059
0.07 (0.04, 0.11)
1.73
Sabatti (NFBC1966)
2009
4531
0.07 (0.05, 0.09)
3.79
1519
0.08 (0.05, 0.11)
2.32
Schouw (PROSPECT/EPIC)
Thompson JF
2007
2087
0.05 (0.03, 0.07)
5.44
Thompson JF
2003
93
0.08 (-0.00, 0.15)
0.32
Tobin MD
2004
182
0.06 (-0.01, 0.13)
0.38
Ridker (WGHS)
2009
18000
0.08 (0.07, 0.09)
14.06
Overall random effect I-squared = 10% (95% CI 0% - 43%), p = 0.318)
0.07 (0.07, 0.08)
100.00
Overall fixed effect
0.07 (0.07, 0.08)
NOTE: Weights are from random effects analysis
-.153
0
.153
Effect Size
7
Supplemental Figure 3(b): Meta-analysis of previously published studies in Europeans for the association of
rs708272 (Taq1B) variant, located in the CETP gene, with HDL-C levels Suppl3, 5, 8, 21-59
Author (Name of Study)
Year
Arca
Barzilai N (Longevity)
Bauerfeind
Blankenberg S (Atherogene)
Carr
Corella D
Cuchel (NORM & CATH)
Deguchi (SVTR)
Dullaart (PREVEND)
Eiriksdottir (Reykjavik)
Freeman DJ
Freeman DJ (WOSCOPS)
Fumeron F (ECTIM)
Girelli (Verona Heart Project)
Gudson (EARS)
Hall
Hannuksela
Heidema (CDRFMP)
Horne (IHCS)
Juvonen T
Kauma H
Keavney
Klos K (CARDIA)
Kondon I
Kuivenhoven JA (The Monitoring Project)
Liu (PHS)
McCaskie (CUDAS/BPHS/CUPID)
Miltiadous
Mitchell
Nettleton (ARIC)
Noone E
Ordovas (Framingham)
Pai J (HPFS)
Pai J (NHS)
Plat
Riemens
Sandhofer (Salzburg Atherosclerosis Prevention)
Schouw (PROSPECT-EPIC)
Sorli
Talmud (NPHS)
Tenkanen H
Thompson JF
Thompson JF
Vohl MC
Weitgasser (SAPHIR)
Ridker (WGHS)
2001
2003
2002
2004
2002
2000
2002
2004
2007
2001
1994
2003
1995
1998
2006
1994
2007
2007
1995
1996
2004
2007
1989
1997
2002
2007
2004
1994
2006
2000
2007
2004
2004
2002
1999
2008
2006
2002
1991
2007
2003
1999
2004
2009
Number of
Participants
ES (95% CI)
180
373
184
571
120
514
224
49
8289
745
220
1105
724
296
767
116
82
1075
1298
91
524
4665
1586
146
238
384
1058
95
112
8764
63
2916
513
480
112
32
1503
1399
549
1727
109
2105
93
182
1017
18245
Overall Random Effect I-squared = 67.0% (95% CI 55% - 76%), p = <0.001)
I-V Overall
% Weight
(D+L)
0.10 (0.01, 0.18)
0.12 (0.05, 0.18)
0.06 (-0.02, 0.14)
0.08 (0.03, 0.13)
0.10 (0.01, 0.19)
0.11 (0.07, 0.14)
0.06 (-0.02, 0.13)
0.02 (-0.10, 0.15)
0.06 (0.05, 0.08)
0.07 (0.03, 0.10)
0.10 (0.03, 0.17)
0.05 (0.02, 0.07)
0.08 (0.04, 0.12)
0.02 (-0.03, 0.07)
0.07 (0.05, 0.09)
0.08 (-0.02, 0.18)
0.10 (-0.00, 0.20)
0.07 (0.04, 0.10)
0.05 (-0.01, 0.11)
0.20 (0.03, 0.37)
0.06 (0.02, 0.10)
0.06 (0.04, 0.07)
0.06 (0.04, 0.08)
0.11 (0.02, 0.20)
0.12 (0.05, 0.18)
0.05 (0.01, 0.09)
0.07 (0.03, 0.10)
0.03 (-0.05, 0.11)
0.12 (0.02, 0.21)
0.07 (0.06, 0.09)
0.02 (-0.12, 0.15)
0.06 (0.04, 0.08)
-0.11 (-0.15, -0.07)
0.11 (0.06, 0.17)
0.06 (-0.03, 0.15)
-0.00 (-0.11, 0.11)
0.06 (0.03, 0.09)
0.06 (0.03, 0.09)
0.05 (0.01, 0.09)
0.05 (0.04, 0.07)
0.04 (-0.07, 0.16)
0.05 (0.03, 0.07)
0.07 (-0.02, 0.17)
0.06 (0.02, 0.09)
0.09 (0.05, 0.12)
0.07 (0.07, 0.08)
0.76
1.21
0.86
1.82
0.69
3.04
0.98
0.40
5.00
2.85
1.03
3.98
2.35
1.67
3.98
0.55
0.54
3.11
1.38
0.20
2.22
4.93
3.96
0.65
1.26
2.14
2.75
0.84
0.66
4.96
0.33
4.34
2.29
1.39
0.66
0.47
3.24
3.05
2.53
4.51
0.46
4.41
0.59
2.68
2.72
5.58
0.06 (0.05, 0.07)
0.07 (0.07, 0.07)
100.00
-.374
0
Effect Size
.374
8
Supplemental Figure 3(c): Meta-analysis of previously published studies in Europeans for the association of
rs646776 variant, located in the CELSR2 gene, with LDL-C levels Suppl 60-64
Author (name of study)
Year No. of participants
ES (95% CI)
%
Weight
Aulchenko (Meta-analysis 15 studies) 2009
12685
-0.14 (-0.17, -0.11)
24.53
Kathiresan (DGI)
2008
2758
-0.19 (-0.25, -0.13)
5.74
Kathiresan (FINRISK 97)
2008
7940
-0.14 (-0.18, -0.10)
15.29
Kathiresan (MDC-CC)
2008
5519
-0.15 (-0.19, -0.11)
13.80
Kathiresan (NHS98 China)
2008
2891
-0.20 (-0.30, -0.10)
2.21
Kathiresan (NHS98 India)
2008
587
-0.19 (-0.31, -0.07)
1.53
Kathiresan (NHS98 Malaysia)
2008
781
-0.29 (-0.49, -0.09)
0.55
Sabatti (NFBC1966)
2009
4507
-0.16 (-0.20, -0.11)
8.83
Sandhu (1958 British Birth Cohort )
2008
1330
-0.13 (-0.21, -0.05)
3.45
Sandhu (Ely study)
2008
1686
-0.15 (-0.21, -0.09)
6.13
Sandhu (EPIC-Norfolk Obese)
2008
993
-0.18 (-0.28, -0.08)
2.21
Sandhu (EPIC-Norfolk Replication)
2008
3293
-0.18 (-0.24, -0.12)
6.13
Sandhu (EPIC-Norfolk subcohort)
2008
2014
-0.17 (-0.23, -0.11)
6.13
Wallace (Twins UK)
2008
1461
-0.08 (-0.16, -0.00)
3.45
-0.15 (-0.17, -0.14)
100.00
Overall random effect
I2 =
0 (95% CI 0%-55%), p = 0.695
-0.15 (-0.17, -0.14)
-.3
-.2
-.1
0
Effect Size
9
Supplemental Figure 3(d): Meta-analysis of previously published studies in Europeans for the association
of variant rs662799 (T1131C), located in the ApoA5 gene, with triglyceride levels Suppl 65-80
Author (name of study)
Year
ES (95% CI)
No. of participants
%
Weight
Klos (CARDIA)
2005
3415
0.08 (0.03, 0.14)
10.80
Grallert (KORA & SAPHIR)
2007
3264
0.11 (0.01, 0.21)
9.05
Talmud (NPHSII)
2002
2537
0.18 (0.06, 0.31)
7.98
Vaessen (EPIC-Norfolk)
2006
1800
0.27 (0.15, 0.39)
8.18
Lai (Framingham Offspring)
2004
1725
0.42 (0.25, 0.58)
6.23
Hubacek (Female)
2004
1368
0.14 (0.03, 0.25)
8.40
Hubacek (Male)
2004
1191
0.27 (0.05, 0.49)
4.55
Evans
2003
1094
0.94 (0.39, 1.50)
1.06
Martinelli (Verona Heart Project) 2007
913
0.19 (0.04, 0.34)
6.88
Lee
2004
438
0.48 (0.04, 0.93)
1.56
Szalai
2004
310
0.18 (0.01, 0.35)
6.25
Vaverkova
2004
267
0.33 (-0.22, 0.88)
1.07
Aouizerat
2003
198
0.25 (0.05, 0.44)
5.33
Lee (Japanese American Family) 2004
154
0.30 (0.04, 0.57)
3.62
Farall (PROCARDIS)
2006
2956
0.23 (0.12, 0.34)
8.40
Helgadottir (PennCATH)
2007
476
0.06 (0.00, 0.12)
10.64
0.20 (0.14, 0.26)
100.00
Overall random effect
I2
= 66 (95% CI 43%-80%), p <0.001
0.14 (0.11, 0.17)
0
.2
.4
.6
.8
1
Effect Size
10
References for Supplemental figures 3a-3d
Supplementary references for the SNP rs1800775 (C-629A) and SNP rs708272 (Taq1B)
1. Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, Penninx BW, Janssens
AC, Wilson JF, Spector T, Martin NG, Pedersen NL, Kyvik KO, Kaprio J, Hofman A, Freimer NB,
Jarvelin MR, Gyllensten U, Campbell H, Rudan I, Johansson A, Marroni F, Hayward C, Vitart V,
Jonasson I, Pattaro C, Wright A, Hastie N, Pichler I, Hicks AA, Falchi M, Willemsen G, Hottenga JJ,
De Geus EJ, Montgomery GW, Whitfield J, Magnusson P, Saharinen J, Perola M, Silander K,
Isaacs A, Sijbrands EJ, Uitterlinden AG, Witteman JC, Oostra BA, Elliott P, Ruokonen A, Sabatti C,
Gieger C, Meitinger T, Kronenberg F, Doring A, Wichmann HE, Smit JH, McCarthy MI, van Duijn
CM, Peltonen L. Loci influencing lipid levels and coronary heart disease risk in 16 European
population cohorts. Nat Genet 2009;41:47-55.
2. Barzilai N, Atzmon G, Schechter C, Schaefer EJ, Cupples AL, Lipton R, Cheng S, Shuldiner AR.
Unique lipoprotein phenotype and genotype associated with exceptional longevity. JAMA
2003;290:2030-40.
3. Bauerfeind A, Knoblauch H, Schuster H, Luft FC, Reich JG. Single nucleotide polymorphism
haplotypes in the cholesteryl-ester transfer protein (CETP) gene and lipid phenotypes. Hum Hered
2002;54:166-73.
4. Bernstein MS, Costanza MC, James RW, Morris MA, Cambien F, Raoux S, Morabia A. No physical
activity x CETP 1b.-629 interaction effects on lipid profile. Med Sci Sports Exerc 2003;35:1124-9.
5. Blankenberg S, Tiret L, Bickel C, Schlitt A, Jungmair W, Genth-Zotz S, Lubos E, Espinola-Klein C,
Rupprecht HJ. [Genetic variation of the cholesterol ester transfer protein gene and the prevalence
of coronary artery disease. The AtheroGene case control study]. Z Kardiol 2004;93 Suppl 4:IV16IV23.
6. Chasman DI, Pare G, Zee RYL, Parker AN, Cook NR, Buring JE, Kwiatkowski DJ, Rose LM, Smith
JD, Williams PT, Rieder MJ, Rotter JI, Nickerson DA, Krauss RM, Miletich JP, Ridker PM. Genetic
Loci Associated With Plasma Concentration of Low-Density Lipoprotein Cholesterol, High-Density
Lipoprotein Cholesterol, Triglycerides, Apolipoprotein A1, and Apolipoprotein B Among 6382 White
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17
Supplemental Figure 4: Effect modification of genetic effects by gender, ghee consumption and
tobacco consumption in Pakistanis
Lipid trait
SNP
Gene
No. of
participants
Change in lipid trait
(mmol/L) per copy
of the minor allele
(95% CI)
2,558
578
0.03 (-0.04, 0.10)
0.09 (-0.05, 0.23)
0.13
449
1,925
635
0.09 (0.05, 0.13)
0.05 (-0.08, 0.18)
0.05 (-0.07, 0.17)
0.50
oil
combination
Ever
Current
1,625
1,426
0.08 (0.04, 0.12)
0.07 (-0.03, 0.18)
0.78
male
female
2,559
578
-0.09 (-0.18, -0.01)
-0.08 (-0.26, 0.10)
0.82
450
1,925
635
-0.07 (-0.12, -0.02)
-0.12 (-0.28, 0.03)
-0.06 (-0.20, 0.08)
0.60
Ever
Current
1,625
1,427
-0.10 (-0.15, -0.05)
-0.05 (-0.18, 0.07)
0.27
male
female
2,557
578
0.13 (0.05, 0.21)
0.15 (-0.03, 0.32)
0.78
449
1,924
635
0.12 (0.08, 0.17)
0.06 (-0.09, 0.22)
0.24 (0.10, 0.38)
0.01
Ever
Current
1,624
1,426
0.11 (0.06, 0.16)
0.16 (0.04, 0.29)
0.16
male
female
2,424
498
-0.09 (-0.26, 0.08)
-0.16 (-0.52, 0.19)
0.44
ghee
oil
combination
438
1,769
601
-0.20 (-0.29, -0.11)
-0.03 (-0.31, 0.25)
-0.14 (-0.40, 0.11)
0.24
Ever
Current
1,486
1,357
-0.18 (-0.28, -0.09)
-0.13 (-0.36, 0.10)
0.46
male
female
2,440
505
0.06 (0.04, 0.09)
0.05 (-0.01, 0.11)
0.32
ghee
oil
combination
441
1,785
603
0.05 (0.03, 0.06)
0.06 (0.01, 0.11)
0.06 (0.02, 0.11)
0.49
Ever
Current
1,504
1,360
0.05 (0.03, 0.07)
0.05 (0.01, 0.09)
0.71
Subgroup
Interaction
p-value
Log triglycerides
rs1260326
GCKR
Gender
male
female
Oil typeghee
Tobacco
Rs271
LPL
Gender
Oil type
ghee
oil
combination
Tobacco
rs651821
APOA5/ZNF259
Gender
Oil type
ghee
oil
combination
Tobacco
LDL-C
Rs646776
CELSR2
Gender
Oil type
Tobacco
HDL-C
rs711752
CETP
Gender
Oil type
Tobacco
-.516
0
.516
Lipid level (mmol/L) and 95% confidence intervals
Analyses are presented only for the lead SNPs at loci that showed highly signficant associations with lipid traits (P < 10-6)
Size of data markers are proportional to the inverse of the variance of the minor allele effect. P-values were derived from F tests of the interaction terms fitted in
linear regression models of each lipid trait, adjusted for age, gender, the first two principle components and case-control status.
18
Supplemental Figure 5(a): Association with HDL-C (mmol/l) in PROMIS and LURIC participants of SNPs
discovered in previous genome wide association studies in association with HDL-C levels
genome wide association studies in association with HDL-C levels
P-value
P-value
for difference
for association between studies
Number
of subjects
Mean difference
(95% CI)
MAF
A
3023
2452
0.00 (-0.01, 0.02)
0.02 (0.00, 0.03)
.45
.58
5.1e-01
2.2e-02
0.09
rs4846914
GALNT2
A
3022
2450
0.00 (-0.01, 0.02)
0.02 (0.00, 0.03)
.45
.58
4.9e-01
2.1e-02
0.09
2
rs693
APOB
T
3024
2451
-0.01 (-0.02, 0.00)
-0.02 (-0.03, -0.00)
.27
.46
1.6e-01
4.4e-02
0.30
8
rs328
LPL
G
3024
2451
0.02 (-0.00, 0.04)
0.05 (0.03, 0.07)
.10
.11
5.8e-02
2.6e-05
0.10
.44
.55
6.0e-01
5.4e-03
0.08
Chr
SNP
Gene
Minor
allele
1
rs2144300
GALNT2
1
8
rs2197089
LPL
A
3001
2434
0.00 (-0.01, 0.02)
0.02 (0.01, 0.04)
9
rs3890182
ABCA1
T
3024
2451
-0.01 (-0.03, 0.01)
-0.03 (-0.05, -0.01)
.08
.12
3.8e-01
5.9e-03
0.10
9
rs1883025
ABCA1
A
3023
2451
-0.01 (-0.03, -0.00)
-0.02 (-0.04, -0.01)
.35
.26
3.4e-02
4.2e-03
0.35
12
rs2338104
KCTD10
C
3023
2451
0.01 (-0.00, 0.02)
0.00 (-0.01, 0.02)
.44
.57
1.8e-01
5.0e-01
0.87
15
rs1800588
LIPC
T
3024
2451
0.02 (0.01, 0.03)
0.04 (0.02, 0.06)
.25
.23
7.3e-03
1.2e-05
0.08
15
rs261332
LIPC
A
3022
2452
0.02 (0.01, 0.04)
0.04 (0.02, 0.05)
.19
.22
5.5e-03
8.7e-05
0.16
16
rs3764261
CETP
A
3023
2451
0.05 (0.04, 0.06)
0.06 (0.04, 0.07)
.34
.32
1.2e-12
1.1e-13
0.69
16
rs1800775
CETP
G
3024
2451
-0.05 (-0.06, -0.03)
-0.06 (-0.08, -0.05)
.40
.4
.53
3.0e-12
3.7e-17
0.13
16
rs711752
CETP
T
3023
2451
0.05 (0.04, 0.06)
0.06 (0.04, 0.07)
.47
.42
4.7e-14
5.0e-15
0.52
16
rs1864163
CETP
A
3024
2443
-0.04 (-0.06, -0.03)
-0.06 (-0.08, -0.05)
.22
.27
1.6e-08
2.4e-13
0.15
16
rs1532624
CETP
T
3023
2448
0.04 (0.03, 0.06)
0.06 (0.04, 0.07)
.48
.43
4.1e-12
8.7e-15
0.30
16
rs7499892
CETP
A
3023
2452
-0.04 (-0.06, -0.03)
-0.06 (-0.08, -0.04)
.22
.18
3.8e-08
8.5e-11
0.10
16
rs5880
CETP
G
3024
2451
-0.06 (-0.09, -0.04)
-0.07 (-0.10, -0.04)
.08
.05
7.9e-08
5.2e-05
0.67
16
rs5882
CETP
C
3024
2452
0.02 (0.01, 0.04)
0.03 (0.02, 0.05)
.43
.32
5.4e-04
3.8e-05
0.28
16
rs1800777
CETP
A
3024
2452
-0.06 (-0.10, -0.03)
-0.07 (-0.11, -0.03)
.04
.04
1.8e-04
2.8e-04
0.63
16
rs255052
DPEP2
A
3024
2449
0.03 (0.01, 0.04)
0.03 (0.01, 0.05)
.19
.15
5.4e-04
6.0e-03
0.74
18
rs2156552
(intergenic)
A
3020
2451
-0.02 (-0.04, -0.00)
-0.00 (-0.02, 0.02)
.14
.15
2.6e-02
6.9e-01
19
rs157580
APOE
G
3015
2449
0.01 (-0.00, 0.02)
0.00 (-0.01, 0.02)
.47
.36
1.9e-01
5.4e-01
0.92
20
rs1800961
HNF4A
A
3021
2451
-0.01 (-0.05, 0.03)
-0.07 (-0.12, -0.03)
.03
.04
5.3e-01
8.8e-04
0.02
PROMIS
-.1
-.05
0
.05
0.19
.1
LURIC
19
Supplemental Figure 5(b): Association with LDL-C (mmol/l) in PROMIS and LURIC participants of SNPs
discovered in previous genome wide association studies in association with LDL-C levels
Chr SNP
Gene
Minor Number
allele of participants
Mean difference
(95% CI)
MAF
P-value
for association
P-value for
difference
between studies
1
rs646776
CELSR2
G
3014
1175
-0.15 (-0.23, -0.08)
-0.01 (-0.09, 0.06)
.25
.24
3.4e-05
7.2e-01
0.05
1
rs599839
CELSR2
G
3013
1176
-0.16 (-0.23, -0.08)
-0.02 (-0.09, 0.06)
.25
.24
2.7e-05
6.7e-01
0.04
2
rs693
APOB
T
3015
1176
0.01 (-0.06, 0.07)
0.03 (-0.04, 0.09)
.27
.45
8.9e-01
4.4e-01
0.64
2
rs7575840 A
(intergenic)
3015
1176
0.01 (-0.08, 0.09)
0.06 (-0.01, 0.13)
.16
.30
8.6e-01
8.8e-02
0.19
5
rs12654264 A
HMGCR
3014
1175
-0.03 (-0.09, 0.04)
-0.01 (-0.07, 0.06)
.42
.63
4.0e-01
8.5e-01
0.46
5
rs3846662
HMGCR
T
3015
1175
-0.02 (-0.08, 0.04)
-0.00 (-0.07, 0.06)
.41
.58
5.5e-01
9.7e-01
0.89
6
rs2254287
COL11A2
C
3014
1174
0.05 (-0.01, 0.11)
-0.01 (-0.07, 0.06)
.49
.58
1.3e-01
8.0e-01
0.06
11
rs102275
FADS1
C
3013
1175
-0.11 (-0.19, -0.03)
-0.04 (-0.11, 0.03)
.20
.31
4.5e-03
2.6e-01
0.05
11
rs174570
FADS2
T
3013
1176
-0.16 (-0.27, -0.05)
-0.04 (-0.13, 0.06)
.08
.13
3.5e-03
4.5e-01
0.02
11
rs1535
FADS1
G
3009
1175
-0.10 (-0.18, -0.02)
-0.04 (-0.11, 0.03)
.18
.30
1.4e-02
2.5e-01
0.03
19
rs6511720
LDLR
T
3015
1176
-0.08 (-0.19, 0.04)
-0.11 (-0.20, -0.01)
.08
.13
1.9e-01
3.7e-02
0.75
19
rs2228671
LDLR
A
3015
1177
-0.06 (-0.18, 0.06)
-0.07 (-0.17, 0.03)
.07
.13
3.5e-01
1.6e-01
0.91
19
rs16996148 T
CILP2
3013
1176
-0.03 (-0.13, 0.07)
-0.07 (-0.18, 0.04)
.11
.10
5.1e-01
1.9e-01
0.78
19
rs157580
APOE
G
3006
1175
0.06 (0.00, 0.12)
0.01 (-0.06, 0.07)
.47
.35
4.6e-02
8.5e-01
0.38
19
rs2075650
APOE
C
3014
1177
0.06 (-0.03, 0.15)
0.07 (-0.02, 0.17)
.11
.14
2.0e-01
1.2e-01
0.51
-.2
PROMIS
-.15
-.1
-.05
0
.05
.1
.15
Effect size
LURIC
20
Supplemental Figure 5(c): Association with log triglycerides (mmol/l) in PROMIS and LURIC participants of SNPs
discovered in previous genome wide association studies in association with triglyceride levels
Minor
allele
MAF
P-value
for association
P-value
for difference
between studies
Number
of participants
Mean difference
(95% CI)
G
3195
2440
-0.06 (-0.08, -0.03)
-0.02 (-0.05, 0.01)
.45
.31
2.6e-05
1.3e-01
0.07
rs12130333
(intergenic)
T
3197
2451
-0.04 (-0.07, -0.00)
-0.02 (-0.05, 0.01)
.17
.20
4.6e-02
2.5e-01
0.48
1
rs4846914
GALNT2
A
3194
2450
-0.02 (-0.04, 0.01)
-0.03 (-0.05, 0.00)
.45
.58
2.4e-01
6.8e-02
0.80
2
rs693
APOB
T
3197
2451
0.03 (-0.00, 0.06)
0.02 (-0.00, 0.05)
.27
.46
8.1e-02
9.1e-02
0.81
2
rs673548
APOB
A
3196
2451
-0.04 (-0.06, -0.01)
-0.04 (-0.07, -0.01)
.49
.22
5.7e-03
6.8e-03
0.69
2
rs1260326
GCKR
T
3196
2451
0.08 (0.05, 0.11)
0.08 (0.05, 0.10)
.26
.44
9.0e-07
7.2e-09
0.86
2
rs780094
GCKR
T
3185
2449
0.07 (0.04, 0.11)
0.08 (0.05, 0.11)
.26
.44
2.6e-06
3.9e-09
0.69
7
rs714052
BAZ1B
G
3193
2451
-0.08 (-0.12, -0.03)
-0.02 (-0.06, 0.03)
.10
.11
1.0e-03
4.9e-01
0.09
7
rs17145738
TBL2
T
3197
2451
-0.09 (-0.14, -0.05)
-0.02 (-0.06, 0.03)
.10
.11
7.3e-05
4.5e-01
0.02
8
rs328
LPL
G
3197
2451
-0.08 (-0.13, -0.03)
-0.12 (-0.17, -0.08)
.10
.11
6.5e-04
2.2e-08
0.12
8
rs2197089
LPL
A
3172
2434
-0.02 (-0.05, 0.01)
-0.04 (-0.07, -0.02)
.44
.55
1.7e-01
1.8e-03
0.39
8
rs17321515
(intergenic)
C
3197
2450
-0.01 (-0.04, 0.01)
-0.03 (-0.06, -0.00)
.37
.48
3.3e-01
3.5e-02
0.65
11
rs12286037
APOA5
T
3197
2451
0.12 (0.06, 0.19)
0.14 (0.09, 0.19)
.04
.06
2.8e-04
1.3e-07
0.68
11
rs662799
ZNF259
C
3195
2452
0.14 (0.11, 0.18)
0.08 (0.03, 0.13)
.17
.068
1.2e-14
2.2e-03
0.07
19
rs16996148
CILP2
T
3195
2451
-0.05 (-0.09, -0.01)
-0.11 (-0.16, -0.06)
.11
.09
2.5e-02
6.5e-06
0.07
19
rs157580
APOE
G
3188
2449
-0.03 (-0.06, -0.01)
-0.02 (-0.05, 0.01)
.47
.36
1.6e-02
1.9e-01
0.73
19
rs439401
APOE
C
3183
2426
0.04 (0.01, 0.07)
0.04 (0.02, 0.07)
.45
.61
4.1e-03
1.9e-03
0.50
Chr
SNP
1
rs1748195
DOCK7
1
-.15
-.1
-.05
0
.05
.1
.15
.2
PROMIS
LURIC
WebFigures 4 (a-b): Estimates represent the per-minor allele increase in lipid levels, adjusted for age, sex, the first two
principal components and case-control status. The P-value for difference between studies corresponds to a test of
nullity of interaction term between study and the SNP of interest. Boxes are proportional to the inverse of the variance
of study estimates. Chr: chromosome, SNP: Single Nucleotide Polymorphism, MAF: minor allele frequency
21
Supplemental Figure 6(a): Comparison of linkage disequilibrium in PROMIS and LURIC participants for genes with nominally significant associations with
HDL-C concentration
22 Supplemental Figure 6(b): Comparison of linkage disequilibrium in PROMIS and LURIC participants for genes with nominally significant associations with
triglyceride concentration
23 Supplemental Figure 6(c): Comparison of linkage disequilibrium in PROMIS and LURIC participants for genes with nominally significant associations with
LDL-C concentration
24 Supplemental Figure 7(a): Association with MI for SNPs associated with high density cholesterol in
PROMIS
SNP
Risk allele
Gene
OR (95% CI)
P-value
Chromosome 9
rs1883025
A
ABCA1
0.92 (0.83, 1.02)
.35
.11
Chromosome 15
rs1800588
rs261332
T
A
LIPC
LIPC
0.89 (0.78, 0.99)
0.84 (0.73, 0.96)
.24
.18
.026
.0049
Chromosome 16
rs12708967
rs3764261
rs17231506
rs1800775
rs711752
rs708272
rs1864163
rs11508026
rs12720922
rs9939224
rs1532625
rs1532624
rs11076175
rs7499892
rs11076176
rs5880
rs5882
rs1800777
rs255052
G
A
A
G
T
A
A
A
T
A
T
T
G
A
C
G
C
A
A
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
CETP
DPEP2
0.98 (0.86, 1.09)
0.97 (0.88, 1.07)
0.97 (0.88, 1.07)
0.97 (0.87, 1.06)
0.99 (0.90, 1.08)
0.99 (0.90, 1.08)
0.97 (0.86, 1.08)
0.98 (0.88, 1.07)
0.97 (0.86, 1.08)
0.97 (0.86, 1.08)
0.99 (0.90, 1.09)
0.98 (0.89, 1.07)
1.00 (0.88, 1.12)
1.01 (0.90, 1.12)
1.00 (0.89, 1.11)
1.00 (0.83, 1.17)
0.97 (0.88, 1.06)
0.93 (0.69, 1.17)
1.15 (1.03, 1.26)
.22
.33
.33
.4
.47
.47
.22
.46
.2
.22
.48
.48
.19
.22
.21
.08
.43
.038
.2
.65
.6
.6
.49
.82
.86
.57
.62
.57
.55
.87
.7
.99
.81
.97
.97
.49
.56
.017
Chromosome 18
rs2156552
A
(intergenic)
0.94 (0.81, 1.07)
.14
.37
.8
MAF
1 1.2
odds ratio
25
Supplemental Figure 7(b): Association with MI for SNPs associated with low density cholesterol in
PROMIS
SNP
Risk allele
Gene
OR (95% CI)
MAF
P-value
Chromosome 1
rs646776
G
CELSR2
0.85 (0.75, 0.96)
.24
.0041
rs599839
G
CELSR2
0.87 (0.76, 0.98)
.24
.011
rs102275
C
FADS1
0.99 (0.88, 1.11) .2
rs174570
T
FADS2
1.03 (0.87, 1.19)
.085
.72
rs1535
G
FADS1
0.95 (0.83, 1.07)
.17
.42
G
APOE
0.99 (0.90, 1.09)
.46
.91
Chromosome 11
.93
Chromosome 19
rs157580
.8
1
1.2
1.4
odds ratio
26
Supplemental Figure 7c: Association with MI for SNPs associated with triglycerides in PROMIS
SNP
Risk allele
Gene
OR (95% CI)
MAF
P-value
Chromosome 1
rs1748195
rs12130333
G
T
DOCK7
(intergenic)
1.03 (0.94, 1.12)
1.06 (0.95, 1.18)
.46
.18
.56
.31
Chromosome 2
rs673548
rs1260326
rs780094
rs780093
A
T
T
T
APOB
GCKR
GCKR
GCKR
0.95 (0.86, 1.04)
1.01 (0.90, 1.11)
1.02 (0.92, 1.13)
1.03 (0.93, 1.14)
.49
.26
.26
.26
.24
.91
.68
.56
Chromosome 7
rs714052
rs17145738
G
T
BAZ1B
TBL2
0.96 (0.81, 1.12)
0.97 (0.81, 1.13)
.1
.095
.63
.72
Chromosome 8
rs271
rs328
A
G
LPL
LPL
0.86 (0.74, 0.99)
0.84 (0.68, 0.99)
.16
.091
.021
.027
Chromosome 11
rs12286037
rs2075290
rs2266788
rs2072560
rs651821
rs662799
rs10750097
T
G
G
A
C
C
G
APOA5
APOA5
ZNF259
ZNF259
ZNF259
ZNF259
APOA5
0.99 (0.77, 1.22)
0.97 (0.85, 1.09)
0.94 (0.82, 1.05)
0.97 (0.84, 1.09)
0.96 (0.84, 1.08)
0.96 (0.84, 1.08)
0.98 (0.88, 1.07)
.043
.18
.19
.16
.17
.17
.44
.94
.61
.28
.6
.53
.52
.61
Chromosome 19
rs16996148
rs157580
rs439401
T
G
C
CILP2
APOE
APOE
0.92 (0.77, 1.07)
0.99 (0.90, 1.09)
0.97 (0.88, 1.06)
.11
.46
.45
.27
.91
.54
.8
1
1.2 1.4
odds ratio
27
Acknowledgements
We would like to acknowledge the contributions of the following individuals:
Epidemiological fieldwork in Pakistan: Zeeshan Ozair, Fahad Shuja, Mustafa Qadir
Hameed, Imad Hussain, Hamza Khalid, Ali Memon, Kamran Shahid, Ali Kazmi, Sana
Nasim, Muhammad Ahsan Javed, Zahir Hussain, Kanwal Aamir, Mazhar Khan,
Muhammad Zuhair Yusuf, Muhammad Zafar, Faisal Majeed, Madiha Ishaq, Turkey
Hussain Marmoos, Faud Khurshid, Farhat Abdul Muntaqim, Sarosh Fatima, Rehan
Ahmed, Muhammad Nabeel, Mansoor Ahmed Khokar, Syed Shazad Hussain, Madad
Ali Ujjan, Parveen Sultan, Asghar Ali, Ayaz Ali, Mir Alam, Hassan Zaib, Abdul
Ghafoor, Saeed Ahmed, Muhammad Riazuddin, Muhammad Irshad Javed, Jabir
Furqan, Abdul Ghaffar, Muhammad Shahid, Tanveer Baig Mirza, Muhammad Naeem,
Afzal Hussain, Abdul Hakeem, Zahid Hussain, Tanveer Abbas, Muhammad Khurram
Shahzad, Khowaja Muhammad Shoaib, Muhammad Imran Nisar, Altaf Hussain, Waleed
Kayani, Muhammad Shazad, Mehmood Jafree and Ayeesha Kamal.
Laboratory assays: Asad Ali Shah, Sobia Naz, Farina Hanif, Shaheen Khanum, Aisha
Nazir, Aisha Sultana, Mehwish Jabar, Zahid Hussain, Madiha Yameen, Nadir Khan,
Inosh Hasan, Jonathan Stephens, Pamela Whittaker, Radhi Ravindrarajah, Owen T
McCann and the personnel of the WTSI Genotyping Facility Jackie Bryant, Sarah L. Clark,
Jen S. Conquer, Thomas Dibling, Stephen Gamble, Clifford Hind, Michelle Ricketts, Claire R.
Stribling, Sam Taylor, Alicja Wilk, Julia C. Wyatt, Silvia Behaim, Ursula Discher, Isolde
Friedrich, Brigitte Haas, Gaby Herr and Brigitte Kreisel.
Data management: Sarfaraz Sher Ali, Touqeer Ahmed, Fariha Nadeem, Matthew
Walker, Sarah Watson and Mohammed J.R. Ghori.
Epidemiological/statistical support: Nilesh Samani and Kausik Ray.
Administration: Kashif Saleheen and Hannah Sneath.
28

Genetic Determinants of Major Blood Lipids in Pakistanis Compared

Transcription

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Novità nella riparazione tessutale - Area-c54

Genetica 7 - Università degli Studi di Verona

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INGREDIENTI ORIGINALI PER UN GELATO SENZA UGUALI.

Estensioni della genetica mendeliana

L`importanza dei numeri EC, il loro funzionamento e significato.