Genetic Determinants of Major Blood Lipids in Pakistanis Compared With Europeans

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DOI: 10.1161/CIRCGENETICS.109.906180 published online Jun 22, 2010; Circ Cardiovasc Genet

Panos Deloukas Simon Thompson, Willem Ouwehand, Winfried März, Philippe Frossard, John Danesh and

Böhm,Sarah Bray, Ralph McGinnis, Frank Dudbridge, Bernhard R. Winkelmann, Bernhard Shahid, Shahzad Majeed Bhatti, Syed Saadat Ali, Muhammad Fahim, Gurdeep Sagoo,

Kumar, Muhammad Salman Daood, Aftab Alam Gul, Shahid Abbas, Junaid Zafar, Faisal Anthony Attwood, Kerstin Koch, Mustafa Hussain, Kishore Kumar, Asim Saleem, Kishwar

Hoffmann, Wilfried Renner, Marcus Kleber, Tanja B. Grammer, Jonathon Stephens, Gardezi, Nazir Ahmed Memon, Abdul Ghaffar, Fazal-ur Rehman, Michael Marcus

Muhammad Ishaq, Syed Zahed Rasheed, Rashid Jooma, Jawaid Hassan Niazi, Ali Raza Qamar, Azhar Faruqui, Nadeem Hayat Mallick, Muhammad Azhar, Abdus Samad,

Hakeem, Khan Shah Zaman, Assadullah Kundi, Zia Yaqoob, Liaquat Ali Cheema, Nadeem Murtaza, Alexander Thompson, Reeta Gobin, Adam Butterworth, Usman Ahmad, Abdul

E. Hunt, Nasir Sheikh, Nabi Shah, Maria Samuel, Shajjia Razi Haider, Muhammed Suzanna Bumpstead, Stephen Kaptoge, Emanuele Di Angelantonio, Nadeem Sarwar, Sarah

Myriam Alexander, Michael Inouye, Moazzam Zaidi, Simon Potter, Philip Haycock, Danish Saleheen, Nicole Soranzo, Asif Rasheed, Hubert Scharnagl, Rhian Gwiliam,

EuropeansGenetic Determinants of Major Blood Lipids in Pakistan Is Compared with

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1

Genetic Determinants of Major Blood Lipids in Pakistan Is Compared with Europeans

Running title: Saleheen et al.; Genetic loci for major lipids in Pakistan

Danish Saleheen+1,2 MBBS, MPhil; Nicole Soranzo+3,4 BSC, PhD; Asif Rasheed1 MBBS; Hubert

Scharnagl5 PhD; Rhian Gwilliam3 PhD; Myriam Alexander2 MSc, MPhil; Michael Inouye3 PhD;

Moazzam Zaidi1 MBBS; Simon Potter3 PhD; Philip Haycock2 MSc, MPhil; Suzanna Bumpstead3

BSC; Stephen Kaptoge2 PHD; Emanuele Di Angelantonio2 MD, MSc, PhD; Nadeem Sarwar2,6

MRPharmS, PhD; Sarah E Hunt3 PhD; Nasir Sheikh2 MSc; Nabi Shah1 B-Pharmacy; Maria

Samuel1 BSC, MSC; Shajjia Razi Haider1 MSC; Muhammed Murtaza1 MBBS; Alexander

Thompson2 PhD; Reeta Gobin2 MBBS, MPhil; Adam Butterworth2 PhD, MSc; Usman Ahmad1

MBBS; Abdul Hakeem1 MBBS; Khan Shah Zaman7 MBBS, MRCP, FRCP, MRCS; Assadullah

Kundi7 MBBS, FCPS; Zia Yaqoob7 MBBS, FACC; Liaquat Ali Cheema7 MBBS, PhD; Nadeem

Qamar7 MBBS FACC; Azhar Faruqui7 FACC, FRCP, FCPS, FAHA; Nadeem Hayat Mallick8

MBBS, MRCP; Muhammad Azhar8 MBBS, MRCP; Abdus Samad9 MD, FACC; Muhammad

Ishaq9 MBBS, MRCP, FRCP, FACC; Syed Zahed Rasheed9 MD, FESC, FRCP; Rashid Jooma10

MBBS; Jawaid Hassan Niazi10 MBBS, FCPS; Ali Raza Gardezi11 MBBS, Dip Card, MRCP;

Nazir Ahmed Memon12 MBBS, FRCP, FACC, FACVS; Abdul Ghaffar12 MBBS, FCPS; Fazal-

ur-Rehman13 MBBS, Dip Card; Michael Marcus Hoffmann14 PhD; Wilfried Renner5 PHD;

Marcus E Kleber15 PhD; Tanja B Grammer16 MD; Jonathon Stephens17 BSC; Anthony

Attwood17; Kerstin Koch17 PhD; Mustafa Hussain1 MBBS; Kishore Kumar1 MBBS; Asim

Saleem1 MBBS; Kishwar Kumar1 MBBS; Muhammad Salman Daood1 MBBS; Aftab Alam Gul1

MBBS; Shahid Abbas1 MBBS; Junaid Zafar1 MBBS; Faisal Shahid1 MBBS; Shahzad Majeed

Bhatti1 MBBS; Syed Saadat Ali1 MBBS; Muhammad Fahim1 MBBS; Gurdeep Sagoo18 BSC,

MSc, PhD; Sarah Bray19 MA, PhD, Grad Dip; Ralph McGinnis3 PhD; Frank Dudbridge19 PhD;

Bernhard R Winkelmann20 PhD; Bernhard Böehm21 MD, PhD; Simon Thompson19 DSc; Willem

Ouwehand4,17 MD, PhD, FRCPath; Winfried März6,16,20 MD; Philippe Frossard1 PhD, DSc;

*John Danesh2 MBChB, MSc, DPhil, FRCP, FFPH; *Panos Deloukas3 PhD

1Center for Non-Communicable Diseases (CNCD) Karachi Pakistan, 2Department of Public Health and Primary Care, University of Cambridge, UK; 3Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK; 4Department of Twin Research & Genetic Epidemiology, King's College London, St Thomas'

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Hospital Campus, Lambeth Palace Rd, London, UK; 5Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Graz, Austria; 6Section of Population Health, University of Aberdeen, UK; 7National Institute of Cardiovascular Diseases, Karachi, Pakistan; 8Punjab Institute of Cardiology Lahore Pakistan; 9Karachi Institute of Heart Diseases, Karachi, Pakistan; 10Jinnah Post-graduate Medical Centre, Karachi, Pakistan; 11Multan Institute of Cardiology, Multan, Pakistan; 12Civil Hospital, Hyderabad, Pakistan; 13Red Crescent Institute of Cardiology, Hyderabad, Pakistan; 14Division of Clinical Chemistry, Department of Medicine, Albert Ludwig University, Freiburg Germany; 15LURIC non profit LLC, Freiburg, Germany; 16Synlab Center of Laboratory Diagnostics Heidelberg, Heidelberg, Germany; 17Department of Haematology, University of Cambridge and NHS Blood and Transplant, Cambridge, UK. 18PHG foundation, Strangeways Research Laboratories, UK; 19MRC Biostatistics Unit, Cambridge, United Kingdom; 20Division of Endocrinology and Diabetes and Institute of Public Health, Social Medicine and Epidemiology, Medical Faculty Mannheim, University of Heidelberg, Germany, Graduate School Molecular Endocrinology and Diabetes, University of Ulm, Ulm Germany; 21Cardiology Group Frankfurt, Frankfurt, Germany, + these authors contributed equally; * these authors contributed equally

Correspondence: Dr Danish Saleheen

Center for Non-Communicable Diseases (CNCD),

Karachi, Pakistan &

Department of Public Health and Primary Care

University of Cambridge

Strangeways Research Laboratory

Cambridge CB1 8RN, UK

[email protected], [email protected]

Dr Panos Deloukas

Wellcome Trust Sanger Institute

Wellcome Trust Genome Campus

Hinxton CB10 1SA.UK

[email protected]

Journal Subject Codes: [8] Epidemiology [109] Clinical genetics [112] Lipids [89] Genetics of cardiovascular disease [146] Genomics [90] Lipid and lipoprotein metabolism

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Abstract

Background Evidence is sparse about the genetic determinants of major lipids in

Pakistanis.

Methods and Results 45,000 variants 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. 41 variants at 14 loci were significantly

associated with levels of HDL-C, 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<10-4).

Conclusions 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 effect sizes of lipid-related variants can differ

between Pakistanis and Europeans.

Key words: Lipids, HDL-C, LDL-C, triglycerides, Pakistan, Gene, Population structure, GWAS, IBC-array, Meta-analysis

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4

INTRODUCTION

Levels 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 serum 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 scans.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 five genetic markers in relation to a few hundred participants.17

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 175 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.

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5

MATERIALS AND METHODS

Participants This paper follows the reporting recommendations of STREGA.21

PROMIS is a case-control study of acute myocardial infarction (MI) in six centres in

urban Pakistan.20 MI cases had symptoms within 24 hours of hospital presentation,

typical electrocardiographic changes, and a positive troponin-I test. Controls 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) nonblood related visitors of index MI cases. People with

recent illnesses or infections were not eligible. Information was recorded on personal

and paternal 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 concentration

was measured using enzymatic methods (Roche Diagnostics, USA) at the Center for

Non-Communicable Diseases, Pakistan. LDL-C was calculated using Friedewald’s

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 50% in at least

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on coronary vessel. Individuals with 20% but <50% stenosis were excluded from the

analyses. Individuals with stenosis <20% were regarded as controls. 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 Genotyping for both studies 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 genomewide

scans (5) priority SNPs identified by IBC consortium investigators.18 45,237 SNPs 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 P-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 and being uninformative in Pakistanis and Europeans). LURIC participants

were typed with the version 2 of the IBC array and underwent the same calling and

quality control procedures. As version 2 has 4,050 additional SNPs, these SNPs were

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excluded from the current analysis. After quality control, 31,883 SNPs in 3197

Pakistanis and 35,533 SNPs in 2452 Germans remained for analyses.

Statistical methods To compare the genetic structure of Pakistanis with that of several

major ethnic groups, we received permission from HapMap3 investigators 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, as it is difficult to reliably infer the minor allele

for A/T or C/G SNPs for non-HapMap populations.8 To investigate genetic sub-

structure, we classified Pakistani participants into eight 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 P-value for each lipid against the expected –log10 p 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. Beta coefficients have been reported using the common allele as the reference

allele in PROMIS. All analyses were done using models adjusting for age, sex, the first

two principal components and case-control status. Effect estimates in LURIC were

reported for the same allele taken as reference in PROMIS.

We adopted a p 10-6 for declaration of significance. The Bonferroni correction for the

32,000 SNPs for three traits is 10-7, assuming 96000 independent tests with no prior

information. We chose a more relaxed cut-off 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

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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 without a history of cardiovascular disease published between

January 1970 and January 2009. We focused on SNPs (ie, rs1800775, rs708272,

rs646776, 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 were minimal previous data owing to the recency of its 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; 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 makes allowances

for between-study heterogeneity). P-value for difference between the effect estimates

obtained in PROMIS and European participants was calculated through a 2 test of

heterogeneity.

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9

RESULTS

The main characteristics of the Pakistani and German participants in this study are

summarised in Table 1. Comparison with HapMap3 population panels shows that the

Pakistani population clustered differently to that of 11 other major ethnic groups,

indicated by the separate clustering on the scatter plot of principal components (Figure

1). Pakistanis appear genetically closest to, but still clearly distinct from, Gujarati

Indians living in the USA, 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 Supplemental Figure 1). Compared with

Germans, the Pakistani participants were about a decade younger and had broadly

similar mean lipid values, though lower HDL-C (Table 1).

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 four genomic regions were associated with

lipid levels in Pakistanis (P 10-6), including 16 variants for HDL-C, 8 variants for

triglycerides, and one 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 &

Supplemental 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

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10

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 controls for these variants revealed

qualitatively similar results, with no substantial evidence of heterogeneity

(Supplemental Figures 2a-c). 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 Figure 5, meta-analyses of the two 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 two studies; the Taq1B variant was in strong LD with rs711752 (r2=0.99), the

lead variant in the Pakistani population. By 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 x 10-4; Figure 5 & Supplemental Figures 3a-1b).

Eight variants in two genomic regions were highly significantly associated with log

triglyceride concentration in the Pakistani participants. The most significant SNP

(rs662799; P=1.25 x 10-14) localized to the APOA5 gene (Figure 3b & Supplemental

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 & 4), with MAF about two

times higher in the Pakistani than German participants (0.17 v 0.07). This variant was in

concentration ooooooooooooooooof

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11

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 & Supplemental figure 3d), an

effect size that was lower than that observed in the Pakistani participants ( 2 for

difference P=7 x 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 seven-pass G-type receptor 2 (CELSR2) gene

was highly significantly associated with LDL-C concentration in the Pakistani

participants (P=1.25 x 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)

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

as observed in Pakistanis.25 The current meta-analysis of rs646776, however,

established its 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

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12

participants; Figure 5), an effect size comparable to that observed in Pakistanis ( 2 test

for difference P =0.84; Figure 5 & Supplemental figure 3c).

No significant interactions were observed on an additive scale of the 25 top variants

with lipid measures by levels of ghee or tobacco consumption or by sex (Supplemental

Figure 4). Qualitatively similar results were observed in analyses of the 875 cases in

PROMIS for whom information was available on time since onset of MI symptoms;

furthermore, adjustment for this variable yielded largely unchanged results (available

upon request).

Variants with nominally significant associations

Of the 152 lipid-related SNPs discovered through previous genome wide scans 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

two or all three traits). At a pre-specified 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 concentration

(including one variant in CELSR2 described above and 4 other variants in FADS1,

FADS2 and CELSR2: Supplemental 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

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13

LURIC participants revealed a similar pattern of LD, with somewhat stronger LD

blocks in APOB and LPL genes in Pakistanis (Supplemental Figure 6). Collectively,

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

on lipid concentration, although the current study was underpowered for reliable gene-

MI analyses (Supplemental 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 differing allelic frequencies and lipid effects for variants in APOA5 in

Pakistanis compared with Europeans. As discussed below, however, further studies are

needed to confirm whether such differences are mainly related to ethnicity rather than

other characteristics.

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14

Most of the highly significant lipid-related loci identified in Pakistani participants were

related to HDL-C and triglyceride, rather than LDL-C, a finding which 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 triglycerides 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 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

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15

impact on triglyceride concentration. However, as at least part of these differences

could have been due to non-ethnic 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% v 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 Pakistanis is, therefore, needed

involving better population-specific tools for genetic mapping. Larger replication

studies should also help to quantify and control any over-estimation in hypothesis-

generating estimates (“winner’s curse”). 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

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16

current study, calculation of LDL-C using Friedewald’s formula. However, as a large

prospective study has shown that associations of major lipids with CHD risk are at least

as extreme in non-fasted participants as in fasted participants,43 use of nonfasting

samples in the current study seems unlikely to have influenced materially the findings

here.

Funding Sources: Epidemiological fieldwork in PROMIS has been 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.

Conflict of Interest Disclosures: Dr Saleheen has received research funding from the Fogarty International Center, National Heart, Lung and Blood Institute, National Institute of Neurological Disorders and Stroke and 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, and Wellcome Trust.

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rch Council.

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10. Sabatti C, Service SK, Hartikainen AL, Pouta A, Ripatti S, Brodsky J, Jones CG, Zaitlen NA, Varilo T, Kaakinen M, Sovio U, Ruokonen A, Laitinen J, Jakkula E, Coin L, Hoggart C, Collins A, Turunen H, Gabriel S, Elliot P, McCarthy MI, Daly MJ, Jarvelin

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https://www.researchgate.net/publication/23782984_Hegele_R_A_Plasma_lipoproteins_genetic_influences_and_clinical_implications_Nat_Rev_Genet_10_109-121?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=

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https://www.researchgate.net/publication/23267427_Common_SNPs_in_HMGCR_in_Micronesians_and_Whites_Associated_With_LDL-Cholesterol_Levels_Affect_Alternative_Splicing_of_Exon13?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=




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https://www.researchgate.net/publication/7485417_A_variant_of_the_gene_encoding_leukotriene_A4_hydrolase_confers_ethnicity-specific_risk_of_myocardial_infarction?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=







https://www.researchgate.net/publication/23791045_Lipid_Profile_Plasma_Apolipoproteins_and_Risk_of_a_First_Myocardial_Infarction_Among_Asians_An_Analysis_From_the_INTERHEART_Study?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=




https://www.researchgate.net/publication/5389680_Mapping_the_Genetic_Architecture_of_Gene_Expression_in_Human_Liver?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=






22

40. Ioannidis JP, Thomas G, Daly MJ. Validating, augmenting and refining genome-wide association signals. Nat Rev Genet 2009;10(5):318-29.

41. Dhandapany PS, Sadayappan S, Xue Y, Powell GT, Rani DS, Nallari P, Rai TS, Khullar M, Soares P, Bahl A, Tharkan JM, Vaideeswar P, Rathinavel A, Narasimhan C, Ayapati DR, Ayub Q, Mehdi SQ, Oppenheimer S, Richards MB, Price AL, Patterson N, Reich D, Singh L, Tyler-Smith C, Thangaraj K. A common MYBPC3 (cardiac myosin binding protein C) variant associated with cardiomyopathies in South Asia. Nat Genet 2009;41:187-91.

42. Ioannidis JP, Thomas G, Daly MJ. Validating, augmenting and refining genome-wide association signals. Nat Rev Genet. 2009;10:318-29

43. Emerging Risk Factors Collaboration, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, Wood AM, Lewington S, Sattar N, Packard CJ, Collins R, Thompson SG, Danesh J. Major lipids, apolipoproteins, and risk of vascular disease. JAMA 2009;302:1993-2000

,



https://www.researchgate.net/publication/23798809_A_Common_MYBPC3_Cardiac_Myosin_Binding_Protein_C_Variant_Associated_with_Cardiomyopathies_in_South_Asia?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=






https://www.researchgate.net/publication/291841484_Major_Lipids_Apolipoproteins_and_Risk_of_Vascular_Disease?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=




23

Table 1: Some characteristics of the participants from PROMIS and LURIC studies

Data are mean (SD), median (IQR), or %.

Figure Legends

Figure 1: (a) Scatter plot of the first two 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)

Scatter plot of the first two principal components and self reported ethnicities in PROMIS

control participants

Figure 1 (a-b) PAK: Pakistani from the PROMIS controls; YRI: Yoruba in Ibadan Nigeria,

LWK: Luhya in Webuye Kenya, ASW: African ancestry in Southwest USA, MKK: Maasai in

PROMIS

LURIC

Characteristic n= 3195 n = 2452

Age (y) 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%

Body mass index (kg/m2) 25.2 (4.3) 27.4 (4.0)

Total cholesterol (mmol/l) 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)

Triglycerides (mmol/l) 0.56 (0.22 -0.95) 0.49 (0.21 – 0.81)

2727272727272727272727272727272727272222 .4.4.4.4.4.44.44.4.44.4.44.4.44444 ((((( (( (((( ((((( (4.4.4.4.4.4.4.4.444.4444 0)0)0)0)0)0)0)00)0)0000)000))00

/l) 4 6 (1 3) 5 0 (1 0)

c )

)

.

/l) 4.6 (1.3) 5.0 (1.0)

chchhhchoooloo esterol (((((mmmmololololo /l/l/l/l/l) ) ) ) 22.700000 ( ( (((111.11 222022 )) 2.2.2.2.2.9666 (((((0...88858 )

cholellel stststtterolll ((mmmmm llol/l/// ) 00.82828222 (((( (0.0.000.2424242424) 0.99999 ( (((0.0000 27727)

000.565656 (( (000.222222 -000.959595) ) ) ) 000.494949 (( (000.212121 – 0 0 0.



24

Kinyawa Kenya, GIH:Gujarati Indians in Houston, CEU: Utah residents with Northern and

Western European ancestry from the CEPH collection, TSI:Toscani in Italia, MEX: Mexican

ancestry in Los Angeles, California, JPT: Japanese in Tokyo, Japan, CHD:Chinese in

Metropolitan Denver, Colorado Texas, CHB:Han Chinese in Beijing, China. C1: First principal

component; C2: Second principal component

Figure 2: : Genomic inflation factor

Figures 3 (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

Figure 4: (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 as the

current gene array has only few SNPs in this gene.

Figure 5: 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 two

components of PCA. Estimates in Whites are derived from a random effect meta-analysis of

additive estimates. Individual plots for each meta-analysis are presented in Webfigures 2a-2d.

The p-value of heterogeneity derives from a heterogeneity test between the overall estimates in

Whites and the estimate in PROMIS. Boxes are proportional to the inverse of the variance of

study estimates. The mean difference is in mmol/l. Scales differ between lipids.

nnnnnnnnorororororororororororororororoooororro aaaaaaaaaaaaaaaaaalllllllllllllllllllllllllllelelelelelellelelelleeeeeee e e ee ee e eee ee e ee e frfrfrfrfrfrfrfrfrfrfrfrfrfrfrreqeqeqeqqeqeqeqeqeqeqeqeqeqeqeqeqeqqeqqe ueueueueueueueueueueuueuueueueueueueueuueue

URICCCCCCCCCCCCCCCCCCCCCC ((((((((((((((((((((( )))))))))))))))))))) LLLLLLLLLLLLLLLLLLLLLLLDDDDDDDDDDDDDDDDDDDDDD lIS (blue) and Luric (red) (b) LD plot (D ) LURIC (c) LD plo

d o

a o

a

h i ll l i i li id l l PROM

IS (blue) ))) and Luric (red) (b) LD plot (D ) LURIC (c) LD plo

drarararawn usisss ngngngng 1111595959595 555 PRPRPRPROMOMOMOMISSSS cc connttrolololol aaa andndndnd 1 1 1171717175 555 LULULULURIRIRIRIC CC C cocococontntntntro

ananananalylylylyseeseses s s s foooor r r r CECECECELSSSSR2R2R2R g g g genenenene eee innnn PPPPROROROROMIMIMIM S SSS anananand d d d LULULULURIRIRRIC C C C wewewewereeee n nno

as only few www SNSNSNSNPsPsPsPs i i iinn nn ththththisisisis g g ggenenenene.e.e.e

h i ll l i i li id l l PROM



C1C

2

(a) PROMIS compared to HAPMAP3 (b) PCA of PROMIS ethnicities alone

C2

C1



= 1.02 = 1.03= 1.02

Pakistani Participants (PROMIS)

HDL

German Participants (LURIC)

LDL TG

HDL LDL TG

= 1.04 = 1.03= 1.01= ==== 111.11 0101010101



16

16

16

16

16

16

16

16

16

16

16

16

16

16

16

16

rs7499892

rs1532624

CETP

rs1532625CETP

CETP

CETP

rs3764261

rs5880

rs1800775

rs11508026

CETP

rs12720922

CETP

CETP

rs12708967

CETP

CETP

CETP

CETP

CETP

rs11076175

rs11076176

rs711752

rs1864163

rs17231506

CETP

rs708272

rs9939224

CETP

CETP

CETP

A

T

T

A

G

T

A

T

G

G

C

T

A

A

A

A

3023

3023

2451

29962428

2451

2450

3023

3024

3024

3021

2448

3024

2451

2451

3021

2452

2451

2452

2450

2452

3022

3021

3023

3024

3023

2452

3023

3021

2451

2451

2443

-0.04 (-0.06, -0.03)

0.04 (0.03, 0.06)

-0.07 (-0.10, -0.04)

0.05 (0.03, 0.06)0.06 (0.04, 0.07)

-0.06 (-0.08, -0.05)

-0.06 (-0.08, -0.04)

0.05 (0.04, 0.06)

-0.06 (-0.09, -0.04)

-0.05 (-0.06, -0.03)

0.05 (0.03, 0.06)

0.06 (0.04, 0.07)

-0.04 (-0.06, -0.03)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.04)

-0.04 (-0.05, -0.02)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.04)

-0.04 (-0.05, -0.02)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.04)

-0.04 (-0.06, -0.03)

-0.04 (-0.05, -0.02)

0.05 (0.04, 0.06)

-0.04 (-0.06, -0.03)

0.05 (0.04, 0.06)

-0.06 (-0.08, -0.04)

0.05 (0.04, 0.06)

-0.04 (-0.05, -0.02)

0.06 (0.04, 0.07)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.05)

.22

.48

.05

.48

.43

.53

.18

.33

.08

.4

.46

.43

.2

.42

.18

.22

.42

.18

.2

.42

.18

.19

.21

.47

.22

.33

.21

.47

.22

.32

.32

.27

3.8e-08

4.1e-12

5.2e-05

2.3e-128.3e-15

3.7e-17

9.7e-11

1.2e-12

7.9e-08

3.0e-12

1.5e-12

8.7e-15

1.3e-07

5.0e-15

1.0e-09

3.8e-07

3.4e-15

1.3e-10

1.2e-04

4.8e-15

8.5e-11

5.0e-08

6.7e-07

4.7e-14

1.6e-08

5.5e-13

1.5e-11

4.8e-14

2.7e-07

7.5e-14

1.1e-13

2.4e-13

-0.04 (-0.06, -0.03)

0.04 (0.03, 0.06)

-0.07 (-0.10, -0.04)

0.05 (0.03, 0.06)0.06 (0.04, 0.07)

-0.06 (-0.08, -0.05)

-0.06 (-0.08, -0.04)

0.05 (0.04, 0.06)

-0.06 (-0.09, -0.04)

-0.05 (-0.06, -0.03)

0.05 (0.03, 0.06)

0.06 (0.04, 0.07)

-0.04 (-0.06, -0.03)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.04)

-0.04 (-0.05, -0.02)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.04)

-0.04 (-0.05, -0.02)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.04)

-0.04 (-0.06, -0.03)

-0.04 (-0.05, -0.02)

0.05 (0.04, 0.06)

-0.04 (-0.06, -0.03)

0.05 (0.04, 0.06)

-0.06 (-0.08, -0.04)

0.05 (0.04, 0.06)

-0.04 (-0.05, -0.02)

0.06 (0.04, 0.07)

0.06 (0.04, 0.07)

-0.06 (-0.08, -0.05)

.22

.48

.05

.48

.43

.53

.18

.33

.08

.40

.46

.43

.20

.42

.18

.22

.42

.18

.20

.42

.18

.19

.21

.47

.22

.33

.21

.47

.22

.32

.32

.27

0-.1 -.05 0 .05 .1

Minor allele

Number of participants P-value

for associationMean difference(95% CI)

MAFSNPGene

Chr

PROMIS

LURIC

mmol/l

P-value for difference between

studies

0.48

0.69

0.25

0.76

0.55

0.63

0.63

0.16

0.11

0.26

0.36

0.11

0.04

0.45

0.25

1

))))))) .222222227...

)))

6, -0.000000000000000000003)3)3)3)33)3)3))3)3)3)3)3)3)3)33)3)6, -0.0000000000000003)3)3)3)333)3))3)3)33)33)33)))8, -0.000000000000000000005)5)5))5)5)5))5)5)5)55)5)5)5)5)5)5)5)5))55)558, -0.000000000000005)55))55)5))5)55)5)5)5)5)5))5)5))55)58 0 05)0 0 )

))) .2222.222222222222222222222222222.22222222222222222.27.27.277..27227272727.27772.27.27.27.27.27.27.27.272.27.27.27.27.2727.27.27.27.27277272.2727.272.272.27.272.272727.27272.2727722

-0 04 (-0 05 -0 02)-0 04 (-0 05 -0 02) 2222

0 00 ( )

-0. ( )-0. ((( )

- ( - )

.46.

.2

.18

.4242

22

0.00 00.00.00 05 (5 (5 (5 (5 (0.00.0000 03, 3, 3, 3, 3, 0.0000 6)6)6)6)6)0.00 00.00.00 05 (5 ((5 (5 (0.00.0000 03,3,3,3,3, 0.0000 6)6)6)6)6)( )( )

((( 3)-0.0.0.00.04 04 0404 04 (-0(((( .060606, --, -, --0.03)-0.040000 (-0.0666, -, -, --0.03)( 4)-0.0.0.0.06 0606 06 06 (-0(-0(-0((-0.08.08.08.08.08, -, -, -, -, 0.00.00.00.00.04)4444-0.0.0.0.0606060606 (-0(-0(-0(( 0.0808080808, -, -, -, 0.00.00.00.00.04)4444

( 7)0.00.00.00.00.066 (66 0.00. 4, 0.00.000077)770.00.00.00.00 66 (6 0.00. 4, 0.00.00077)770 000 066 (0 00 4 0 00 0077)000 066 (0 00 4 00 077)( )( )( )( )

00-0 040404 ( 0( 0(-0 050505 -0 00 00 02)2)2)00-0 040404 ( 0( 0(-0 050505 -0 00 00 02)2)2)

.46.46.46.4646.46.46.46.46.46.46.4646.4646

.20.2.2

.18.18.18

.42.42222.42.4242422.42.42222

222222222222



2

11

2

2

11

11

11

11

rs1260326

rs662799

APOA5

GCKR

GCKR

rs780094

rs780093

rs2266788APOA5

APOA5

rs2075290

APOA5rs651821

rs2072560

GCKR

ZNF259/APOA5

T

C

T

T

G

G

C

A

5500

3195

2450

2451

2445

3185

3194

31952452

2452

3189

24523195

3195

2449

2452

0.08 (0.06, 0.10)

0.14 (0.11, 0.18)

0.08 (0.03, 0.13)

0.08 (0.05, 0.10)

0.08 (0.05, 0.11)

0.07 (0.04, 0.11)

0.07 (0.04, 0.11)

0.13 (0.10, 0.16)0.07 (0.02, 0.12)

0.08 (0.03, 0.13)

0.13 (0.10, 0.17)

0.08 (0.03, 0.13)0.14 (0.11, 0.18)

0.14 (0.11, 0.18)

0.08 (0.05, 0.11)

0.08 (0.03, 0.13)

.26

.17

.07

.44

.44

.26

.26

.2

.07

.07

.19

.07

.17

.16

.44

.07

1.1e-10

1.2e-14

3.3e-03

7.2e-09

3.4e-09

2.6e-06

2.3e-06

6.9e-143.8e-03

2.2e-03

8.8e-14

1.5e-031.5e-14

2.1e-14

3.9e-09

2.2e-03

0.08 (0.06, 0.10)

0.14 (0.11, 0.18)

0.08 (0.03, 0.13)

0.08 (0.05, 0.10)

0.08 (0.05, 0.11)

0.07 (0.04, 0.11)

0.07 (0.04, 0.11)

0.13 (0.10, 0.16)0.07 (0.02, 0.12)

0.08 (0.03, 0.13)

0.13 (0.10, 0.17)

0.08 (0.03, 0.13)0.14 (0.11, 0.18)

0.14 (0.11, 0.18)

0.08 (0.05, 0.11)

0.08 (0.03, 0.13)

.26

.17

.07

.44

.44

.26

.26

.20

.07

.07

.19

.07

.17

.16

.44

.07

0-.05 .05 .1 .15 .2

Minor allele



MAFSNPGene

Chr

PROMIS

LURIC

log mmol/l

0.07

0.69

0.11

0.15

0.17

0.86

0.64

0.19

P-value for difference

between studies

)))))))

07..

161616161616616

0.12)2, ,,,,, 0.12)222,,,,,, 0.12)22

)))0 118)8)8)8)8)8)8)8)8)8)8)))8)8)88)))0 118)88)8)8)8))8)8)8)8888))

.07...07......07....

616161616161616161616166161666166616.)( , )( , ) 6.16.16

0 10 10.1444 (4 0 10 10 11111 0 10 10 10 8)8)8

0.0 ( 0.13)

0.0008 ((((00 000 3 0 13)3)3)3)0.1 (( )

1.17

.07

.07

.17

.

(( )8))0 10 10.100 4 (4 (4 (44 0.10 10 10 10 11, 1111 0.10 10 100 8)8)8)80 10 10.100 444 (4 0.10 10 10 10 11,111 0.10 10 100 8)8)8)8

0.0 (( 0.13)0.08 (((((0.000.00 00 033, 333 0.13)0.08 (((((0.00033,33 0.13)

( 3)0.00.00.00.00. 8 ((((00.000 3, 33 0.1000 3)3)3)3)0.000.0.00 8 (((0.03,33 0.10 3)3)3)( 8)0.10.100.10.14 (4 (4 (4 4 0.0.10.0.0. 1, 1,1,1,1, 0.10.10.1.1.18)80.10.1000 4 (0.0.1.. ,,1,,, 0.10.10.1.1.18)

( , )( , )( , )

.171.1711.171

.07.07.07

.07.07.07

.171717.171717717.171771

.16.16.16



(a)

(b)

(c)



0.07 (0.07, 0.08)

0.05 (0.03, 0.06)

0.06 (0.05, 0.07)

0.05 (0.04, 0.06)

0.07 (0.07, 0.08)

0.05 (0.03, 0.06)

0.06 (0.05, 0.07)

0.05 (0.04, 0.06)

00 .02 .04 .06 .08 .1

-0.15 (-0.17, -0.14)

-0.16 (-0.21, -0.11)

-0.15 (-0.17, -0.14)

-0.16 (-0.21, -0.11)

0-.3 -.2 -.1 0

0.20 (0.14, 0.26)

0.39 (0.30, 0.48)

0.20 (0.14, 0.26)

0.39 (0.30, 0.48)

0-.2 -.1 0 .1 .2 .3

a) High density lipoprotein (mmol/l)

rs646776

CELSR2

rs1800775

CETP

rs708272

CETP

rs662799

ApoA5

Europeans

PROMIS

Europeans

PROMIS

Europeans

PROMIS

Europeans

PROMIS

48,445 (14)

5576

80,184 (26)

3024

65,640 (46)

3023

20,963 (16)

3195

SNPGene

N of participants (N of studies)Data source

c) Triglycerides (mmol/l)

b) Low density lipoprotein (mmol/l)

4e-11

3e-17

1e-93

7e-10

P-valueMean difference(95% CI)

3e-12

5e-14

2e-53

3e-217

7e-4

0.84

P -valueheterogeneity

0.06

2e-4

0000000000000000

000000 .02 .0.0.0.0.04 .06 .08 .1



1

"SUPPLEMENTAL MATERIAL."



2

Supplemental Figure 1: Scatter plot of additional principal components and self reported ethnicities in PROMIS control participants

at INSE

RM

- DISC

on July 18, 2010 circgenetics.ahajournals.org

Dow

nloaded from


3

PROMIS LURIC P‐value for difference between studies

Chr snp bp gene a1 N maf beta se p N maf beta se p

Association with HDL‐C levels (mmol/l)

16 rs711752 55553712 CETP T 3023 0.47 0.048 0.006 4.67E‐14 2451 0.42 0.058 0.007 5.05E‐15 0.45

16 rs708272 55553789 CETP A 3023 0.47 0.048 0.006 4.77E‐14 2450 0.42 0.058 0.007 4.77E‐15 0.76

16 rs17231506 55552029 CETP A 3023 0.33 0.049 0.007 5.54E‐13 2451 0.32 0.059 0.008 7.50E‐14 1

16 rs3764261 55550825 CETP A 3023 0.33 0.049 0.007 1.17E‐12 2451 0.32 0.058 0.008 1.08E‐13 0.63

16 rs11508026 55556829 CETP A 3021 0.46 0.046 0.006 1.47E‐12 2452 0.42 0.058 0.007 3.40E‐15 0.55

16 rs1532625 55562802 CETP T 2996 0.48 0.045 0.006 2.27E‐12 2428 0.43 0.058 0.007 8.28E‐15 0.63

16 rs1800775 55552737 CETP G 3024 0.40 ‐0.045 0.006 3.00E‐12 2451 0.53 ‐0.062 0.007 3.69E‐17 0.11

16 rs1532624 55562980 CETP T 3023 0.48 0.044 0.006 4.06E‐12 2448 0.43 0.057 0.007 8.69E‐15 0.25

16 rs1864163 55554734 CETP A 3024 0.22 ‐0.043 0.008 1.56E‐08 2443 0.27 ‐0.063 0.009 2.40E‐13 0.16

16 rs7499892 55564091 CETP A 3023 0.22 ‐0.042 0.008 3.82E‐08 2452 0.18 ‐0.063 0.010 8.52E‐11 0.11

16 rs11076175 55563879 CETP G 3022 0.19 ‐0.044 0.008 5.04E‐08 2451 0.18 ‐0.063 0.010 1.27E‐10 0.36

16 rs5880 55572592 CETP G 3024 0.08 ‐0.064 0.012 7.86E‐08 2451 0.05 ‐0.068 0.017 5.25E‐05 0.69

16 rs12720922 55558386 CETP T 3024 0.20 ‐0.042 0.008 1.29E‐07 2451 0.18 ‐0.060 0.010 1.02E‐09 0.48

16 rs9939224 55560233 CETP A 3021 0.22 ‐0.039 0.008 2.74E‐07 2452 0.21 ‐0.062 0.009 1.49E‐11 0.25

16 rs12708967 55550712 CETP G 3021 0.22 ‐0.039 0.008 3.85E‐07 2452 0.20 ‐0.036 0.009 1.16E‐04 0.26

16 rs11076176 55564947 CETP C 3021 0.21 ‐0.038 0.008 6.71E‐07 2450 0.18 ‐0.063 0.010 9.67E‐11 0.04

Association with LDL‐C levels (mmol/l)

1 rs646776++ 109620053 CELSR2 G 5576 0.25 ‐0.158 0.025 7.19E‐10 1175 0.239 ‐0.014 0.040 0.7224 0.05

Association with log‐triglyceride levels (mmol/l)

11 rs662799 116168917 APOA5 C 3195 0.17 0.142 0.018 1.25E‐14 2452 0.07 0.080 0.026 2.22E‐03 0.07

11 rs651821 116167789 APOA5 C 3195 0.17 0.142 0.018 1.47E‐14 2452 0.07 0.083 0.026 1.49E‐03 0.15

11 rs2072560 116167036 APOA5 A 3195 0.16 0.142 0.019 2.13E‐14 2450 0.07 0.077 0.026 3.34E‐03 0.11

11 rs2266788 116165896 APOA5 G 3195 0.20 0.129 0.017 6.94E‐14 2452 0.07 0.073 0.025 3.83E‐03 0.17

11 rs2075290 116158506 ZNF259/APOA5 G 3189 0.19 0.132 0.018 8.77E‐14 2452 0.07 0.077 0.025 2.15E‐03 0.19

2 rs1260326++ 27584444 GCKR T 5500 0.26 0.078 0.012 1.09E‐10 2451 0.44 0.078 0.014 7.19E‐09 0.86

2 rs780093 27596107 GCKR T 3194 0.26 0.075 0.016 2.35E‐06 2445 0.44 0.080 0.014 3.41E‐09 0.64

2 rs780094 27594741 GCKR T 3185 0.26 0.074 0.016 2.63E‐06 2449 0.44 0.080 0.014 3.86E‐09 0.69

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)

++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.

at INSE

RM

- DISC


Dow

nloaded from


4

rs1107617516

rs1107617616

rs1150802616

rs1270896716

rs1272092216

rs153262416

rs153262516

rs1723150616

rs180077516

rs186416316

rs376426116

rs588016

rs70827216

rs71175216

rs749989216

rs993922416

SNP_id/Chr.

Status

-0.05 (-0.07, -0.02)-0.08 (-0.11, -0.05)

-0.04 (-0.06, -0.01)-0.06 (-0.08, -0.03)

0.05 (0.03, 0.07)0.06 (0.04, 0.09)

-0.06 (-0.08, -0.03)-0.06 (-0.09, -0.03)

-0.05 (-0.07, -0.02)-0.08 (-0.11, -0.05)

0.05 (0.03, 0.07)0.06 (0.04, 0.09)

0.05 (0.03, 0.08)0.07 (0.04, 0.09)

0.06 (0.04, 0.09)0.07 (0.05, 0.10)

-0.04 (-0.06, -0.02)-0.07 (-0.10, -0.05)

-0.05 (-0.07, -0.02)-0.07 (-0.10, -0.04)

0.06 (0.04, 0.09)0.07 (0.05, 0.10)

-0.08 (-0.12, -0.04)-0.07 (-0.11, -0.02)

0.05 (0.03, 0.08)0.07 (0.05, 0.09)

0.06 (0.03, 0.08)0.07 (0.05, 0.09)

-0.05 (-0.07, -0.02)-0.07 (-0.10, -0.04)

-0.04 (-0.07, -0.02)-0.07 (-0.10, -0.05)

Mean difference (95% CI)

.128

.288

.554

.869

.118

.491

.502

.609

.049

.196

.57

.609

.327

.362

.327

.119

P-value het.

-0.05 (-0.07, -0.02)-0.08 (-0.11, -0.05)

-0.04 (-0.06, -0.01)-0.06 (-0.08, -0.03)

0.05 (0.03, 0.07)0.06 (0.04, 0.09)

-0.06 (-0.08, -0.03)-0.06 (-0.09, -0.03)

-0.05 (-0.07, -0.02)-0.08 (-0.11, -0.05)

0.05 (0.03, 0.07)0.06 (0.04, 0.09)

0.05 (0.03, 0.08)0.07 (0.04, 0.09)

0.06 (0.04, 0.09)0.07 (0.05, 0.10)

-0.04 (-0.06, -0.02)-0.07 (-0.10, -0.05)

-0.05 (-0.07, -0.02)-0.07 (-0.10, -0.04)

0.06 (0.04, 0.09)0.07 (0.05, 0.10)

-0.08 (-0.12, -0.04)-0.07 (-0.11, -0.02)

0.05 (0.03, 0.08)0.07 (0.05, 0.09)

0.06 (0.03, 0.08)0.07 (0.05, 0.09)

-0.05 (-0.07, -0.02)-0.07 (-0.10, -0.04)

-0.04 (-0.07, -0.02)-0.07 (-0.10, -0.05)

.128

.288

.554

.869

.118

.491

.502

.609

.049

.196

.570

.609

.327

.362

.327

.119

0-.1 -.05 0 .05 .1

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

mmol/l

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)



5

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)

rs12603262

rs207256011

rs207529011

rs226678811

rs65182111

rs66279911

rs7800932

rs7800942

SNP_id/chr

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

Status

0.08 (0.04, 0.12)0.08 (0.03, 0.12)

0.15 (0.10, 0.20)0.13 (0.08, 0.18)

0.14 (0.09, 0.19)0.12 (0.07, 0.17)

0.13 (0.08, 0.18)0.13 (0.08, 0.18)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)

Mean Difference(95% CI)

.908

.608

.561

.972

.732

.734

.909

.838

P-value het.

0.08 (0.04, 0.12)0.08 (0.03, 0.12)

0.15 (0.10, 0.20)0.13 (0.08, 0.18)

0.14 (0.09, 0.19)0.12 (0.07, 0.17)

0.13 (0.08, 0.18)0.13 (0.08, 0.18)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)

.908

.608

.561

.972

.732

.734

.909

.838

0 .050 .05 .1 .15 .2

log mmol/l

rs12603262

rs207256011

rs207529011

rs226678811

rs65182111

rs66279911

rs7800932

rs7800942

SNP_id/chr

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

casecontrol

Status

0.08 (0.04, 0.12)0.08 (0.03, 0.12)

0.15 (0.10, 0.20)0.13 (0.08, 0.18)

0.14 (0.09, 0.19)0.12 (0.07, 0.17)

0.13 (0.08, 0.18)0.13 (0.08, 0.18)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)


.908

.608

.561

.972

.732

.734

.909

.838

P-value het.

0.08 (0.04, 0.12)0.08 (0.03, 0.12)

0.15 (0.10, 0.20)0.13 (0.08, 0.18)

0.14 (0.09, 0.19)0.12 (0.07, 0.17)

0.13 (0.08, 0.18)0.13 (0.08, 0.18)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.15 (0.10, 0.20)0.13 (0.08, 0.19)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)

0.07 (0.03, 0.11)0.08 (0.03, 0.12)

.908

.608

.561

.972

.732

.734

.909

.838

0 .050 .05 .1 .15 .2

log mmol/l



6

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

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.

rs6467761

chr

case

control

Status

-0.05 (-0.09, -0.02)

-0.07 (-0.11, -0.03)


.584

P_value het.

-0.05 (-0.09, -0.02)

-0.07 (-0.11, -0.03)

.584

0-.1 -.05 0 .05

SNP_id/

rs6467761

chr

case

control

Status

-0.05 (-0.09, -0.02)

-0.07 (-0.11, -0.03)


.584

P_value het.

-0.05 (-0.09, -0.02)

-0.07 (-0.11, -0.03)

.584

0-.1 -.05 0 .05

SNP_id/

mmol/l



7

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

% Weight

NOTE: Weights are from random effects analysis

Overall random effect I-squared = 10% (95% CI 0% - 43%), p = 0.318)

Blankenberg S (AtheroGene)

Thompson JF

Ridker (WGHS)

Eiriksdottir Reykjavik)

Thompson JF

Overall fixed effect

Dullaart (PREVEND)

Schouw (PROSPECT/EPIC)

Bernstein MS

Sabatti (NFBC1966)

Kathiresan (NORDIL)

Girelli (Verona Heart Project)

Kathiresan (FIINRISK97)

Kathiresan (MDC-CC)

Kakko (OPERA)

Tobin MD

Aulchenko (ENGAGE consortium)

Bauerfeind

Freeman DJ (WOSCOPS)

Kathiresan (DGI)

Horne (IHCS)

Dachet C (ECTIM)

Author (Name of Study)

Heidema (CDRFMP)

Chasman (WGHS)

Barzilai N (Longevity)

McCaskie (CUDAS/BPHS/CUPID)

Rotterdam study

2004

2007

2009

2001

2003

2007

2003

2009

2008

2008

2008

2001

2004

2009

2002

2003

2008

2007

1999

Year

2007

2008

2003

2007

2007

574

2087

18000

745

93

8141

1519

1720

4531

5095

1187

7940

5519

481

182

5840

185

1107

2758

1309

668

Number ofparticipants

1071

6195

743

1059

1435

0.07 (0.07, 0.08)

0.08 (0.03, 0.13)

0.05 (0.03, 0.07)

0.08 (0.07, 0.09)

0.08 (0.05, 0.11)

0.08 (-0.00, 0.15)

0.07 (0.07, 0.08)

0.06 (0.05, 0.08)

0.08 (0.05, 0.11)

0.05 (0.02, 0.09)

0.07 (0.05, 0.09)

0.08 (0.05, 0.10)

0.07 (0.04, 0.10)

0.06 (0.05, 0.08)

0.08 (0.07, 0.09)

0.05 (0.01, 0.09)

0.06 (-0.01, 0.13)

0.06 (0.05, 0.08)

0.07 (-0.01, 0.15)

0.06 (0.04, 0.08)

0.07 (0.05, 0.09)

0.05 (-0.00, 0.11)

0.08 (0.04, 0.12)

ES (95% CI)

0.07 (0.04, 0.10)

0.09 (0.08, 0.10)

0.07 (0.03, 0.11)

0.07 (0.04, 0.11)

0.08 (0.06, 0.11)

100.00

0.84

5.44

14.06

2.38

0.32

9.87

2.32

1.50

3.79

3.88

2.24

7.79

7.79

1.17

0.38

9.33

0.32

4.20

4.43

0.59

1.11

(D+L)

2.14

8.28

1.17

1.73

2.94

0.07 (0.07, 0.08)

0.08 (0.03, 0.13)

0.05 (0.03, 0.07)

0.08 (0.07, 0.09)

0.08 (0.05, 0.11)

0.08 (-0.00, 0.15)

0.07 (0.07, 0.08)

0.06 (0.05, 0.08)

0.08 (0.05, 0.11)

0.05 (0.02, 0.09)

0.07 (0.05, 0.09)

0.08 (0.05, 0.10)

0.07 (0.04, 0.10)

0.06 (0.05, 0.08)

0.08 (0.07, 0.09)

0.05 (0.01, 0.09)

0.06 (-0.01, 0.13)

0.06 (0.05, 0.08)

0.07 (-0.01, 0.15)

0.06 (0.04, 0.08)

0.07 (0.05, 0.09)

0.05 (-0.00, 0.11)

0.08 (0.04, 0.12)

ES (95% CI)

0.07 (0.04, 0.10)

0.09 (0.08, 0.10)

0.07 (0.03, 0.11)

0.07 (0.04, 0.11)

0.08 (0.06, 0.11)

100.00

0.84

5.44

14.06

2.38

0.32

9.87

2.32

1.50

3.79

3.88

2.24

7.79

7.79

1.17

0.38

9.33

0.32

4.20

4.43

0.59

1.11

(D+L)

2.14

8.28

1.17

1.73

2.94

0-.153 0 .153

Effect Size



8

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

% Weight


Overall Random Effect I-squared = 67.0% (95% CI 55% - 76%), p = <0.001)

Mitchell

Pai J (NHS)

Gudson (EARS)

Corella D

Eiriksdottir (Reykjavik)

Klos K (CARDIA)


Noone EOrdovas (Framingham)

Schouw (PROSPECT-EPIC)Sorli

Hall

Cuchel (NORM & CATH)

Horne (IHCS)

Miltiadous

Ridker (WGHS)

I-V Overall

Plat

Vohl MC


Dullaart (PREVEND)

Liu (PHS)Kuivenhoven JA (The Monitoring Project)

Riemens

Talmud (NPHS)

Deguchi (SVTR)

Freeman DJ

Kondon I

Tenkanen H

Heidema (CDRFMP)

Juvonen T

Thompson JF

Blankenberg S (Atherogene)

Thompson JF

Hannuksela


Kauma H

Pai J (HPFS)

Nettleton (ARIC)

Keavney

Carr


Fumeron F (ECTIM)

Bauerfeind


Sandhofer (Salzburg Atherosclerosis Prevention)

Arca

Weitgasser (SAPHIR)

1994

2004

1998

2000

2001

2007

20002007

2006

2006

2002

2007

2004

2009

2002

1999

2003

2007

20021997

1999

2002

2004

1994

1989

1991

2007

1995

2007

2004

2003

1994

2003

1996

2004

2006

2004

2002

2007

1995

2002

Year

2008

2001

2004

112

480

767

514

745

1586

296

632916

1399549

116

224

1298

95

18245

112

182

373

8289

384238

32

1727

49

220

146

109

1075

91

2105

571

93

82

1105

524

513

8764

4665

120

1058

724

184

Number ofParticipants

1503

180

1017

0.06 (0.05, 0.07)

0.12 (0.02, 0.21)

0.11 (0.06, 0.17)

0.07 (0.05, 0.09)

0.11 (0.07, 0.14)

0.07 (0.03, 0.10)

0.06 (0.04, 0.08)

0.02 (-0.03, 0.07)

0.02 (-0.12, 0.15)0.06 (0.04, 0.08)

0.06 (0.03, 0.09)0.05 (0.01, 0.09)

0.08 (-0.02, 0.18)

0.06 (-0.02, 0.13)

0.05 (-0.01, 0.11)

0.03 (-0.05, 0.11)

0.07 (0.07, 0.08)

0.07 (0.07, 0.07)

0.06 (-0.03, 0.15)

0.06 (0.02, 0.09)

0.12 (0.05, 0.18)

0.06 (0.05, 0.08)

0.05 (0.01, 0.09)0.12 (0.05, 0.18)

-0.00 (-0.11, 0.11)

0.05 (0.04, 0.07)

0.02 (-0.10, 0.15)

0.10 (0.03, 0.17)

0.11 (0.02, 0.20)

0.04 (-0.07, 0.16)

0.07 (0.04, 0.10)

0.20 (0.03, 0.37)

0.05 (0.03, 0.07)

0.08 (0.03, 0.13)

0.07 (-0.02, 0.17)

0.10 (-0.00, 0.20)

0.05 (0.02, 0.07)

0.06 (0.02, 0.10)

-0.11 (-0.15, -0.07)

0.07 (0.06, 0.09)

0.06 (0.04, 0.07)

0.10 (0.01, 0.19)

0.07 (0.03, 0.10)

0.08 (0.04, 0.12)

0.06 (-0.02, 0.14)

0.06 (0.03, 0.09)

0.10 (0.01, 0.18)

0.09 (0.05, 0.12)

100.00

0.66

1.39

3.98

3.04

2.85

3.96

1.67

0.334.34

3.052.53

0.55

0.98

1.38

0.84

5.58

0.66

2.68

1.21

5.00

2.141.26

0.47

4.51

0.40

1.03

0.65

0.46

3.11

0.20

4.41

1.82

0.59

0.54

3.98

2.22

2.29

4.96

4.93

0.69

2.75

2.35

0.86

(D+L)

3.24

0.76

2.72

0.06 (0.05, 0.07)

0.12 (0.02, 0.21)

0.11 (0.06, 0.17)

0.07 (0.05, 0.09)

0.11 (0.07, 0.14)

0.07 (0.03, 0.10)

0.06 (0.04, 0.08)

0.02 (-0.03, 0.07)

0.02 (-0.12, 0.15)0.06 (0.04, 0.08)

0.06 (0.03, 0.09)0.05 (0.01, 0.09)

0.08 (-0.02, 0.18)

0.06 (-0.02, 0.13)

0.05 (-0.01, 0.11)

0.03 (-0.05, 0.11)

0.07 (0.07, 0.08)

0.07 (0.07, 0.07)

0.06 (-0.03, 0.15)

0.06 (0.02, 0.09)

0.12 (0.05, 0.18)

0.06 (0.05, 0.08)

0.05 (0.01, 0.09)0.12 (0.05, 0.18)

-0.00 (-0.11, 0.11)

0.05 (0.04, 0.07)

0.02 (-0.10, 0.15)

0.10 (0.03, 0.17)

0.11 (0.02, 0.20)

0.04 (-0.07, 0.16)

0.07 (0.04, 0.10)

0.20 (0.03, 0.37)

0.05 (0.03, 0.07)

0.08 (0.03, 0.13)

0.07 (-0.02, 0.17)

0.10 (-0.00, 0.20)

0.05 (0.02, 0.07)

0.06 (0.02, 0.10)

-0.11 (-0.15, -0.07)

0.07 (0.06, 0.09)

0.06 (0.04, 0.07)

0.10 (0.01, 0.19)

0.07 (0.03, 0.10)

0.08 (0.04, 0.12)

0.06 (-0.02, 0.14)

ES (95% CI)

0.06 (0.03, 0.09)

0.10 (0.01, 0.18)

0.09 (0.05, 0.12)

100.00

0.66

1.39

3.98

3.04

2.85

3.96

1.67

0.334.34

3.052.53

0.55

0.98

1.38

0.84

5.58

0.66

2.68

1.21

5.00

2.141.26

0.47

4.51

0.40

1.03

0.65

0.46

3.11

0.20

4.41

1.82

0.59

0.54

3.98

2.22

2.29

4.96

4.93

0.69

2.75

2.35

0.86

(D+L)

3.24

0.76

2.72

0-.374 0 .374

% Weight


Overall Random Effect I-squared = 67.0% (95% CI 55% - 76%), p = <0.001)

Mitchell

Pai J (NHS)

Gudson (EARS)

Corella D

Eiriksdottir (Reykjavik)

Klos K (CARDIA)


Noone EOrdovas (Framingham)

Schouw (PROSPECT-EPIC)Sorli

Hall

Cuchel (NORM & CATH)

Horne (IHCS)

Miltiadous

Ridker (WGHS)

I-V Overall

Plat

Vohl MC


Dullaart (PREVEND)

Liu (PHS)Kuivenhoven JA (The Monitoring Project)

Riemens

Talmud (NPHS)

Deguchi (SVTR)

Freeman DJ

Kondon I

Tenkanen H

Heidema (CDRFMP)

Juvonen T

Thompson JF

Blankenberg S (Atherogene)

Thompson JF

Hannuksela


Kauma H

Pai J (HPFS)

Nettleton (ARIC)

Keavney

Carr


Fumeron F (ECTIM)

Bauerfeind


Sandhofer (Salzburg Atherosclerosis Prevention)

Arca

Weitgasser (SAPHIR)

1994

2004

1998

2000

2001

2007

20002007

2006

2006

2002

2007

2004

2009

2002

1999

2003

2007

20021997

1999

2002

2004

1994

1989

1991

2007

1995

2007

2004

2003

1994

2003

1996

2004

2006

2004

2002

2007

1995

2002

Year

2008

2001

2004

112

480

767

514

745

1586

296

632916

1399549

116

224

1298

95

18245

112

182

373

8289

384238

32

1727

49

220

146

109

1075

91

2105

571

93

82

1105

524

513

8764

4665

120

1058

724

184

Number ofParticipants

1503

180

1017

0.06 (0.05, 0.07)

0.12 (0.02, 0.21)

0.11 (0.06, 0.17)

0.07 (0.05, 0.09)

0.11 (0.07, 0.14)

0.07 (0.03, 0.10)

0.06 (0.04, 0.08)

0.02 (-0.03, 0.07)

0.02 (-0.12, 0.15)0.06 (0.04, 0.08)

0.06 (0.03, 0.09)0.05 (0.01, 0.09)

0.08 (-0.02, 0.18)

0.06 (-0.02, 0.13)

0.05 (-0.01, 0.11)

0.03 (-0.05, 0.11)

0.07 (0.07, 0.08)

0.07 (0.07, 0.07)

0.06 (-0.03, 0.15)

0.06 (0.02, 0.09)

0.12 (0.05, 0.18)

0.06 (0.05, 0.08)

0.05 (0.01, 0.09)0.12 (0.05, 0.18)

-0.00 (-0.11, 0.11)

0.05 (0.04, 0.07)

0.02 (-0.10, 0.15)

0.10 (0.03, 0.17)

0.11 (0.02, 0.20)

0.04 (-0.07, 0.16)

0.07 (0.04, 0.10)

0.20 (0.03, 0.37)

0.05 (0.03, 0.07)

0.08 (0.03, 0.13)

0.07 (-0.02, 0.17)

0.10 (-0.00, 0.20)

0.05 (0.02, 0.07)

0.06 (0.02, 0.10)

-0.11 (-0.15, -0.07)

0.07 (0.06, 0.09)

0.06 (0.04, 0.07)

0.10 (0.01, 0.19)

0.07 (0.03, 0.10)

0.08 (0.04, 0.12)

0.06 (-0.02, 0.14)

0.06 (0.03, 0.09)

0.10 (0.01, 0.18)

0.09 (0.05, 0.12)

100.00

0.66

1.39

3.98

3.04

2.85

3.96

1.67

0.334.34

3.052.53

0.55

0.98

1.38

0.84

5.58

0.66

2.68

1.21

5.00

2.141.26

0.47

4.51

0.40

1.03

0.65

0.46

3.11

0.20

4.41

1.82

0.59

0.54

3.98

2.22

2.29

4.96

4.93

0.69

2.75

2.35

0.86

(D+L)

3.24

0.76

2.72

0.06 (0.05, 0.07)

0.12 (0.02, 0.21)

0.11 (0.06, 0.17)

0.07 (0.05, 0.09)

0.11 (0.07, 0.14)

0.07 (0.03, 0.10)

0.06 (0.04, 0.08)

0.02 (-0.03, 0.07)

0.02 (-0.12, 0.15)0.06 (0.04, 0.08)

0.06 (0.03, 0.09)0.05 (0.01, 0.09)

0.08 (-0.02, 0.18)

0.06 (-0.02, 0.13)

0.05 (-0.01, 0.11)

0.03 (-0.05, 0.11)

0.07 (0.07, 0.08)

0.07 (0.07, 0.07)

0.06 (-0.03, 0.15)

0.06 (0.02, 0.09)

0.12 (0.05, 0.18)

0.06 (0.05, 0.08)

0.05 (0.01, 0.09)0.12 (0.05, 0.18)

-0.00 (-0.11, 0.11)

0.05 (0.04, 0.07)

0.02 (-0.10, 0.15)

0.10 (0.03, 0.17)

0.11 (0.02, 0.20)

0.04 (-0.07, 0.16)

0.07 (0.04, 0.10)

0.20 (0.03, 0.37)

0.05 (0.03, 0.07)

0.08 (0.03, 0.13)

0.07 (-0.02, 0.17)

0.10 (-0.00, 0.20)

0.05 (0.02, 0.07)

0.06 (0.02, 0.10)

-0.11 (-0.15, -0.07)

0.07 (0.06, 0.09)

0.06 (0.04, 0.07)

0.10 (0.01, 0.19)

0.07 (0.03, 0.10)

0.08 (0.04, 0.12)

0.06 (-0.02, 0.14)

ES (95% CI)

0.06 (0.03, 0.09)

0.10 (0.01, 0.18)

0.09 (0.05, 0.12)

100.00

0.66

1.39

3.98

3.04

2.85

3.96

1.67

0.334.34

3.052.53

0.55

0.98

1.38

0.84

5.58

0.66

2.68

1.21

5.00

2.141.26

0.47

4.51

0.40

1.03

0.65

0.46

3.11

0.20

4.41

1.82

0.59

0.54

3.98

2.22

2.29

4.96

4.93

0.69

2.75

2.35

0.86

(D+L)

3.24

0.76

2.72

0-.374 0 .374Effect Size



9

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


12685

1461

3293

781

4507

587

1686

993

2891

2014

5519

1330

2758

7940

-0.15 (-0.17, -0.14)

-0.14 (-0.17, -0.11)

-0.08 (-0.16, -0.00)

-0.18 (-0.24, -0.12)

-0.29 (-0.49, -0.09)

-0.16 (-0.20, -0.11)

-0.19 (-0.31, -0.07)

-0.15 (-0.21, -0.09)

-0.18 (-0.28, -0.08)

-0.20 (-0.30, -0.10)

-0.17 (-0.23, -0.11)

-0.15 (-0.19, -0.11)

-0.13 (-0.21, -0.05)

-0.19 (-0.25, -0.13)

-0.14 (-0.18, -0.10)

100.00

24.53

3.45

6.13

0.55

8.83

1.53

6.13

2.21

2.21

6.13

13.80

3.45

5.74

15.29

-0.15 (-0.17, -0.14)

-0.14 (-0.17, -0.11)

-0.08 (-0.16, -0.00)

-0.18 (-0.24, -0.12)

-0.29 (-0.49, -0.09)

-0.16 (-0.20, -0.11)

-0.19 (-0.31, -0.07)

-0.15 (-0.21, -0.09)

-0.18 (-0.28, -0.08)

-0.20 (-0.30, -0.10)

-0.17 (-0.23, -0.11)

-0.15 (-0.19, -0.11)

-0.13 (-0.21, -0.05)

-0.19 (-0.25, -0.13)

-0.14 (-0.18, -0.10)

100.00

24.53

3.45

6.13

0.55

8.83

1.53

6.13

2.21

2.21

6.13

13.80

3.45

5.74

15.29

0-.3 -.2 -.1 0

Overall random effect

2008

2009

2008

2008

2008

2008

2008

2008

2008

2008

2008

2009

2008

Kathiresan (FINRISK 97)

Sabatti (NFBC1966)

Wallace (Twins UK)

Sandhu (EPIC-Norfolk Obese)

Sandhu (EPIC-Norfolk Replication)

Sandhu (Ely study)

Kathiresan (MDC-CC)

Kathiresan (NHS98 Malaysia)

Sandhu (1958 British Birth Cohort )

Kathiresan (NHS98 India)

Sandhu (EPIC-Norfolk subcohort)

Aulchenko (Meta-analysis 15 studies)

Kathiresan (DGI)

2008Kathiresan (NHS98 China)

ES (95% CI)%WeightES (95% CI)%WeightNo. of participantsYearAuthor (name of study)

-0.15 (-0.17, -0.14)Overall fixed effect

I2 = 0 (95% CI 0%-55%), p = 0.695

Effect Size



10

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


Overall random effect

Hubacek (Male)

Hubacek (Female)

Helgadottir (PennCATH)

Lee (Japanese American Family)

Talmud (NPHSII)

Grallert (KORA & SAPHIR)

Szalai

Martinelli (Verona Heart Project)

Lee

Vaessen (EPIC-Norfolk)

Aouizerat

Farall (PROCARDIS)

Lai (Framingham Offspring)

Klos (CARDIA)

Evans

Vaverkova

1191

1368

476

154

2537

3264

310

913

438

1800

198

2956

1725

3415

1094

267

0.20 (0.14, 0.26)

0.27 (0.05, 0.49)

0.14 (0.03, 0.25)

0.06 (0.00, 0.12)

0.30 (0.04, 0.57)

0.18 (0.06, 0.31)

0.11 (0.01, 0.21)

0.18 (0.01, 0.35)

0.19 (0.04, 0.34)

0.48 (0.04, 0.93)

0.27 (0.15, 0.39)

0.25 (0.05, 0.44)

0.23 (0.12, 0.34)

0.42 (0.25, 0.58)

0.08 (0.03, 0.14)

0.94 (0.39, 1.50)

0.33 (-0.22, 0.88)

100.00

4.55

8.40

10.64

3.62

7.98

9.05

6.25

6.88

1.56

8.18

5.33

8.40

6.23

10.80

1.06

1.07

0.20 (0.14, 0.26)

0.27 (0.05, 0.49)

0.14 (0.03, 0.25)

0.06 (0.00, 0.12)

0.30 (0.04, 0.57)

0.18 (0.06, 0.31)

0.11 (0.01, 0.21)

0.18 (0.01, 0.35)

0.19 (0.04, 0.34)

0.48 (0.04, 0.93)

0.27 (0.15, 0.39)

0.25 (0.05, 0.44)

0.23 (0.12, 0.34)

0.42 (0.25, 0.58)

0.08 (0.03, 0.14)

0.94 (0.39, 1.50)

0.33 (-0.22, 0.88)

100.00

4.55

8.40

10.64

3.62

7.98

9.05

6.25

6.88

1.56

8.18

5.33

8.40

6.23

10.80

1.06

1.07

00 .2 .4 .6 .8 1

ES (95% CI)%WeightES (95% CI)%WeightNo. of participantsYearAuthor (name of study)

2004

2004

2007

2004

2002

2007

2004

2007

2004

2006

2003

2006

2004

2005

2003

2004

0.14 (0.11, 0.17)Overall fixed effect

I2 = 66 (95% CI 43%-80%), p <0.001

Effect Size



11

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:IV16-IV23.

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 Women in Genome-Wide Analysis With Replication. Circ Cardiovasc Genet 2008;1:21-30.

7. Dachet C, Poirier O, Cambien F, Chapman J, Rouis M. New functional promoter polymorphism, CETP/-629, in cholesteryl ester transfer protein (CETP) gene related to CETP mass and high density lipoprotein cholesterol levels: role of Sp1/Sp3 in transcriptional regulation. Arterioscler Thromb Vasc Biol 2000;20:507-15.

8. Dullaart RP, Borggreve SE, Hillege HL, Dallinga-Thie GM. The association of HDL cholesterol concentration with the -629C>A CETP promoter polymorphism is not fully explained by its relationship with plasma cholesteryl ester transfer. Scand. J Clin.Lab Invest 67: 2007.

9. Eiriksdottir G, Bolla MK, Thorsson B, Sigurdsson G, Humphries SE, Gudnason V. The -629C>A polymorphism in the CETP gene does not explain the association of TaqIB polymorphism with risk and age of myocardial infarction in Icelandic men. Atherosclerosis 2001;159:187-92.

10. Freeman DJ, Griffin BA, Holmes AP, Lindsay GM, Gaffney D, Packard CJ, Shepherd J. Regulation of plasma HDL cholesterol and subfraction distribution by genetic and environmental factors. Associations between the TaqI B RFLP in the CETP gene and smoking and obesity. Arterioscler Thromb 1994;14:336-44.

11. Heidema AG, Feskens EJ, Doevendans PA, Ruven HJ, van Houwelingen HC, Mariman EC, Boer JM. Analysis of multiple SNPs in genetic association studies: comparison of three multi-locus methods to prioritize and select SNPs. Genet Epidemiol 2007.



https://www.researchgate.net/publication/12644996_New_Functional_Promoter_Polymorphism_CETP-629_in_Cholesteryl_Ester_Transfer_Protein_CETP_Gene_Related_to_CETP_Mass_and_High_Density_Lipoprotein_Cholesterol_Levels_Role_of_Sp1Sp3_in_Transcriptional_Reg?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=




https://www.researchgate.net/publication/10738165_Single_Nucleotide_Polymorphism_Haplotypes_in_the_Cholesteryl-Ester_Transfer_Protein_CETP_Gene_and_Lipid_Phenotypes?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=



https://www.researchgate.net/publication/10678063_No_Physical_Activity_CETP_1b-629_Interaction_Effects_on_Lipid_Profile?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=

https://www.researchgate.net/publication/10678063_No_Physical_Activity_CETP_1b-629_Interaction_Effects_on_Lipid_Profile?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=

https://www.researchgate.net/publication/26868581_Genetic_Loci_Associated_With_Plasma_Concentration_of_Low-Density_Lipoprotein_Cholesterol_High-Density_Lipoprotein_Cholesterol_Triglycerides_Apolipoprotein_A1_and_Apolipoprotein_B_Among_6382_White_Wome?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=





https://www.researchgate.net/publication/8618683_Genetic_variation_of_the_cholesterol_ester_transfer_protein_gene_and_the_prevalence_of_coronary_artery_disease_The_AtheroGene_case_control_study?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=




https://www.researchgate.net/publication/5987416_The_association_of_HDL_cholesterol_concentration_with_the_-629CA_CETP_promoter_polymorphism_is_not_fully_explained_by_its_relationship_with_plasma_cholesteryl_ester_transfer?el=1_x_8&enrichId=rgreq-4b7399959f1640c434d5681b1d9d7108-XXX&enrichSource=Y292ZXJQYWdlOzQ0Njk1MjkzO0FTOjk5NTcwMzczNjI3OTEwQDE0MDA3NTA4MzUzNzk=



12

12. Horne BD, Camp NJ, Anderson JL, Mower CP, Clarke JL, Kolek MJ, Carlquist JF. Multiple less common genetic variants explain the association of the cholesteryl ester transfer protein gene with coronary artery disease. J Am Coll Cardiol 2007;49:2053-60.

13. Kakko S, Tamminen M, Paivansalo M, Kauma H, Rantala AO, Lilja M, Reunanen A, Kesaniemi YA, Savolainen MJ. Variation at the cholesteryl ester transfer protein gene in relation to plasma high density lipoproteins cholesterol levels and carotid intima-media thickness. Eur J Clin Invest 2001;31:593-602.

14. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, Wahlstrand B, Hedner T, Corella D, Tai ES, Ordovas JM, Berglund G, Vartiainen E, Jousilahti P, Hedblad B, Taskinen MR, Newton-Cheh C, Salomaa V, Peltonen L, Groop L, Altshuler DM, Orho-Melander M. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 2008.

15. McCaskie PA, Beilby JP, Chapman CM, Hung J, McQuillan BM, Thompson PL, Palmer LJ. Cholesteryl ester transfer protein gene haplotypes, plasma high-density lipoprotein levels and the risk of coronary heart disease. Hum Genet 2007;121:401-11.

16. Ridker PM, Pare G, Parker AN, Zee RYL, Miletich JP, Chasman DI. Polymorphism in the CETP Gene Region, HDL Cholesterol, and Risk of Future Myocardial Infarction: Genomewide Analysis Among 18 245 Initially Healthy Women From the Women's Genome Health Study. Circ Cardiovasc Genet 2009;2:26-33.

17. Sabatti C, Service SK, Hartikainen AL, Pouta A, Ripatti S, Brodsky J, Jones CG, Zaitlen NA, Varilo T, Kaakinen M, Sovio U, Ruokonen A, Laitinen J, Jakkula E, Coin L, Hoggart C, Collins A, Turunen H, Gabriel S, Elliot P, McCarthy MI, Daly MJ, Jarvelin MR, Freimer NB, Peltonen L. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet 2009;41:35-46.

18. Thompson JF, Lira ME, Durham LK, Clark RW, Bamberger MJ, Milos PM. Polymorphisms in the CETP gene and association with CETP mass and HDL levels. Atherosclerosis 2003;167:195-204.

19. Thompson JF, Wood LS, Pickering EH, Dechairo B, Hyde CL. High-density genotyping and functional SNP localization in the CETP gene. J Lipid Res 2007;48:434-43.

20. Tobin MD, Braund PS, Burton PR, Thompson JR, Steeds R, Channer K, Cheng S, Lindpaintner K, Samani NJ. Genotypes and haplotypes predisposing to myocardial infarction: a multilocus case-control study. Eur Heart J 2004;25:459-67.

21. Arca M, Montali A, Ombres D, Battiloro E, Campagna F, Ricci G, Verna R. Lack of association of the common TaqIB polymorphism in the cholesteryl ester transfer protein gene with angiographically assessed coronary atherosclerosis. Clin Genet 2001;60:374-80.

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Supplementary references for the SNP rs646776 located in the CELSR2 gene

60. 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, Döring A, Wichmann HE, Smit JH, McCarthy MI, van Duijn CM, Peltonen L; ENGAGE Consortium. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet. 2009;41:47-55

61. Sabatti C, Service SK, Hartikainen AL, Pouta A, Ripatti S, Brodsky J, Jones CG, Zaitlen NA, Varilo

T, Kaakinen M, Sovio U, Ruokonen A, Laitinen J, Jakkula E, Coin L, Hoggart C, Collins A, Turunen H, Gabriel S, Elliot P, McCarthy MI, Daly MJ, Järvelin MR, Freimer NB, Peltonen L. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet. 2009;41:35-46.

62. Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K, Zhao JH, Song K, Yuan X,

Johnson T, Ashford S, Inouye M, Luben R, Sims M, Hadley D, McArdle W, Barter P, Kesäniemi YA, Mahley RW, McPherson R, Grundy SM; Wellcome Trust Case Control Consortium, Bingham SA, Khaw KT, Loos RJ, Waeber G, Barroso I, Strachan DP, Deloukas P, Vollenweider P, Wareham NJ, Mooser V. LDL-cholesterol concentrations: a genome-wide association study. Lancet. 2008 9;371:483-91.



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16

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Supplementary reference for SNP rs662799 (T1131C) located in the ApoA5 gene

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plasma triglycerides in young, healthy African Americans and whites of the CARDIA Study. J Lipid Res. 2005;46:564-71

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Gehringer A, Haak M, Giani G, Kronenberg F, Wichmann HE, Adamski J, Paulweber B, Illig T, Rathmann W. APOA5 variants and metabolic syndrome in Caucasians. J Lipid Res. 2007;48:2614-21

67. Talmud PJ, Hawe E, Martin S, Olivier M, Miller GJ, Rubin EM, Pennacchio LA, Humphries SE.

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68. Vaessen SF, Schaap FG, Kuivenhoven JA, Groen AK, Hutten BA, Boekholdt SM, Hattori H,

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70. Lai CQ, Demissie S, Cupples LA, Zhu Y, Adiconis X, Parnell LD, Corella D, Ordovas JM. Influence

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71. Hubacek JA, Skodova Z, Adamkova V, Lanska V, Poledne R. The influence of APOAV

polymorphisms (T-1131>C and S19>W) on plasma triglyceride levels and risk of myocardial infarction. Clin Genet. 2004;65:126-30

72. Evans D, Buchwald A, Beil FU. The single nucleotide polymorphism -1131T>C in the

apolipoprotein A5 (APOA5) gene is associated with elevated triglycerides in patients with hyperlipidemia. J Mol Med. 2003;81:645-54

73. Martinelli N, Trabetti E, Bassi A, Girelli D, Friso S, Pizzolo F, Sandri M, Malerba G, Pignatti PF,

Corrocher R, Olivieri O. The -1131 T>C and S19W APOA5 gene polymorphisms are associated with high levels of triglycerides and apolipoprotein CIII, but not with coronary artery disease: an angiographic study. Atherosclerosis. 2007;191:409-17

74. Lee KW, Ayyobi AF, Frohlich JJ, Hill JS. APOA5 gene polymorphism modulates levels of

triglyceride, HDL cholesterol and FERHDL but is not a risk factor for coronary artery disease. Atherosclerosis. 2004 Sep;176(1):165-72Lee KW, Ayyobi AF, Frohlich JJ, Hill JS. APOA5 gene



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polymorphism modulates levels of triglyceride, HDL cholesterol and FERHDL but is not a risk factor for coronary artery disease. Atherosclerosis. 2004;176:165-72

75. Szalai C, Keszei M, Duba J, Prohaszka Z, Kozma GT, Csaszar A, Balogh S, Almassy Z, Fust G,

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76. Vaverkova H, Novotn D, Kar sek D, Bud kov. Slav L, Hutyra, Halenka M. Polymorphism T-1131C

(SNP3) of apoAV gene increases triglycerides levels independently of the presence of insulin resistance. Abstract, 74th EAS Congress, 2004.

77. Aouizerat BE, Kulkarni M, Heilbron D, Drown D, Raskin S, Pullinger CR, Malloy MJ, Kane JP.

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78. Lee KW, Ayyobi AF, Frohlich JJ, Hill JS. APOA5 gene polymorphism modulates levels of

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A, Sigurdsson A, Baker A, Palsson A, Masson G, Gudbjartsson DF, Magnusson KP, Andersen K, Levey AI, Backman VM, Matthiasdottir S, Jonsdottir T, Palsson S, Einarsdottir H, Gunnarsdottir S, Gylfason A, Vaccarino V, Hooper WC, Reilly MP, Granger CB, Austin H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR, Thorgeirsson G, Thorsteinsdottir U, Kong A, Stefansson K. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;316:1491-3



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18

Supplemental Figure 4: Effect modification of genetic effects by gender, ghee consumption and tobacco consumption in Pakistanis

Log triglyceridesrs1260326GCKR

Rs271LPL

rs651821APOA5/ZNF259

LDL-CRs646776CELSR2

HDL-Crs711752CETP

Lipid traitSNPGene

Gender

Oil type

Tobacco

Gender

Oil type

Tobacco

Gender

Oil type

Tobacco

Gender

Oil type

Tobacco

Gender

Oil type

Tobacco

malefemale

gheeoilcombination

EverCurrent

malefemale

gheeoilcombination

EverCurrent

malefemale

gheeoilcombination

EverCurrent

malefemale

gheeoilcombination

EverCurrent

malefemale

gheeoilcombination

EverCurrent

Subgroup

2,558 578

4491,925635

1,625 1,426

2,559 578

4501,925635

1,625 1,427

2,557578

4491,924 635

1,624 1,426

2,424 498

4381,769601

1,486 1,357

2,440 505

4411,785603

1,504 1,360

No. of participants

0.03 (-0.04, 0.10)0.09 (-0.05, 0.23)

0.09 (0.05, 0.13)0.05 (-0.08, 0.18)0.05 (-0.07, 0.17)

0.08 (0.04, 0.12)0.07 (-0.03, 0.18)

-0.09 (-0.18, -0.01)-0.08 (-0.26, 0.10)

-0.07 (-0.12, -0.02)-0.12 (-0.28, 0.03)-0.06 (-0.20, 0.08)

-0.10 (-0.15, -0.05)-0.05 (-0.18, 0.07)

0.13 (0.05, 0.21)0.15 (-0.03, 0.32)

0.12 (0.08, 0.17)0.06 (-0.09, 0.22)0.24 (0.10, 0.38)

0.11 (0.06, 0.16)0.16 (0.04, 0.29)

-0.09 (-0.26, 0.08)-0.16 (-0.52, 0.19)

-0.20 (-0.29, -0.11)-0.03 (-0.31, 0.25)-0.14 (-0.40, 0.11)

-0.18 (-0.28, -0.09)-0.13 (-0.36, 0.10)

0.06 (0.04, 0.09)0.05 (-0.01, 0.11)

0.05 (0.03, 0.06)0.06 (0.01, 0.11)0.06 (0.02, 0.11)

0.05 (0.03, 0.07)0.05 (0.01, 0.09)

0.13

0.50

0.78

0.82

0.60

0.27

0.78

0.01

0.16

0.44

0.24

0.46

0.32

0.49

0.71

Interaction p-value

0.03 (-0.04, 0.10)0.09 (-0.05, 0.23)

0.09 (0.05, 0.13)0.05 (-0.08, 0.18)0.05 (-0.07, 0.17)

0.08 (0.04, 0.12)0.07 (-0.03, 0.18)

-0.09 (-0.18, -0.01)-0.08 (-0.26, 0.10)

-0.07 (-0.12, -0.02)-0.12 (-0.28, 0.03)-0.06 (-0.20, 0.08)

-0.10 (-0.15, -0.05)-0.05 (-0.18, 0.07)

0.13 (0.05, 0.21)0.15 (-0.03, 0.32)

0.12 (0.08, 0.17)0.06 (-0.09, 0.22)0.24 (0.10, 0.38)

0.11 (0.06, 0.16)0.16 (0.04, 0.29)

-0.09 (-0.26, 0.08)-0.16 (-0.52, 0.19)

-0.20 (-0.29, -0.11)-0.03 (-0.31, 0.25)-0.14 (-0.40, 0.11)

-0.18 (-0.28, -0.09)-0.13 (-0.36, 0.10)

0.06 (0.04, 0.09)0.05 (-0.01, 0.11)

0.05 (0.03, 0.06)0.06 (0.01, 0.11)0.06 (0.02, 0.11)

0.05 (0.03, 0.07)0.05 (0.01, 0.09)

Change in lipid trait (mmol/L) per copy of the minor allele (95% CI)

0-.516 0 .516

Lipid level (mmol/L) and 95% confidence intervalsAnalyses 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.



19

9

20

1

16

16

16

16

16

19

2

15

16

16

16

8

9

12

16

16

1

15

8

18

CETP

CETP

rs1883025

LPL

rs1800961

rs2144300

rs5882

rs1800775

APOB

rs1800777CETP

rs5880

APOE

HNF4A

rs255052

GALNT2

rs157580

LIPC

rs693

CETP

CETP

LPL

ABCA1

rs1800588

rs7499892

CETP

rs3764261

rs2197089

CETP

LIPC

rs3890182

DPEP2

rs2338104

rs1864163

KCTD10

rs711752

rs4846914

rs261332

CETP

ABCA1

rs328

rs1532624

rs2156552(intergenic)

GALNT2

CETP

A

A

A

C

G

A

G

A

G

T

T

A

A

A

T

C

A

T

A

A

G

T

A

2451

2451

3023

2434

3021

3023

3024

3024

2451

30242452

3024

2449

2451

3024

2450

3015

2452

3024

2443

2452

2451

2451

3024

3023

2448

3023

3001

2452

2451

3024

2449

3023

3024

2451

3023

3022

3022

2451

2451

3024

3023

30202451

2452

2451

0.06 (0.04, 0.07)

-0.07 (-0.10, -0.04)

-0.01 (-0.03, -0.00)

0.02 (0.01, 0.04)

-0.01 (-0.05, 0.03)

0.00 (-0.01, 0.02)

0.02 (0.01, 0.04)

-0.05 (-0.06, -0.03)

-0.02 (-0.03, -0.00)

-0.06 (-0.10, -0.03)-0.07 (-0.11, -0.03)

-0.06 (-0.09, -0.04)

0.00 (-0.01, 0.02)

-0.07 (-0.12, -0.03)

0.03 (0.01, 0.04)

0.02 (0.00, 0.03)

0.01 (-0.00, 0.02)

0.04 (0.02, 0.05)

-0.01 (-0.02, 0.00)

-0.06 (-0.08, -0.05)

0.03 (0.02, 0.05)

0.05 (0.03, 0.07)

-0.03 (-0.05, -0.01)

0.02 (0.01, 0.03)

-0.04 (-0.06, -0.03)

0.06 (0.04, 0.07)

0.05 (0.04, 0.06)

0.00 (-0.01, 0.02)

-0.06 (-0.08, -0.04)

0.04 (0.02, 0.06)

-0.01 (-0.03, 0.01)

0.03 (0.01, 0.05)

0.01 (-0.00, 0.02)

-0.04 (-0.06, -0.03)

0.00 (-0.01, 0.02)

0.05 (0.04, 0.06)

0.00 (-0.01, 0.02)

0.02 (0.01, 0.04)

-0.06 (-0.08, -0.05)

-0.02 (-0.04, -0.01)

0.02 (-0.00, 0.04)

0.04 (0.03, 0.06)

-0.02 (-0.04, -0.00)-0.00 (-0.02, 0.02)

0.02 (0.00, 0.03)

0.06 (0.04, 0.07)

.32

.05

.35

.55

.03

.45

.43

.4

.46

.04

.04

.08

.36

.04

.19

.58

.47

.22

.27

.27

.32

.11

.12

.25

.22

.43

.34

.44

.18

.23

.08

.15

.44

.22

.57

.47

.45

.19

.53

.26

.10

.48

.14

.15

.58

.42

1.1e-13

5.2e-05

3.4e-02

5.4e-03

5.3e-01

5.1e-01

5.4e-04

3.0e-12

4.4e-02

1.8e-042.8e-04

7.9e-08

5.4e-01

8.8e-04

5.4e-04

2.1e-02

1.9e-01

8.7e-05

1.6e-01

2.4e-13

3.8e-05

2.6e-05

5.9e-03

7.3e-03

3.8e-08

8.7e-15

1.2e-12

6.0e-01

8.5e-11

1.2e-05

3.8e-01

6.0e-03

1.8e-01

1.6e-08

5.0e-01

4.7e-14

4.9e-01

5.5e-03

3.7e-17

4.2e-03

5.8e-02

4.1e-12

2.6e-026.9e-01

2.2e-02

5.0e-15

0.06 (0.04, 0.07)

-0.07 (-0.10, -0.04)

-0.01 (-0.03, -0.00)

0.02 (0.01, 0.04)

-0.01 (-0.05, 0.03)

0.00 (-0.01, 0.02)

0.02 (0.01, 0.04)

-0.05 (-0.06, -0.03)

-0.02 (-0.03, -0.00)

-0.06 (-0.10, -0.03)-0.07 (-0.11, -0.03)

-0.06 (-0.09, -0.04)

0.00 (-0.01, 0.02)

-0.07 (-0.12, -0.03)

0.03 (0.01, 0.04)

0.02 (0.00, 0.03)

0.01 (-0.00, 0.02)

0.04 (0.02, 0.05)

-0.01 (-0.02, 0.00)

-0.06 (-0.08, -0.05)

0.03 (0.02, 0.05)

0.05 (0.03, 0.07)

-0.03 (-0.05, -0.01)

0.02 (0.01, 0.03)

-0.04 (-0.06, -0.03)

0.06 (0.04, 0.07)

0.05 (0.04, 0.06)

0.00 (-0.01, 0.02)

-0.06 (-0.08, -0.04)

0.04 (0.02, 0.06)

-0.01 (-0.03, 0.01)

0.03 (0.01, 0.05)

0.01 (-0.00, 0.02)

-0.04 (-0.06, -0.03)

0.00 (-0.01, 0.02)

0.05 (0.04, 0.06)

0.00 (-0.01, 0.02)

0.02 (0.01, 0.04)

-0.06 (-0.08, -0.05)

-0.02 (-0.04, -0.01)

0.02 (-0.00, 0.04)

0.04 (0.03, 0.06)

-0.02 (-0.04, -0.00)-0.00 (-0.02, 0.02)

0.02 (0.00, 0.03)

0.06 (0.04, 0.07)

.32

.35

.55

.45

.43

.40

.46

.36

.19

.58

.47

.22

.27

.27

.32

.11

.12

.25

.22

.43

.34

.44

.18

.23

.15

.44

.22

.57

.47

.45

.19

.53

.26

.48

.14

.15

.58

.42

0-.1 -.05 0 .05 .1

Minor allele

Number of subjects P-value


MAFSNPGene

Chr

PROMISLURIC

0.15

0.10

0.74

0.92

0.02

0.10

0.87

0.19

0.10

0.67

0.52

0.28

0.63

0.30

0.13

0.16

0.08

0.09

0.69

0.09

0.08

0.30

0.35


between studies

genome wide association studies in association with HDL-C levels

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



20

19

19

5

5

1

19

11

11

19

2

19

1

6

2

11

rs16996148

HMGCR

rs2228671

rs12654264

rs3846662

LDLR

FADS2

rs646776

APOE

rs6511720

rs102275

rs1535

HMGCR

rs2075650

rs7575840

rs157580

APOB

CELSR2

FADS1

APOE

LDLR

(intergenic)

rs599839

rs2254287

CILP2

FADS1

CELSR2

COL11A2

rs693

rs174570

T

A

A

T

G

T

C

G

C

A

G

G

C

T

T

3013

1175

3015

3014

3015

1177

1176

3014

1175

3015

3013

3009

1175

3014

3015

3006

1176

1175

1175

1177

1176

1176

3013

3014

1176

1175

1176

1174

3015

3013

-0.03 (-0.13, 0.07)

-0.01 (-0.07, 0.06)

-0.06 (-0.18, 0.06)

-0.03 (-0.09, 0.04)

-0.02 (-0.08, 0.04)

-0.07 (-0.17, 0.03)

-0.04 (-0.13, 0.06)

-0.15 (-0.23, -0.08)

0.01 (-0.06, 0.07)

-0.08 (-0.19, 0.04)

-0.11 (-0.19, -0.03)

-0.10 (-0.18, -0.02)

-0.00 (-0.07, 0.06)

0.06 (-0.03, 0.15)

0.01 (-0.08, 0.09)

0.06 (0.00, 0.12)

0.03 (-0.04, 0.09)

-0.01 (-0.09, 0.06)

-0.04 (-0.11, 0.03)

0.07 (-0.02, 0.17)

-0.11 (-0.20, -0.01)

0.06 (-0.01, 0.13)

-0.16 (-0.23, -0.08)

0.05 (-0.01, 0.11)

-0.07 (-0.18, 0.04)

-0.04 (-0.11, 0.03)

-0.02 (-0.09, 0.06)

-0.01 (-0.07, 0.06)

0.01 (-0.06, 0.07)

-0.16 (-0.27, -0.05)

.11

.63

.07

.42

.41

.13

.13

.25

.35

.08

.20

.18

.58

.11

.16

.47

.45

.24

.30

.14

.13

.25

.49

.10

.31

.24

.58

.27

.08

5.1e-01

8.5e-01

3.5e-01

4.0e-01

5.5e-01

1.6e-01

4.5e-01

3.4e-05

8.5e-01

1.9e-01

4.5e-03

1.4e-02

9.7e-01

2.0e-01

8.6e-01

4.6e-02

4.4e-01

7.2e-01

2.5e-01

1.2e-01

3.7e-02

8.8e-02

2.7e-05

1.3e-01

1.9e-01

2.6e-01

6.7e-01

8.0e-01

8.9e-01

3.5e-03

-0.03 (-0.13, 0.07)

-0.01 (-0.07, 0.06)

-0.06 (-0.18, 0.06)

-0.03 (-0.09, 0.04)

-0.02 (-0.08, 0.04)

-0.07 (-0.17, 0.03)

-0.04 (-0.13, 0.06)

-0.15 (-0.23, -0.08)

0.01 (-0.06, 0.07)

-0.08 (-0.19, 0.04)

-0.11 (-0.19, -0.03)

-0.10 (-0.18, -0.02)

-0.00 (-0.07, 0.06)

0.06 (-0.03, 0.15)

0.01 (-0.08, 0.09)

0.06 (0.00, 0.12)

0.03 (-0.04, 0.09)

-0.01 (-0.09, 0.06)

-0.04 (-0.11, 0.03)

0.07 (-0.02, 0.17)

-0.11 (-0.20, -0.01)

0.06 (-0.01, 0.13)

-0.16 (-0.23, -0.08)

0.05 (-0.01, 0.11)

-0.07 (-0.18, 0.04)

-0.04 (-0.11, 0.03)

-0.02 (-0.09, 0.06)

-0.01 (-0.07, 0.06)

0.01 (-0.06, 0.07)

-0.16 (-0.27, -0.05)

.11

.63

.42

.41

.13

.13

.25

.35

.08

.18

.58

.11

.16

.47

.45

.24

.14

.13

.30

.25

.49

.31

.24

.58

.27

Minor allele



MAFSNPGene

Chr

PROMIS

LURIC

Effect size

0-.2 -.15 -.1 -.05 0 .05 .1 .15

0.04

0.75

0.64

0.03

0.02

0.19

0.89

0.05

0.06

0.91

0.46

0.78

0.51

0.38

0.05

P-value fordifference

between studies

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



21

8

2

11

7

19

8

2

1

2

19

2

7

1

8

11

19

1

(intergenic)

GALNT2

DOCK7

CILP2

rs328

APOE

APOE

(intergenic)

rs780094

rs12286037

LPL

rs17145738

rs16996148

APOB

GCKR

GCKR

rs17321515

rs673548

rs4846914

rs1260326

rs439401

APOBrs693

rs714052

ZNF259

BAZ1B

rs12130333

APOA5

rs2197089

rs662799

rs157580

TBL2

rs1748195

LPL

G

T

T

T

T

C

A

A

T

C

T

G

T

A

C

G

G

2451

2450

2440

2451

3197

2449

2426

2450

3185

3197

2451

3197

3195

2451

2451

2449

3197

3196

3194

3196

3183

24513197

3193

2452

2451

3197

2451

3172

3195

3188

2451

3195

2434

-0.02 (-0.05, 0.01)

-0.03 (-0.05, 0.00)

-0.02 (-0.05, 0.01)

-0.11 (-0.16, -0.06)

-0.08 (-0.13, -0.03)

-0.02 (-0.05, 0.01)

0.04 (0.02, 0.07)

-0.03 (-0.06, -0.00)

0.07 (0.04, 0.11)

0.12 (0.06, 0.19)

-0.12 (-0.17, -0.08)

-0.09 (-0.14, -0.05)

-0.05 (-0.09, -0.01)

-0.04 (-0.07, -0.01)

0.08 (0.05, 0.10)

0.08 (0.05, 0.11)

-0.01 (-0.04, 0.01)

-0.04 (-0.06, -0.01)

-0.02 (-0.04, 0.01)

0.08 (0.05, 0.11)

0.04 (0.01, 0.07)

0.02 (-0.00, 0.05)0.03 (-0.00, 0.06)

-0.08 (-0.12, -0.03)

0.08 (0.03, 0.13)

-0.02 (-0.06, 0.03)

-0.04 (-0.07, -0.00)

0.14 (0.09, 0.19)

-0.02 (-0.05, 0.01)

0.14 (0.11, 0.18)

-0.03 (-0.06, -0.01)

-0.02 (-0.06, 0.03)

-0.06 (-0.08, -0.03)

-0.04 (-0.07, -0.02)

.20

.58

.31

.36

.61

.48

.26

.04

.11

.11

.22

.44

.44

.37

.49

.45

.26

.45

.46

.27

.10

.068

.11

.17

.06

.44

.17

.47

.11

.45

.55

2.5e-01

6.8e-02

1.3e-01

6.5e-06

6.5e-04

1.9e-01

1.9e-03

3.5e-02

2.6e-06

2.8e-04

2.2e-08

7.3e-05

2.5e-02

6.8e-03

7.2e-09

3.9e-09

3.3e-01

5.7e-03

2.4e-01

9.0e-07

4.1e-03

9.1e-028.1e-02

1.0e-03

2.2e-03

4.9e-01

4.6e-02

1.3e-07

1.7e-01

1.2e-14

1.6e-02

4.5e-01

2.6e-05

1.8e-03

-0.02 (-0.05, 0.01)

-0.03 (-0.05, 0.00)

-0.02 (-0.05, 0.01)

-0.11 (-0.16, -0.06)

-0.08 (-0.13, -0.03)

-0.02 (-0.05, 0.01)

0.04 (0.02, 0.07)

-0.03 (-0.06, -0.00)

0.07 (0.04, 0.11)

0.12 (0.06, 0.19)

-0.12 (-0.17, -0.08)

-0.09 (-0.14, -0.05)

-0.05 (-0.09, -0.01)

-0.04 (-0.07, -0.01)

0.08 (0.05, 0.10)

0.08 (0.05, 0.11)

-0.01 (-0.04, 0.01)

-0.04 (-0.06, -0.01)

-0.02 (-0.04, 0.01)

0.08 (0.05, 0.11)

0.04 (0.01, 0.07)

0.02 (-0.00, 0.05)0.03 (-0.00, 0.06)

-0.08 (-0.12, -0.03)

0.08 (0.03, 0.13)

-0.02 (-0.06, 0.03)

-0.04 (-0.07, -0.00)

0.14 (0.09, 0.19)

-0.02 (-0.05, 0.01)

0.14 (0.11, 0.18)

-0.03 (-0.06, -0.01)

-0.02 (-0.06, 0.03)

-0.06 (-0.08, -0.03)

-0.04 (-0.07, -0.02)

.58

.31

.09

.10

.36

.61

.48

.26

.11

.10

.11

.22

.44

.44

.37

.49

.45

.26

.45

.46

.27

.068

.11

.17

.44

.17

.47

.11

.45

.55

0-.15 -.1 -.05 0 .05 .1 .15 .2

Minor allele

Number of participants

P-valuefor association

Mean difference(95% CI)

MAFSNPChr

PROMIS

LURIC

0.81

0.07

0.73

0.07

0.07

0.02

0.68

0.80

0.50

0.12

0.69

0.69

0.39

0.65

0.48

0.86

0.09


between studies

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

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



22

Supplemental Figure 6(a): Comparison of linkage disequilibrium in PROMIS and LURIC participants for genes with nominally significant associations with HDL-C concentration

at INSE

RM

- DISC


Dow

nloaded from


23

Supplemental Figure 6(b): Comparison of linkage disequilibrium in PROMIS and LURIC participants for genes with nominally significant associations with triglyceride concentration

at INSE

RM

- DISC


Dow

nloaded from


24

Supplemental Figure 6(c): Comparison of linkage disequilibrium in PROMIS and LURIC participants for genes with nominally significant associations with LDL-C concentration

at INSE

RM

- DISC


Dow

nloaded from


25

Supplemental Figure 7(a): Association with MI for SNPs associated with high density cholesterol in PROMIS

rs12708967

rs11508026

rs9939224

rs255052

rs17231506

rs1800777rs5882

rs2156552

rs1864163

rs261332

rs12720922

rs11076175

rs3764261

rs5880

rs711752

rs1883025

rs1532625

rs1800775

rs7499892rs11076176

rs1800588

rs708272

rs1532624

G

A

A

A

A

AC

A

A

A

T

G

A

G

T

A

T

G

AC

T

A

T

CETP

CETP

CETP

DPEP2

CETP

CETPCETP

(intergenic)

CETP

LIPC

CETP

CETP

CETP

CETP

CETP

ABCA1

CETP

CETP

CETPCETP

LIPC

CETP

CETP

0.98 (0.86, 1.09)

0.98 (0.88, 1.07)

0.97 (0.86, 1.08)

1.15 (1.03, 1.26)

0.97 (0.88, 1.07)

0.93 (0.69, 1.17)0.97 (0.88, 1.06)

0.94 (0.81, 1.07)

0.97 (0.86, 1.08)

0.84 (0.73, 0.96)

0.97 (0.86, 1.08)

1.00 (0.88, 1.12)

0.97 (0.88, 1.07)

1.00 (0.83, 1.17)

0.99 (0.90, 1.08)

0.92 (0.83, 1.02)

0.99 (0.90, 1.09)

0.97 (0.87, 1.06)

1.01 (0.90, 1.12)1.00 (0.89, 1.11)

0.89 (0.78, 0.99)

0.99 (0.90, 1.08)

0.98 (0.89, 1.07)

.22

.46

.22

.2

.33

.038

.43

.14

.22

.18

.2

.19

.33

.08

.47

.35

.48

.4

.22

.21

.24

.47

.48

.65

.62

.55

.017

.6

.56

.49

.37

.57

.0049

.57

.99

.6

.97

.82

.11

.87

.49

.81

.97

.026

.86

.7

0.98 (0.86, 1.09)

0.98 (0.88, 1.07)

0.97 (0.86, 1.08)

1.15 (1.03, 1.26)

0.97 (0.88, 1.07)

0.93 (0.69, 1.17)0.97 (0.88, 1.06)

0.94 (0.81, 1.07)

0.97 (0.86, 1.08)

0.84 (0.73, 0.96)

0.97 (0.86, 1.08)

1.00 (0.88, 1.12)

0.97 (0.88, 1.07)

1.00 (0.83, 1.17)

0.99 (0.90, 1.08)

0.92 (0.83, 1.02)

0.99 (0.90, 1.09)

0.97 (0.87, 1.06)

1.01 (0.90, 1.12)1.00 (0.89, 1.11)

0.89 (0.78, 0.99)

0.99 (0.90, 1.08)

0.98 (0.89, 1.07)

.22

.46

.22

.2

.33

.038

.43

.14

.22

.18

.2

.19

.33

.08

.47

.35

.48

.4

.22

.21

.24

.47

.48

.8 1 1.2

Chromosome 15

Chromosome 16

Chromosome 18

Chromosome 9

SNP Gene OR (95% CI) P-valueMAFRisk allele

odds ratio



26

Supplemental Figure 7(b): Association with MI for SNPs associated with low density cholesterol in PROMIS

Chromosome 11

rs1535

rs174570

rs102275

rs599839

Chromosome 19

rs646776

rs157580

Chromosome 1

G

T

C

G

G

G

FADS1

FADS2

FADS1

CELSR2

CELSR2

APOE

0.95 (0.83, 1.07)

1.03 (0.87, 1.19)

0.99 (0.88, 1.11)

0.87 (0.76, 0.98)

0.85 (0.75, 0.96)

0.99 (0.90, 1.09)

.17

.085

.2

.24

.24

.46

.42

.72

.93

.011

.0041

.91

0.95 (0.83, 1.07)

1.03 (0.87, 1.19)

0.99 (0.88, 1.11)

0.87 (0.76, 0.98)

0.85 (0.75, 0.96)

0.99 (0.90, 1.09)

.17

.085

.2

.24

.24

.46

.8 1 1.2 1.4


odds ratio



27

Supplemental Figure 7c: Association with MI for SNPs associated with triglycerides in PROMIS

rs12286037

rs1748195

rs10750097

rs157580

Chromosome 11

rs662799

Chromosome 19

rs1260326

rs16996148

Chromosome 7

rs2266788

rs271

rs780093

rs12130333

rs714052

Chromosome 8

Chromosome 2rs673548

rs2072560

Chromosome 1

rs780094

rs17145738

rs2075290

rs439401

rs651821

rs328

T

G

G

G

C

T

T

G

A

T

T

G

A

A

T

T

G

C

C

G

APOA5

DOCK7

APOA5

APOE

ZNF259

GCKR

CILP2

ZNF259

LPL

GCKR

(intergenic)

BAZ1B

APOB

ZNF259

GCKR

TBL2

APOA5

APOE

ZNF259

LPL

0.99 (0.77, 1.22)

1.03 (0.94, 1.12)

0.98 (0.88, 1.07)

0.99 (0.90, 1.09)

0.96 (0.84, 1.08)

1.01 (0.90, 1.11)

0.92 (0.77, 1.07)

0.94 (0.82, 1.05)

0.86 (0.74, 0.99)

1.03 (0.93, 1.14)

1.06 (0.95, 1.18)

0.96 (0.81, 1.12)

0.95 (0.86, 1.04)

0.97 (0.84, 1.09)

1.02 (0.92, 1.13)

0.97 (0.81, 1.13)

0.97 (0.85, 1.09)

0.97 (0.88, 1.06)

0.96 (0.84, 1.08)

0.84 (0.68, 0.99)

.043

.46

.44

.46

.17

.26

.11

.19

.16

.26

.18

.1

.49

.16

.26

.095

.18

.45

.17

.091

.94

.56

.61

.91

.52

.91

.27

.28

.021

.56

.31

.63

.24

.6

.68

.72

.61

.54

.53

.027

0.99 (0.77, 1.22)

1.03 (0.94, 1.12)

0.98 (0.88, 1.07)

0.99 (0.90, 1.09)

0.96 (0.84, 1.08)

1.01 (0.90, 1.11)

0.92 (0.77, 1.07)

0.94 (0.82, 1.05)

0.86 (0.74, 0.99)

1.03 (0.93, 1.14)

1.06 (0.95, 1.18)

0.96 (0.81, 1.12)

0.95 (0.86, 1.04)

0.97 (0.84, 1.09)

1.02 (0.92, 1.13)

0.97 (0.81, 1.13)

0.97 (0.85, 1.09)

0.97 (0.88, 1.06)

0.96 (0.84, 1.08)

0.84 (0.68, 0.99)

.043

.46

.44

.46

.17

.26

.11

.19

.16

.26

.18

.1

.49

.16

.26

.095

.18

.45

.17

.091

.8 1 1.2 1.4


odds ratio



28

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.



Genetic Determinants of Major Blood Lipids in Pakistanis Compared With Europeans

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