Genetic Susceptibility to Lipid Levels and Lipid Change ...circgenetics.ahajournals.org/content/circcvg/early/2015/11/18/CIRC... · of Incident Hyperlipidemia in Chinese Populations

DOI: 10.1161/CIRCGENETICS.115.001096

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Genetic Susceptibility to Lipid Levels and Lipid Change Over Time and Risk

of Incident Hyperlipidemia in Chinese Populations

Running title: Lu et al.; Genetics and incident hyperlipidemia

Xiangfeng Lu, PhD1; Jianfeng Huang, MD2; Zengnan Mo, MD3; Jiang He, PhD4; Laiyuan Wang,

PhD1,5; Xueli Yang, PhD1; Aihua Tan, MD3; Shufeng Chen, PhD1; Jing Chen, MD4; C. Charles

Gu, PhD6; Jichun Chen, MS;1 Ying Li, MD1; Liancheng Zhao, MD1; Hongfan Li, MS1; Yongchen

Hao, PhD1; Jianxin Li, MS1; James E. Hixson, PhD7; Yunzhi Li, PhD1; Min Cheng, MD1; Xiaoli

Liu, PhD8; Jie Cao, MS1; Fangcao Liu, PhD1; Chen Huang, PhD1; Chong Shen, PhD9; Jinjin Shen,

MD10; Ling Yu, MD11; Lihua Xu, MD12; Jianjun Mu, MD13; Xianping Wu, MD14; Xu Ji, MD15;

Dongshuang Guo, MD16; Zhengyuan Zhou, MD17; Zili Yang, MD18; Renping Wang, MD19; Jun

Yang, MD20; Weili Yan, PhD21; Xiaozhong Peng, PhD22; Dongfeng Gu, MD, PhD1

1State Key Laboratory of Cardiovascular Disease, 2Hypertension Division, Fuwai Hospital,

National Center for Cardiovascular Diseases, 22Institute of Basic Medical Sciences, Chinese

Academy of Medical Sciences & Peking Union Medical College, Beijing; 3Center for Genomic

& Personalized Medicine, Medical Scientific Research Center & Department of Occupational

Health & Environmental Health, School of Public Health, Guangxi Medical University, Nanning,

Guangxi, China; 4Department of Epidemiology, Tulane University School of Public Health &

Tropical Medicine, New Orleans, LA; 5National Human Genome Center at Beijing; 8Department

of Cardiology, Anzhen Hospital, Capital Medical University, Beijing, China; 6Division of

Biostatistics, Washington University School of Medicine, St. Louis, MO; 7Department of

Epidemiology, University of Texas School of Public Health, Houston, TX; 9Department of

Gu, PhD ; Jichun Chen, MS; Ying Li, MD ; Liancheng Zhao, MD ; Hongfan Li, MSM ; Yongche

Hao, PhD1; Jianxin Li, MS1; James E. Hixson, PhD7; Yunzhi Li, PhD1; Min Cheneneng,g,g, MDMDMD111;;; XiXiXiaoaoaolll

Liu,u,u, PPPhDhDhD888;;; JiJiJieee CaCaCaooo, MS1; Fangcao Liu, PhD1; Chhheene Huang, PhD1; Chonnng g g Shen, PhD9; Jinjin She

MMDMD10; Ling YYYuuu, MDMDMD1111; ; ; LiLiLihuhuhua a a XuXu, , , MDMDMD1222; Jianananjun MMMu, MDMM 131313;;; XiXianananpipingg WWWu, MDMDM 14414;;; XuXuXu JJJi,, MDMDMD1151 ;

DoDoDongngngshuaaangngng GGGuouoo, MDMDMD16; ZhZhhenenngygygyuauau n ZZZhohohou,u,u, MMMD1117; ; ZiZiilii YYYananang,g,g, MDMDMD18;;; ReReennpnpinnng g g WaWaW ngngng, MDMDMD19;; JuJuJunn

Yang, MD2000;; WeWeWeilililiii YaYaYan,n,n PPPhDhDhD212121; ; ; XiXiXiaoaoaozhzhzhononong g g PePePengngng, PhPhPhDDD222222;; DoDoDongngngfefefengngng GGGuuu, MD, PhD1

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Epidemiology & Biostatistics, School of Public Health, Nanjing Medical University, Nanjing;10Yancheng Municipal Center for Disease Control & Prevention, Yancheng, Jiangsu;

11Department of Cardiology, Fujian Provincial People's Hospital, Fuzhou, Fujian; 12Department

of Cardiology, the Affiliated Hospital of Beihua University, Beihua University, Jilin; 13Department of Cardiology, First Affiliated Hospital of Medical College, Xi'an Jiaotong

University, Xi'an, Shannxi; 14Center for Chronic & Noncommunicable Disease Control &

Prevention, Sichuan Center of Disease Control & Prevention, Chengdu, Sichuan; 15Department

of Internal Medicine, Xinle Red Cross Hospital, Xinle, Hebei; 16Department of Internal Medicine,

Yuxian Renmin Hospital, Yuxian, Shanxi; 17Changshu Center of Disease Control & Prevention,

Changshu; 18Nantong Center of Disease Control & Prevention, Nantong, Jiangsu; 19Department

of Internal Medicine, Shijiazhuang Greatwall Hospital, Shijiazhuang, Hebei; 20Department of

Cardiology, Hanzhong People’s Hospital, Hanzhong, Shannxi; 21Department of Clinical

Epidemiology, Children’s Hospital of Fudan University, Shanghai, China

Correspondence:

Xiangfeng Lu, PhD Dongfeng Gu, MD, PhD

State Key Laboratory of State Key Laboratory of

Cardiovascular Disease, Fuwai Hospital, Cardiovascular Disease, Fuwai Hospital,

National Center for Cardiovascular Diseases, National Center for Cardiovascular Diseases,

Chinese Academy of Medical Sciences & Chinese Academy of Medical Sciences &

Peking Union Medical College, Peking Union Medical College,

Beijing, 100037, China Beijing, 100037, China

Tel: (8610) 60866599 Tel: (8610) 68331752

Fax: (8610) 88363812 Fax: (8610) 88363812

E-mail: [email protected] E-mail: [email protected]

Journal Subject Terms: Lipids and Cholesterol; Genetic, Association Studies; Genetics

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Epidemiology, Children’s Hospital of Fudan University, Shanghai, CChChinininayy

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Abstract:

Background - Multiple genetic loci associated with lipid levels have been identified

predominantly in Europeans, and the issue of to what extent these genetic loci can predict blood

lipid levels increase over time and the incidence of future hyperlipidemia remains largely

unknown.

Methods and Results - We conducted a meta-analysis of genome-wide association studies of

lipid levels in 8,344 subjects followed by replication studies including 14,739 additional

individuals. We replicated seventeen previously reported loci. We also newly identified three

Chinese specific variants in previous regions (HLA-C, LIPG, and LDLR) with genome-wide

significance. Almost all the variants contributed to lipid levels change and incident

hyperlipidemia over 8.1-years follow-up among 6,428 individuals of a prospective cohort study.

The strongest associations for lipid levels change were detected at LPL, TRIB1,

APOA1-C3-A4-A5, LIPC, CETP, and LDLR (P range from 4.84 × 10-4 to 4.62 × 10-18), while

LPL, TRIB1, ABCA1, APOA1-C3-A4-A5, CETP, and APOE displayed significant strongest

associations for incident hyperlipidemia (P range from 1.20 × 10-3 to 4.67 × 10-16). The four

lipids genetic risk scores (GRS) were independently associated with linear increases in their

corresponding lipid levels and risk of incident hyperlipidemia. A C-statistics analysis showed

significant improvement in the prediction of incident hyperlipidemia on top of traditional risk

factors including the baseline lipid levels.

Conclusions - These findings identified some evidence for allelic heterogeneity in Chinese

compared with Europeans in relation to lipid associations. The individual variants and those

cumulative effects were independent risk factors for lipids increase and incident hyperlipidemia.

Key words: Genome Wide Association Study; lipids; incidence; incident hyperlipidemia, lipid level change

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The strongest associations for lipid levels change were detected at LPL, TRIB1,

APOA1-C3-A4- -A5- , LIPC, CETP, and LDLR (P( range from 4.84 × 10-4 to 4.62 × 100 1-18), while

LPPPLLL,,, TTRTRIBIBB111,,, ABABABCACACA1, APOA1-C3-A4- -A5- , CETP, aananddd APOE displaaayeyy d sisiigggnnificant strongest

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Introduction

Plasma concentrations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C),

high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) are the most important risk

factors for cardiovascular diseases and are targets for therapeutic intervention1-2. Genome-wide

association studies have successfully identified multiple genetic loci associated with blood

lipids3-7. However, almost all these loci were identified initially in European ancestry populations,

and few such genetic studies have assessed Asian populations8-10, especially Chinese populations.

Moreover, most of these studies mainly focused on cross-sectional data, and little data exist

regarding their genetic loci predicting lipid levels variation over time and hyperlipidemia

incidence. Lifestyle factors, including poor diet, obesity, and lack of exercise, are known to

increase the risk of hyperlipidemia. The differences in environmental exposures and genetic

background between Chinese and Europeans might suggest potential different pathways of blood

lipids. Some variants may be more common in specific ethnic groups, thereby providing greater

statistical power, or the effects of genetic variants on lipid levels may be enlarged in specific

ethnic groups11,12. Therefore, large scale studies in Chinese are needed not only to evaluate

whether the previous reported loci could be generalized to Chinese population but also to

identify new loci or Chinese specific variants for blood lipids.

Herein we conducted a genome-wide association study (GWAS) of blood lipid levels that

included a meta-analysis of GWAS from 8,344 samples at the discovery stage and additional

14,739 samples in an independent replication study, involving a total of 23,083 subjects from

Chinese Han ancestry. We subsequently investigated whether the identified variants would

contribute to lipids increase and incident hyperlipidemia in 6,428 individuals who participated in

a prospective cohort study.

ncidence. Lifestyle factors, including poor diet, obesity, and lack of exercise, areee knknknowowwn n n tototo

ncreasese tthe rrisi k ofof hyperlipidemia. The differencees s in environmental exxpopop sures and genetic

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Material and Methods

Study population

The study comprised two-staged analyses carried out separately for TC, LDL-C, HDL-C, and TG.

A detailed description of the sample characteristics and phenotype measurements for each study

are provided in Table 1 and Supplementary material, Methods. The discovery stage included

meta-analysis of four independent GWAS in 8,344 individuals from Chinese populations: the

China atherosclerosis study (CAS)13, the Beijing atherosclerosis study (BAS)14, Genetic

Epidemiology Network of Salt-Sensitivity study (GenSalt)15, and the Guangxi Fangchenggang

Area Male Health and Examination Survey (FAMHES)16. In stage 2, replication analyses were

conducted in an independent sample with a total of 14,739 individuals from China

Cardiovascular Health Study (CCHS). CCHS was conducted to investigate the risk factors for

cardiovascular diseases in China since 2006. Overnight fasting blood samples were drawn by

venipuncture to measure lipid levels. Blood specimens were processed in the central clinical

laboratory at the Department of Population Genetics at Fuwai Hospital of the Chinese Academy

of Medical Sciences in Beijing. This laboratory participates in the Lipid Standardization Program

of the US Centers for Disease Control and Prevention and National Heart, Lung, and Blood

Institute.

A subset of CCHS individuals was from two prospective cohorts (ChinaMUCA17 and

InterAsia18) and used to investigate individual single nucleotide polymorphisms (SNPs) and

those cumulative effects on blood lipids increase and incident hyperlipidemia. The individuals

completed the baseline survey and examination in 1998, 2000 and 2001. For the present study,

the analyses were limited to participants for whom complete data were available for both

follow-up data and genetic risk score. These restrictions resulted in 6,428 individuals. After

conducted in an independent sample with a total of 14,739 individuals from Chinnana

Cardiovovasa culalar HeHealth Study (CCHS). CCHS was s coconducted to investigagatet the risk factors for

cacaardddiovascularaa dddisisiseaeasesesesss ininn CCChiinanana sssininincecee 2220000 6.6.6. OOOverrnniiightt fffasasastititingnn bbblloloododod ssamamamplpp ess wwwererere e e drdd awawawn n n bybyby

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aboratory at the Deepapapartrtr ment ooof f f Poopulaatit on Genettticici ss at Fuwuwuwaiaa Hospip tataalll ofofof the Chinese Academy

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exclusion of prevalent hyperlipidemia cases at baseline, 6,022, 5,814, 4,977, and 5,450

individuals for TC, LDL-C, HDL-C, and TG, respectively, were eligible for the present analysis

of lipids increase and incident hyperlipidemia.

The diagnosis criteria of hyperlipidemia were based on Detection, Evaluation, and

Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) report19:

dl), high levels of low-density lipoprotein cholesterol

(LDL- -density lipoprotein cholesterol (HDL-C < 40 mg/dl),

Each study obtained approval from the institutional review boards of local research

institutions. All participants in each study gave written informed consent.

Genotype imputation and genotyping of selected SNPs

Detailed descriptions of genotyping arrays and quality control filters applied to the four

discovery studies are provided in Supplementary Material, Table S1. To facilitate combining

results of genome-wide association scans based on the different genotyping platforms, we

imputed missing genotypes based on reference haplotypes from the phased CHB+JPT HapMap

data release 22 reference dataset using MACH20 or IMPUTE21. Only imputed SNPs with high

genotype information content were used for the association analysis. After

quality control, we obtained up to 2.5 million genotyped or imputed autosomal SNPs for

subsequent association analysis.

After genome-wide association analyses for each of the four discovery studies and

meta-analysis in the combined samples, SNPs representing the independent association signal

were taken forward to replication if they showed potential association (P < 5.0 × 10-5) for lipid

levels in the discovery meta-analysis. If a SNP could not be genotyped, alternative tagging SNPs

nstitutions. All participants in each study gave written informed consent.

Genotytyypepep impmpututata ion and genotyping of selecteded SNPs

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didiiscscscovoo ery studiess aaare ppprooovidededed in Suuupppp lllemmennntaaary MaMaMateeriririalalal, TaTaTable S11. To faaacilillitittataa e cooommmbiiiniining

esults of genome-wiwiwidedede assoccciaiaiatitit on scans based ooon nn tht e difffffferererenent geg nootytyypipipingg pplatforms, we

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were considered. In total, twenty-seven SNPs were selected and genotyped using iPLEX

Sequenom MassARRAY platform (Sequenom).

Statistical analysis

TG values were log transformed prior to analysis. Population stratification was estimated by a

principal component approach, as implemented by EIGENSTRAT software22. Within each of the

four discovery studies, continuous lipid levels were adjusted for age, age-squared, gender, body

mass index (BMI), the first two principal components, and lipid lowering drug prescriptions (if

applicable) in linear regression models under an additive model. We used allele dosages from

imputation to account for uncertainty in imputed genotypes. A fixed-effects inverse

variance-weighted meta-analysis implemented in METAL23 was used to combine the four studies

in the discovery stage and to obtain results for each SNP. A quantile-quantile plot generated using

R was used to evaluate the overall significance of the GWAS results and the potential impact of

2 statistic divided by 0.456. We detected the associations of SNPs in the replication populations,

and additionally, we carried out meta-analysis of the discovery and replications.

Genetic risk score (GRS)

We assessed the cumulative effect of the SNPs by using lipids GRS, which was a weighted sum

across the SNPs combining doses of the lipid increasing alleles (lipid decreasing alleles for

HDL-C) and the -coefficient) for lipid levels change over time in a prospective

cohort. Four scores were calculated individually for each lipid level (TC, LDL-C, HDL-C, and

TG). We included in the GRS only those SNPs significantly associated with the corresponding

lipid levels in our combined discovery and replication studies with genome-wide significance or

P < 5.0 × 10-5 for the SNPs located in known lipid genes. There was no linkage disequilibrium

variance-weighted meta-analysis implemented in METAL23 was used to combineee ttthehehe fffououour r r stststudududies

n the ddisiscoveeryy sstat ge and to obtain results for eacch h SNP. A quantile-quanantit le plot generated using

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2 statistic divided bbby y y 0.0.0 456. WWWe ee ded tectede the assococociaations ofofof SNPN s in ttthehehe repplication populations,

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(LD) between SNPs included in the GRS. A 14-SNP GRS was constructed for TC, an 8-SNP

GRS for LDL-C, a 6-SNP GRS for HDL-C, and a 9-SNP GRS for TG. Missing genotype data

for each SNP were imputed using the average risk allele frequency. However, if more than two

SNP genotypes were missing for a given individual, the GRS was set as missing for that

individual. The GRS was modeled as a continuous variable (per one standard deviation increase)

and as quartiles of TC (7.85 to 19.79, 19.79 to 22.28, 22.28 to 24.77, and 24.77 to 35.40), LDL-C

(2.95 to 10.02, 10.02 to 11.60, 11.60 to 13.20, and 13.20 to 17.90), HDL-C (-13.38 to -9.18,

-9.18 to -8.25, -8.25 to -7.11, and -7.11 to -1.34), and TG (0.05 to 0.14, 0.14 to 0.16, 0.16 to 0.19,

and 0.19 to 0.26). Associations of the GRS with their corresponding lipid levels increase and

incident hyperlipidemia were analyzed using linear and logistic regression, respectively, in

models adjusting for sex, age, body mass index (BMI), and the baseline lipid levels

corresponding to GRS. We tested the null hypothesis of no linear trends effect over the quintiles.

We determined the effect estimates for quintiles of GRS with the bottom quintile serving as the

reference group. P values are reported for the linear trends across the quintiles. To evaluate the

improvement in risk discrimination by using the genetic information, we compared C-indices24

for the models with and without the GRS.

Results

The discovery meta-analysis and replication study

The discovery stage included four independent GWAS in 8,344 individuals from Chinese

populations (Table 1). The discovery meta-analysis evaluated separately associations for TC,

LDL-C, HDL-C, and TG, with 2,573,667 genotyped or imputed autosomal SNPs. All genotyped

and imputed autosomal SNPs passed quality control filters in each of the four datasets prior to

conducting the meta-analysis (Supplementary Material, Table S1). Quantile-quantile plots for

ncident hyperlipidemia were analyzed using linear and logistic regression, respeecectititivevev lylyly, , , ininin

modelss adjdjjusstit ngngg ffor sex, age, body mass index ((BMBMI), and the baseline llipipid levels

cocoorrrrespondinnnggg tototo GGGRSRSS. WeWeWe tttessteteted dd thththe e nununullll hhhypypypotoo hesis offf nnno oo lililinenn ararar trererendndn sss efefeffectt ooovevever r r ththt eee quququininintititileleles.s

WWWe dddetermined tthheee effffeccct esttimimimates ffforoo quuuintiiileees offf GRGRGRSS S wwwittth the booottommm qqquininntitiile serrrvivivingngg aaas thhhe

eference group. P vvvalalalueueu s aree rererepopop rted for the lineaeaear trendsss aaacrcrc oso s the e quququinini tiles. P To evaluate the

mmprprp ovovememenent t inin rrisisk k didiscscririmiminanatitionon bybyy uusisingngg tthehe gggeneneteticic iinfnforormamatitionon,,, wewe ccomompapap rered d C-C inindidicecess24



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lipid levels are presented in Supplementary Material, Figure S1. The genomic control inflation

- 30~1.038). As shown in the Manhattan plots

of the -log10 P values for lipid levels in Supplementary Material, Figure S2, the meta-analysis

identified nine well-established loci (GCKR, HMGCR, LPL, ABO APOA1-C3-A4-A5, LIPC,

CETP, LIPG, and LDLR) at genome-wide significance (defined as P < 5.0 × 10-8). We then

selected 27 SNPs that were associated with TC, LDL-C, HDL-C, and/or TG at P < 5.0 × 10-5 in

the discovery analysis and genotyped them in an independent sample comprising 14,739 Chinese

individuals. In replication analysis, of these 27 SNPs, 18 SNPs showed significant association

with lipid after adjustment for multiple testing (P < 0.0018 = 0.05/27), including 11 SNPs with

genome wide significance). Additionally, two SNPs showed nominal significance (P < 0.05)

(Supplementary Material, Table S2).

Associations at previously reported loci and Chinese-specific variants

We evaluated the evidence of association for these SNPs by combined results of the discovery

and replication study. We confirmed 14 previously reported loci of TC, LDL-C, HDL-C, and/or

TG (PCSK9, ANGPTL3, GCKR, HMGCR, MLXIPL, LPL, TRIB1, ABCA1, ABO,

APOA1-C3-A4-A5, ALDH2, LIPC, CETP, and APOE) at the level of genome-wide significance

(P < 5.0 × 10-8). Moreover, suggestive associations were replicated for APOB, HPR and ABCA8

loci (Table 2). We also identified three novel ethnic-specific variants in previous reported

regions (HLA-C, LIPG and LDLR) in European populations at the level of genome-wide

significance. These SNPs were not in LD (r2 < 0.2 in JPT+CHB) with the previously reported

SNPs. These include SNPs in HLA-C (TC, P = 1.50 × 10-10; LDL-C, P = 3.28 × 10-10), LIPG

(HDL-C, P = 2.35 × 10-9), and LDLR (TC, P = 2.39 × 10-16; LDL-C, P = 1.44 × 10-24).

genome wide significance). Additionally, two SNPs showed nominal significancecee (((PPP( <<< 000.0.005)5)5) P

Supppplplplemememenenentatataryy MMMaterial, Table S2).

AAAsssosociations aaat t t prprprevvvioioiousususlylyly repepeporororteteteddd lololocicc aaandndnd Chhhinnnesesee-spspspecececifii iccc vvvararariaiaiantntntss

WWWe e eveve aluated d d theee eeevidddennnce ooof ff aasassociciiataa iooonnn forrr thhheseee SSSNPNPPss s bybyy cccombbibinnned reeessultttsss ooof theee dddisi ccocovev ryyy

and replication studddy.y.y. WWWee cococonfnfnfiririrmememed d d 141414 prprprevevevioioiousususlylyly rrrepepeporoo teteteddd loloocicici ofofof TCTCTC,,, LDLDLDLLL-CCC, HDL-C, and/or

TGTGTG (((PCPCPCSKSKSK999((( , ANANANGPGPGPTLTLTL333, GCGCGCKRKRKR, HMHMHMGCGCGCRRR, MLMLMLXIXIXIPLPLPL, LPLPLPLLL, TRTRTRIBIBIB111, ABABABCACACA111, ABABABOOO,



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Single variants and lipid levels change and incident hyperlipidemia

Of these 20 SNPs in Chinese shown in Table 2, 17 were associated in at least two lipid

phenotypes, which resulted in a total of 37 associations of those SNPs with the corresponding

lipid levels change and incident hyperlipidemia (Supplementary Material, Table S3). For lipid

levels change, all the SNPs displayed a positive association in a direction consistent with their

effect on lipid. We compared the effect sizes for lipids change over time to those observed in

GWAS shown in Table 2 and found a high degree of correlation (r values from 0.83 to 0.92)

(Supplementary Material, Figure S3). Of these 37 associations, 24 (64.9%) showed nominal

significance (P < 0.05), while 10 (27.0%) displayed significance even at a Bonferroni-corrected

threshold (P range from 4.84 × 10-4 to 4.62 × 10-18 < 0.05/37) in LPL, TRIB1, APOA1-C3-A4-A5,

LIPC, CETP, and LDLR genes (Table 3). For incident hyperlipidemia, directions of effect were

consistent for all SNPs but the association of LIPG with hypercholesterolemia. 21 (56.8%)

displayed were significantly associated with the corresponding incident hyperlipidemia at

nominal significance (P < 0.05). ABCA1, APOA1-C3-A4-A5, and CETP with low levels of

high-density lipoprotein cholesterol, and LPL, TRIB1, APOA1-C3-A4-A5, and APOE with

hypertriglyceridemia displayed significant associations after accounting for multiple testing (P

range from 1.20 × 10-3 to 4.67 × 10-16 < 0.05/37).

Genetic risk score and the corresponding lipid levels change and incident hyperlipidemia

Over the mean follow-up period of 8.1 year, 404, 221, 476, and 807 individuals developed

incident hypercholesterolemia, high levels of low-density lipoprotein cholesterol, low levels of

high-density lipoprotein cholesterol, and hypertriglyceridemia, respectively. For each lipid

outcome (TC, LDL-C, HDL-C, and TG), the SNPs associated with the corresponding lipid levels

(Supplementary Material, Table S3) were used to calculate GRS. The GRS for four lipids were

hreshold (P( range from 4.84 × 10-4 to 4.62 × 10-18 < 0.05/37) in LPL, TRIB1, APPPOAOAOA111--C3C3C3--A4A4A4-- -AAA5-

LIPC, CECETPP,,, and d LDL LR genes (Table 3). For incidedent hyperlipidemia, didirer ctions of effect were

cocoonsnsnsistent forrr aallllll SSSNPNPPsss bububuttt thtt e e asasassososocicc atatatioioion ofofof LILL PGGG wwwithhh hhhypypyperee chhhooolesesestetet rororolelelemia.a.a. 222111 (5(5(56..888%)%)%)

didiispspsplalal yed were sssigggnifiicacaantly asssssociatetet ddd wwwith thhhe cooorrrrespopoponndinining innnciiided nt hhhyyypeererlililipidemimimia aat

nominal significancecee (((PPP((( < 0.050505).).). ABCA1, APOA11--C3C -A4- -A5A5A5- ,,, ana d CETPTPTP wiww th low levels of

hihighghg -ddenensisitytyy llipippopopproroteteinin cchoholeleststererolol,,, anand d LPLPLL,,, TRTRIBIB11,,, APAPOAOA11-C3C3-A4A4- -A5A5- ,,, anandd APAPOEOE wiwithth



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independently associated with linear increases in their corresponding lipid levels over time (P

range from 6.55 × 10-11 to 1.42 × 10-28) and incident hyperlipidemia (P range from 5.33 × 10-4 to

5.33 × 10-18) (Table 4). For example, individuals in the highest quartile of risk score had an

increase of 9.930 mg/dl in TC levels, and 93.5% increased risk for incident hypercholesterolemia,

compared with those in the lowest quartile. An increase of one standard deviation of the GRS

was associated with increases of 3.724 mg/dl (P = 6.59 × 10-22) in TC, 2.252 mg/dl (P = 4.72 ×

10-12) in LDL-C, and 0.033 mg/dl (P = 5.71 × 10-36) in log-transformed TG, and a decrease of

1.699mg/dl (P = 2.06 × 10-28) in HDL-C. Figure 1 showed the distribution of GRS by their

corresponding incident hyperlipidemia. There was significant difference of GRS between

individuals with hyperlipidemia and without hyperlipidemia (P from 4.87 × 10-5 to 1.57 × 10-27).

Each standard deviation increase of the GRS resulted in 28.6%-48.2% increased risk for the

corresponding incident hyperlipidemia (P values range from 8.46 × 10-4 to 2.00 × 10-21).

Adding GRS to the models including traditional risk factors significantly improved risk

discrimination of incident hypercholesterolemia (C-index change = 1%; P = 0.035),

hypertriglyceridemia (C-index change = 2%; P = 2.0 × 10-4) and low levels of high-density

lipoprotein cholesterol (C-index change = 2%; P = 5.0 × 10-3) (Supplementary Material, Table

S4), while C-index analysis showed no improvement in the prediction of incident high levels of

low-density lipoprotein cholesterol (P = 0.122).

Discussion

The present study systematically investigated genetic susceptibility to lipid levels and its

relevance to lipid change over time and risk of incident hyperlipidemia in Chinese populations.

Our GWAS for lipids in Chinese replicated seventeen loci previously identified in populations of

European. We also identified Chinese specific variants at three loci (HLA-C, LIPG, and LDLR).

ndividuals with hyperlipidemia and without hyperlipidemia (P( from 4.87 × 10-5 tto o 11.1 575757 ××× 101010--2227).

Each statandn arrd d deeviv ation increase of the GRS resultltede in 28.6%-48.2% inncrc eased risk for the

cocoorrrrespondinnnggg ininincicicideentntnt hhhypypyperee liiipipipidededemimimiaaa (((aaa PPP((( vavavalululues rrannnge fffrororom m m 8.464646 ××× 10101 -4 tototo 2.0000 0 0 ××× 101010-21).).).

Adding g GRGRGRS tooo ttthe mmmodododels innnclclududdinggg trrraditttiooonaaall l riririskskk fffactooorsss signnnififfiiicananantltltly immprprrovovveddd risssk

discrimination of incicicideded nt hyhyypepepercrr holestterolemia (C(C(C-index chchchanangeg = 1%;%;%; PPP = 0.035),

hyhyypepep rtrtririglglg ycycy ererididememiaia (((CC-inindedex x chchanangegeg = 22%%;;; PP = 22.0.0 × 1100PP -444) ) ) anand d lolow w lelevevelsls oof f hihighghg -dedensnsitity y y



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The study is the first investigation to address effect of variants on both lipid changes over time

and risk of incident hyperlipidemia in East Asians. In a sample of 6,418 Chinese participants

from a prospective cohort study, during more than 8.1 years of follow-up, we found that this

genetic information in Chinese could improve the prediction of incident hyperlipidemia beyond

baseline lipid levels.

Recent GWAS have identified 157 significantly lipid-associated loci in individuals of

European ancestry, and ancestry-specific analyses identified some associated SNPs clearly

distinct from the original GWAS variants3. As these studies were conducted almost exclusively in

populations of European descent, studies in non-European populations would allow us to assess

the relevance of the findings to other ethnic groups. In the present study, we found both shared

and population-specific lipid susceptibility was commonly present. We not only replicated 17

previously reported loci but also identified three Chinese specific variants (HLA-C, LIPG, and

LDLR). Of those, the association of LIPG with HDL-C has been found not to be generalized to

diverse populations including African, Indians, Mexican and Hispanics.12 We observed that the

reported lead SNPs at these three loci in Europeans were monomorphic or had low minor allele

frequency (MAF < 0.1) in the Chinese Han population, whereas these SNPs were quite

polymorphic in European populations (Supplementary Material, Table S5). For example, in

Europeans a prominent association was reported for rs6511720, which is not polymorphic in the

Chinese, whereas the strongest LDL-C association at LDLR (rs7258950) was detected in the

Chinese. These data suggest that genetic heterogeneity in these loci may be due to different LD

structure and minor allele frequency. As expected, the ethnic specific variants we detected in

Chinese were not in LD with the initially reported lead SNPs in Europeans. Conversely, we also

investigated whether the 3 Chinese specific variants identified in our samples were associated

he relevance of the findings to other ethnic groups. In the present study, we foundndnd bbbototth h h shshshararar deded

and popopupup lationo -spsppece ific lipid susceptibility was comommonly present. We nonot only replicated 17

prprprevvviously repepeporororteteted loloocicici bbbututut alslssooo idididenene tititifififiedee thththrereree CCChiiinessse e e spspspecececifficicic vvvararariaiaiantntntsss (HLLLAAA( -C-C-C,, LILL PGPGPG,, anananddd

LDLDDLLRLR). Of thoseee, ttthe aaasssociaaatitiioono of LILL PGPGPG wiiithhh HDDDLLL-CCC hahahas bebebeen fffoouound nnnot tooo bbbe gennnerereralllizzzede ttto

diverse populations inininclclc uding g g AfAfAfrican, Indians, MeMeMexican ananand d d Hisppaniciccs.s.s 12 We observed that the

eepopop rtrteded lleaead d SNSNPsPs aat t ththesese e ththreree e lolocici iin n EuEuroropepep anans s wewerere mmononomomororphphp icic oor r hahad d lolow w miminonor r alallelelele



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with lipid in Europeans, using the results from the Global Lipids Genetics Consortium (GLGC),

a meta-analysis of 188,577 individuals.3 The rs12970066 at LIPG showed suggestive significant

association with HDL-C in the population of European ancestry (P = 4.77 × 10-7), while

rs9357121 at HLA-C and rs7258950 at LDLR showed no associations with lipid levels. These

differences in European and Chinese populations may facilitate the fine mapping of common

causal variants. We used HaploReg25 to search for evidence of the functional role for three

Chinese-specific variants. Rs9357121 (HLA-C) and rs12970066 (LIPG) were in strong LD with

rs1131151 (r2=0.91 in 1000 Genomes ASN) and rs2000813 (r2=0.81), respectively, which were

non-synonymous variants. The SNP rs7258950 in LDLR lies within the promoter and enhancer

histone marks as well as DNase hypersensitive sites. To gain further understanding of the lipid

susceptibility loci, we tested their associations with the traditional risk factors of cardiovascular

diseases in the replication samples. After Bonferroni correction for 20 independent tests, LPL

showed significant associations (P < 2.5 × 10-3 = 0.05/20) with diastolic blood pressure and

hypertension, while GCKR was significantly associated with plasma glucose (Supplementary

Material, Table S6).

We further assessed the individual effect of lipids-related SNPs on increases in lipids and

risk of incident hyperlipidemia. All the SNPs indicated a directionality-consistent association

with increases in lipids for lipid-raising alleles. These SNPs were associated with mild increases

in TC (range, 0.456~2.849 mg/dl per allele), LDL-C (range, 0.664~2.464 mg/dl per allele) and

log-transformed TG (range, 0.005~0.036 mg/dl per allele), and decrease in HDL-C (range,

0.460~1.959 mg/dl per allele). As expected, almost all of these SNPs were also observed to

increase the risk of incident hyperlipidemia. Although each SNP exerts a modest effect, a

combination of SNPs, in aggregate, can have a substantial influence on incident hyperlipidemia.

histone marks as well as DNase hypersensitive sites. To gain further understandinngng oof f f thththe e e lililipipipiddd

uscepepptitibib litytyy locci,i,, we tested their associations withth the traditional risk fafactc ors of cardiovascular

dididiseeeases in thhheee rerereplplp iccatatatioioion n n sasasampmpmpleleles.s.s. AAAftftftererer BBBonononfeff rroooniii coorrrrrrececectititionoo fffooror 222000 ininindededependndndenenent t t tetet sttts,s,s, LPLPLPLL L

hhhowwwed significaaanntt assssoccciatiooonsnsns (P( << 2..5 × 1000P -333 = 000.0005//202020))) wwwittth diiiassstolic bbbloododod pressssururure anannd

hypertension, while e GCGCGCKR wawawasss signg ificcantly y assooocicc ata ed wwwititith hh plp asma ggglululucocc se ((Supplementary

MaMateteririalal,,, TaTablble e S6S6).).)



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Consistent with single variants finding, the variants in aggregate were significantly associated

with linear increases in lipids and risk of incident hyperlipidemia. These associations were

largely independent of lipid levels measured at baseline. By accumulation, for example,

individuals in the top compared to bottom quintiles of GRS differed by an increase of 9.930

mg/dl in TC and a 93.5% increased risk for incident hypercholesterolemia in a follow-up period

of 8.1 year. We also found GRS improved risk discrimination of incident hypercholesterolemia,

hypertriglyceridemia, and low levels of high-density lipoprotein cholesterol over the traditional

risk factors. Several European studies also showed the lipid-associated SNPs were associated

with the longitudinal lipid changes26-29. Tibor et al.26 further demonstrated that the ability of the

157 establish lipid-loci to predict incident dyslipidemia was modest in the Swedish populations

with 10-year follow-up. The C-index values were increased by 2-4%, which were slightly higher

than those (1-2%) reported by our study, given only 20 variants were involved in our GRS.

Although the variants have modest effects on lipids, their presence may act over the entire life

course and translate into comparatively large effects. It has been shown that joint effect of

LDL-C related SNPs was an independent risk factor for incident cardiovascular diseases in

Europeans30,31. However, the results might not be generalized to populations with genetic

backgrounds different from that of European populations. Thus, it is necessary to further evaluate

the predictive abilities of the genetic predisposition to higher lipids for risk of cardiovascular

diseases in Chinese population.

The major strength of our study includes the use of a population-based cohort for the

assessment of effect of variants on both lipid change over time and risk of incident

hyperlipidemia. The effect size estimated in published GWAS is the most frequently used to

create the GRS in published studies of genetic variants and risk prediction, but it may be

157 establish lipid-loci to predict incident dyslipidemia was modest in the Swedisissh h h popoopupupulalalatititioono s

with 10-0 yeyey ar ffollolow-w up. The C-index values were inincreased by 2-4%, whhici h were slightly higher

hhhannn those (1-2%2%%))) reepopoportrtrtededed bbby yy ououour r r stststududdy,y,y, givivivenenen onllly y 20 vvvararariaiaiantnn s wewewererere iinvnvnvoloo ved d d ininin oooururu GGGRSRSRS.. .

AlAlAlthhhouo gh the varrriaaants haaave mmmoododest efefe feeecttts onnn lllipiddds,, theheheiririr prrresssenccce mam y acacct oovoveeer thee eeennntiiiree e lifeee

course and translatee ininintotot commpapapararr tit velyy largeg effececectss. It hasasas bbbeeen showwwn n n ththt at jjoint effect of

LDLDLL-CC rerelalateted d SNSNPsPs wwasas aan n inindedepepep ndndenent t ririsksk ffacactotor r fforor iincncididenent tffff cacardrdioiovavascsculularar ddisiseaeasesess iin n



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overestimated due to winner’s curse. In the present study, we constructed the GRS using

coefficient as determined in our prospective cohort to increase the accuracy of the GRS. Our

results should be interpreted in the context of potential limitations. Firstly, our discovery sample

size is modest relative to other previous consortia of European descent. This means that some

signals especially with a low minor allele frequency and/or weaker genetic effects may have

been missed. Secondly, although our genetic predisposition score captured the combined

information from the established genetic variants for lipids in Chinese thus far, it may account

for only a small proportion of lipid variation. For the 20 significantly associated variants in

Chinese, trait variance explained was 5.0% for TC, 4.7% for LDL-C, 6.9% for TG and 6.1% for

HDL-C, respectively, which was small when compared to 12%–15% explained by ~157 variants

discovered in European populations.3-4 The identification of the variants with small effect in

large-scale studies would be expected to capture additional genetic variance. Finally, our study

was undertaken in individuals of Chinese, and hence the results might not be generalized to

populations with genetic backgrounds different from that of our population.

In conclusion, we replicated seventeen loci previously identified in populations of

European for lipid levels. At HLA-C, LIPG, and LDLR, we also identified some evidence for

allelic heterogeneity in Chinese compared to Europeans. The individual SNPs and those

aggregate effects were independent risk factors for lipid increase and incident hyperlipidemia.

Funding Sources: This study was funded by the National Basic Research Program of China (973 Plan) (2011CB503901) and the High-Tech Research and Development Program of China (863 Plan) (2012AA02A516) from the Ministry of Science and Technology of China and the National Science Foundation of China (91439202, 81422043, 81370002). This study was also supported by Beijing Natural Science Foundation (7142138).

Conflict of Interest Disclosures: None.

HDL-C, respectively, which was small when compared to 12%–15% explained bybyby ~~~15155777 vavavariririants

discovere ede in n Euuroropean populations.3-4 The identifificac tion of the variantss wwith small effect in

aaargrggee-scale stttudududieieiesss wowooulululd d d bebebe expxpxpecececteteted dd tototo capapaptututurer adddddditiooonananal ll gegegeneeetititic vavavaririiananancecc . FiFiFinananalllllly,y,y, oooururur ssstututudydydy

wawawas s unu dertaken iiinn indidiivvviduallls ofo Chihiineneeseee, annnd hennnceee thhheee rressulllts mmmiggght nooot beee gggene eraaalizizzeddd ttto

populations with genenneteteticic backgkgkgroror unu ds ddifferent ffrororom that ooof f f ouour pop pupulalalatititionoo .

IIn n coconcnclulusisionon, , , wewe rereplplp icicatateded ssevevenenteteenen llococi i prprp evevioioususlylyy iidedentntififieied d inin pppopoppululatatioionsns oof f



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genetic determinants of plasma total cholesterol levels and their predictive value in n n a aa loongnggititududini atudy. Atherosclerosis. 2010;213:200-205.

30. Isaaaacss A, , WiWilllleme s SM, Bos D, Dehghan A, Hoofmfman A, Ikram MA, etet al. Risk scores of coommmmmmono geneneneeeticcc vvvariants for lipid levels influenceee aaatherosclerosis aaand incncnciiident coronary heart dididiseeease. Artererrioooscscsclell r r ThThThrororombmbm VVVasasasc c c BiBiBiololol. 2000131313;3;; 3:22222233-2-2-22323239.9.9.

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Table 1: Baseline characteristics of the participants

Stage of studyDiscovery Replication

CAS BAS GenSalt FAMHES CCHS

sample size 3,998 466 1,881 1,999 14,739

Male/Female 1870/2128 363/103 993/888 1999/0 4222/10517

Age (yrs) 52.53±7.69 51.07±9.53 38.72±9.53 37.5 ±11.1 52.49±8.70

BMI (kg/m2) 25.18±3.79 24.64±3.36 23.35±3.18 23.3±3.4 24.77±3.69

TC (mg/dl) 185.84±35.79 195.34±37.05 170.70±33.65 220.42 ±40.22 183.17±34.93

HDL-C (mg/dl) 49.92±12.42 49.78±12.38 50.97±11.31 54.52 ±12.76 51.20±12.42

TG (mg/dl) 159.24±115.52 128.04±98.84 123.47±78.20 137.24 ±158.49 149.20±110.61

LDL-C (mg/dl) 105.58±30.35 119.79±32.94 95.25±27.30 114.46 ±30.94 103.82±29.73

Cigarette Smoking (%) 32.74 62.23 34.13 50.8 16.29

Alcohol consumers (%) 24.71 46.78 29.24 82.6 20.24

CAS, China atherosclerosis study; BAS, Beijing atherosclerosis study; GenSalt, the Genetic Epidemiology Network of Salt-Sensitivity study; FAMHES, Guangxi Fangchenggang Area Male Health and Examination Survey; CCHS, China Cardiovascular Health Study (CCHS) project; s.d., standard deviation; TC; total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; BMI, body mass index.

/Female 1870/2128 363/103 993/888 1999/0 424242222222/1/1/10505051717

yrs) 52.53±7.69 51.07±9.53 38.72±9.53 37.5 ±11.1 52.49±8.70

(kkg/g/g/mmm222)) 22525.18±3.79 24.64±3.36 23233.35±3.18 232 .3±333 44.4 24.77±3.69

mmmg/dddl) 1885.848484±3±33555.797979 11919555.344±3±337.05 17770.70700±±3±33.66565 222 0.424242 ±±40.222222 1811 33..1777±±±34.4.4.9333

-C-CC (m(mmg/g dl) 4999.92±±±1222.42 49.7.778±±±1222.38 5000.97±7±7±11111.3111 545 .52 ±±±12.76766 55151.2220±±±12.4442

mg/dl) 159.2424±115.52 128.04±98.84 123.47±78..2020 137.244 ±158.49 149.20±110.61

C (mg/dl) 105.5.5.585858±3±3±30.0.353535 1111119.9.9 79797 ±3±3±322.2.949494 959595.2.2255±±2777.3.3.3000 1111114.4.4.464646 ±±±303030 9.9.944 4 103.82±29.73

eetttte e SmSmokokining g (%(%)) 3232.7.744 6262.2.233 3434.1.133 5050.8.8 1616.2.299



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Table 2: Association results with lipid levels in the combined discovery and replication studies

Gene SNP CHR BP Code/other allele Code alleleFrequency Trait Beta(S.E.) N P

Previous reported loci in GWAS

PCSK9 rs7525649 1 55271744 T/C 0.72 TC 2.212(0.34) 22612 1.62 × 10-10

LDL-C 1.954(0.29) 22376 2.69 × 10-11

ANGPTL3 rs12042319 1 62822407 G/A 0.81 TC 2.229(0.404) 23028 4.85 × 10-08

TG 0.02(0.003) 23027 8.33 × 10-12

APOB rs312949 2 21187788 C/G 0.28 TC 1.594(0.355) 23031 6.92 × 10-06

LDL-C 1.415(0.303) 22788 2.87 × 10-06

GCKR rs1260333 2 27602128 A/G 0.52 TC 1.773(0.32) 22873 2.94 × 10-08

TG 0.022(0.002) 22873 2.16 × 10-21

HMGCR rs6871667 5 74640498 A/G 0.55 TC 2.214(0.324) 22668 7.99 × 10-12

LDL-C 2.014(0.278) 22427 7.41 × 10-13

MLXIPL rs13231516 7 72501185 T/G 0.87 TG 0.03(0.004) 23039 9.65 × 10-17

LPL rs12678919 8 19888502 A/G 0.91 HDL-C -1.954(0.204) 22648 1.86 × 10-21

TG 0.044(0.004) 22649 3.86 × 10-25

TRIB1 rs2954029 8 126560154 A/T 0.41 TC 2.218(0.324) 23008 1.16 × 10-11

TG 0.023(0.002) 23008 1.86 × 10-20

ABCA1 rs2575876 9 106705560 G/A 0.79 TC 3.224(0.392) 23027 1.17 × 10-16

HDL-C 1.006(0.14) 23027 4.03 × 10-13

ABO rs579459 9 135143989 C/T 0.20 TC 3.038(0.480) 18474 2.72 × 10-10

LDL-C 2.853(0.392) 18259 5.70 × 10-13

APOA1-C3-A4-A5 rs662799 11 116168917 G/A 0.28 HDL-C -2.516(0.126) 23021 1.84 × 10-85

TG 0.081(0.003) 23021 4.18 × 10-213

PTL3 rs12042319 1 62822407 G/A 0.81 TC 2.229(0.404004)4)4) 232323020202888 4.4.4.8588 ×TG 0.02(0.000303) 232323020202777 8.8.8 3333 ×

B rs312949 2 21187788 C/G 0.28 TC 1.594(0.355)5) 23031 6.92 ×LDL-CCC 1.415(0.303) 22788 2.87 ×

R rs1260333 2 27602128 A//GGG 0.52 TC 1.773(0.32) 22873 2.94 ×TGTGTG 0.02222(2(2(00.0.000002)2)2) 2222228878 333 2.2.2.1666 ×

CCCR rs688871667 555 746400049998 A/GGG 0.0 555 TC 2.2221444(0.324) 222 6666888 7.9999 ×LDL-CCC 2.0101014(((0.0 278))) 224442777 7.4441 ×

PL rsss13131323232315151 161616 77 772501111858585 T/T/T/GGG 000.878787 TGTGTG 00.0 030 (0(00 00.0040404))) 232323030303999 9.99 656565 ×××rs1267898989191919 88 19191988888885858502022 AAA/GGG 00.0.919191 HDHDHDLL-C-C-C -1-1-1.9.9.95455 (0.204) 22648 1.86 ×

TGTGTG 0.0.0.040404444(0.004) 22649 3.86 ×111 rs292929545454020202999 888 121212656565606060151515444 A/A/A/TTT 000.414141 TCTCTC 222.2121218(8(8(000.3232324)4)4) 232323000000888 111.161616 ×××



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ALDH2 rs11066280 12 111302166 T/A 0.83 TG 0.017(0.003) 22589 9.79 × 10-9

LIPC rs1800588 15 56510967 T/C 0.38 TC 2.719(0.33) 22845 8.45 × 10-16

HDL-C 1.462(0.118) 22844 1.90 × 10-34

TG 0.018(0.002) 22845 2.52 × 10-14

CETP rs3764261 16 55550825 A/C 0.17 TC 4.439(0.447) 20576 9.69 × 10-23

HDL-C 3.353(0.159) 20575 2.00 × 10-97

HPR rs12927205 16 70582578 A/G 0.74 TC 1.784(0.362) 23005 6.94 × 10-07

LDL-C 1.462(0.309) 22763 8.02 × 10-07

ABCA8 rs12453914 17 64650473 A/C 0.42 TC 1.237(0.279) 23032 1.67 × 10-06

LDL-C 0.993(0.250) 22788 3.66 × 10-05

APOE rs157582 19 50088059 T/C 0.18 TG 0.078(0.008) 12320 3.92 × 10-22

Novel Chinese-specific variants at the previous reported lociHLA-C rs9357121 6 31348458 T/G 0.85 TC 2.062(0.330) 23001 1.50 × 10-10

(tagging rs3177928 r2=0.02) LDL-C 1.842(0.301) 22759 3.28 × 10-10

LIPG rs12970066 18 45361150 G/C 0.28 HDL-C 0.784(0.128) 22850 2.35 × 10-09

(tagging rs7241918 r2=0.017) LDLR rs7258950 19 11111139 G/A 0.78 TC 3.168(0.383) 22495 2.39 × 10-16

(tagging rs6511720 r2=0) LDL-C 3.387(0.326) 22252 1.44 × 10-24

SNP IDs and chromosomal positions are based on NCBI Build 36 of the genome. CHR, chromosome; r2 are obtained from 1000 Genomes data in JPT+CHB

LDL-C 1.462(0.3030309)9)9) 222222767676333 8.8.8.0200 ×8 rs12453914 17 64650473 A/C 0.42 TC 1.237(0.2727279)9)) 2323230303032 2 2 1.1.1.676767 ××

LDL-C 0.993(0.250) 22788 3.66 ×E rs1515575757 82 19 50088059 T/T/T/CCC 0.18 TG 0.078(0.008) 12320 3.92 ×

ChChiininese-specific vararariaiaiantntnts atatt ttthehehe ppprrereviviviouo ss rererepopoportrtrtededed llococociiiCCC rs93335777121 6 313313488845558 T/GGG 0.0 885 TC 2.00606222(0.330))) 232 0000111 1.505050 ×

(t( aggggiing rs33317777928 rrr2=0=0=0.0. 2) LDL-CCC 1.8484842(2((0.301)) 222 7775999 3.2228 ×rsss121212979797000000666666 18 4445361111505050 G/G/G/CCC 0.0.0 282828 HDHDHDLLL-C-C-C 0.0.0 78784(4(4(0.0.0.1212128)8)8) 22222285858500 0 2.2.2 353535 ×××(tagginggg rrrs7s7s7242419188 r222=0=0=0.0.0.0177) )rs72589595950 0 0 191919 11111111111111111 393939 G/G/G AAA 0.0.0.78788 TCTCTC 3.3.3.161616888(0.383) 22495 2.39 ×(((tatatagggggginininggg rrrs6s6s6515151171717202020 rrr2=0=00))) LDLDLDLLL-C-CC 333.3838387(7(7(000.3232326)6)6) 222222252525222 111.444444 ×××



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Table 3: Significant variants associated with lipid levels change and incident hyperlipidemia

SNP IDs and chromosomal positions are based on NCBI Build 36 of the genome. CHR, chromosome

Lipids Change Incident Hyperlipidemia

SNP Gene CHR Position Code allele other allele Beta(S.E.) P OR(S.E.) P

TC change and incident hypercholesterolemia

rs1800588 LIPC 15 56510967 T C 1.985(0.569) 4.84 × 10-04 1.222(0.075) 7.76 × 10-03

rs3764261 CETP 16 55550825 A C 2.596(0.730) 3.83 × 10-04 1.281(0.092) 7.15 × 10-03

rs7258950 LDLR 19 11111139 G A 2.849(0.655) 1.39 × 10-05 1.240(0.093) 2.08 × 10-02

LDL-C change and incident high levels of low-density lipoprotein cholesterol

rs7258950 LDLR 19 11111139 G A 2.464(0.552) 8.27 × 10-06 1.317(0.127) 3.02 × 10-02

HDL-C change and incident low levels of high-density lipoprotein cholesterol

rs2575876 ABCA1 9 106705560 A G -0.847(0.271) 1.77 × 10-03 1.310(0.082) 9.14 × 10-04

rs662799 APOA1-C3-A4-A5 11 116168917 G A -1.256(0.252) 6.24 × 10-07 1.282(0.077) 1.20 × 10-03

rs1800588 LIPC 15 56510967 C T -1.246(0.225) 3.35 × 10-08 1.266(0.074) 1.38 × 10-03

rs3764261 CETP 16 55550825 C A -1.959(0.284) 5.62 × 10-12 1.452(0.100) 1.80 × 10-04

TG change and incident hypertriglyceridemia

rs12678919 LPL 8 19888502 A G 0.030(0.006) 3.31 × 10-06 1.453(0.110) 6.40 × 10-04

rs2954029 TRIB1 8 126560154 A T 0.017(0.004) 6.72 × 10-06 1.303(0.056) 2.33 × 10-06

rs662799 APOA1-C3-A4-A5 11 116168917 G A 0.036(0.004) 4.62 × 10-18 1.629(0.060) 4.67 × 10-16

rs157582 APOE 19 50088059 T C 0.021(0.009) 1.76 × 10-02 1.576(0.118) 1.21 × 10-04

0588 LIPC 15 56510967 T C 1.985(0.569) 4.84 × 10 1.222((0.0.0.0700 5) 7.76 × 10

4261 CETP 16 55550825 A C 2.596(0.730) 3.83 × 10-04 1.282881(1((0.00.090992)2)2) 7.7.7.15151 × 10

8950 LDLR 19 11111139 G A 2.849(0.655) 1.39 × 10-05 1.240(0(0(000.0093)3)3) 222.080808 ××× 111000

C chaangngngeee anananddd inincicicided ntntt hihihigh levels of low-density lipoprotein cholesttererrololol

89595950 0 0 LDLR 19 11111139 G AA 2.464(0.552)2)2) 8.27 × 10-06 1.317(0.127) 3.02 × 10

CCC chhhange and incidentntt looow lel vvvelsss ofofof hhhigighhh--ddedensittty y y llilipoppproootein ccchooolesterrroll

555878 666 ABCA1 9 1000677705560 AAA G -0-0-0.884447(((0.27711) 1.77 ××× 1110-033 1.1 310(000.080882) 9.14 × 10

79999 APAPAPOAOAOA111--CC3C3-AA4A4- -AA5A5- 11111 1161616168688919191777 G GG AA -11-1.2.2.25556(0(0(0.2.252522) )) 6.6.6.2424 ××× 11000-07 1.1.1.2828282(2(2(0.0.0.00077)7)7) 1.1.20200 × 11100

0588 LIPC 15 56665151510900 67 C T T -11.2.2.2464646(0.225) 3.353535 ××× 10-08 1.266(0.074) 1.38 × 10

4261 CETP 161616 55555555555 080808252525 C C C A AA -1-11.9.99595959(0(0(0.2.2.28444) 5.5.5.626262 × 11000-12122 1.452(0.100) 1.80 × 10

hhahangngee ananddd iinin iciciddede tntnt hhyhypepe trtrt iriri lglglycycereridididememiiaia



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Table 4: Association of genetic risk scores for lipid levels with their corresponding lipid levels change and incident hyperlipidemia

Continuous GRS (per SD)*

Quintiles of GRS*

Q2 vs Q1 Q3 vs Q1 Q4 vs Q1

Lipid levels change Beta(S.E.) P value Beta(S.E.) Beta(S.E.) Beta(S.E.) P for trend

TC GRS vs TC change 3.724(0.386) 6.59 × 10-22 4.488(1.082) 7.04(1.085) 9.930(1.092) 9.72 × 10-21

LDL-C GRS vs LDL-C change 2.252(0.325) 4.72 × 10-12 2.012(0.918) 4.53(0.922) 5.529(0.924) 6.55 × 10-11

HDL-C GRS vs HDL-C change -1.699(0.153) 2.06 × 10-28 -1.174(0.445) -2.129(0.440) -4.178(0.433) 4.77 × 10-23

TG GRS vs TG change 0.033(0.003) 5.71 × 10-36 0.021(0.007) 0.038(0.007) 0.081(0.007) 1.42 × 10-28

Incident hyperlipidemia OR(S.E.) P value OR(S.E.) OR(S.E.) OR(S.E.) P for trend

TC GRS vs HyperTC 1.312(0.055) 6.49 × 10-7 1.292(0.175) 1.645(0.168) 1.935(0.162) 1.09 × 10-5

LDL-C GRS vs HyperLDL-C 1.286(0.075) 8.46 × 10-4 0.794(0.238) 1.392(0.210) 1.701(0.203) 5.33 × 10-4

HDL-C GRS vs LowHDL-C 1.351(0.052) 6.17 × 10-9 1.200(0.129) 1.582(0.135) 2.146(0.145) 1.72 × 10-8

TG GRS vs HyperTG 1.482(0.041) 2.00 × 10-21 1.319(0.126) 1.563(0.123) 2.661(0.118) 5.33 × 10-18

GRS, genetic risk score; OR, odds ratio; SD, standard deviation. HyperTC: hypercholesterolemia, HyperTG: hypertriglyceridemia, LowHDL-C:low levels of high-density lipoprotein cholesterol, HyperLDL-C: high levels of low-density lipoprotein cholesterol*The covariates were sex, age, BMI, and the baseline lipid levels corresponding to the genetic risk scores. The OR and Beta for continuous GRS are for 1 standard deviation unit increase in GRS.

RS vs TC change 3.724(0.386) 6.59 × 10-22 4.488(1.082) 7.04(1.085) 9.930(0((1.1.1.09090 2)2)2) 9.9.9.727272 × 1

C GRS vs LDL-C change 2.252(0.325) 4.72 × 10-12 2.012(0.918) 4.53(0.922) 5.52229(9(9(0.00 9292924)4)4) 6.6.6.555555 ××× 111

C GRS vs HDL-C change -1.699(0.153) 2.06 × 10-28 -1.174(0.445) -2.129(0.440) ) -4.178(0.433) 4.77 × 1

RSRSRS vsvsvs TG channngegege 0.033(0.003) 5.71 × 10-36 0..021(0.007) 0..0303038(0.007)7) 0.081(0.007) 1.42 × 1

eeent hhyperlipidemia OR(S..EEE.)) P vvvalluue OR(S..E.E.E.))) ORRR(S.E.))) OR(S..E.E.))) P fofofor tr

RSRSS vs ss HyHyH perTTCCC 1..313 2(0.0050 5)5)5) 6.66 499 × 11100-7 1...2929222(0.0.0.171755)5) 11..64445(5((0.00 1668) 1.1.9399 5((00.0.1616162) 1.0099 ×××

C GRS vs HyperLDL-C 1.1 286(0.0707075)5)5) 8.46 × 10-4 0.794(0.232338)8)8) 1.392((0.0..2121210)00 1.701(0.203) 5.33 ×

C GRS vs LowHDL-C 1.1.1.3535351(1(1(00.0.0505052)2)2) 6.6.6.171717 ×× 111000-9-9-9 1.1.1.202020000(0.1212129)9)9) 1.1..585882(2(2(0.0.0.1313135)5)5) 222.146(0.145) 1.72 ×

RS vs Hyypep rTG 1.482((0.041)) 2.00 × 10-2121 1.319((0.126)) 1.563((0.123)) 2.661((0.118)) 5.33 × 1



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Figure Legend:

Figure 1: Distribution of genetic risk scores by their corresponding incident hyperlipidemia in

follow up. (a) HyperTC: hypercholesterolemia, (b) HyperLDL-C: high levels of low-density

lipoprotein cholesterol, (c) LowHDL-C: low levels of high-density lipoprotein cholesterol, (d)

HyperTG: hypertriglyceridemia; The y-axis is the proportion of the group (either with or without

incident hyperlipidemia) with a given GRS. Plots were generated using R package PredictABEL.



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Peng and Dongfeng GuDongshuang Guo, Zhengyuan Zhou, Zili Yang, Renping Wang, Jun Yang, Weili Yan, XiaozhongLiu, Chen Huang, Chong Shen, Jinjin Shen, Ling Yu, Lihua Xu, Jianjun Mu, Xianping Wu, Xu Ji, Yongchen Hao, Jianxin Li, James E. Hixson, Yunzhi Li, Min Cheng, Xiaoli Liu, Jie Cao, Fangcao

Shufeng Chen, Jing Chen, C. Charles Gu, Jichun Chen, Ying Li, Liancheng Zhao, Hongfan Li, Xiangfeng Lu, Jianfeng Huang, Zengnan Mo, Jiang He, Laiyuan Wang, Xueli Yang, Aihua Tan,

Hyperlipidemia in Chinese PopulationsGenetic Susceptibility to Lipid Levels and Lipid Change Over Time and Risk of Incident

Print ISSN: 1942-325X. Online ISSN: 1942-3268 Copyright © 2015 American Heart Association, Inc. All rights reserved.

TX 75231is published by the American Heart Association, 7272 Greenville Avenue, Dallas,Circulation: Cardiovascular Genetics

published online November 18, 2015;Circ Cardiovasc Genet.

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1

SUPPLEMENTAL MATERIAL

Supplementary Methods

CAS

The China Atherosclerosis Study (CAS), including 1,010 cases of CAD and 3,998 controls,

was designed to identify the genetic susceptibility genes of coronary artery disease1. In the

present study, 3,998 participants in control were used to perform GWAS meta-analysis of

lipid levels. Study samples who were recruited from the International Collaborative Study

of Cardiovascular Disease in Asia. All individual were judged to be free of coronary,

cerebrovascular, renal diseases and other major chronic diseases by medical history.

Standard questionnaire were used by trained interviewers to obtain information on

demographic characteristics including age, sex, ethnicity, details of medical history,

smoking and alcohol consumption. Overnight fasting blood samples were drawn by

venipuncture to measure serum biochemical measurements including total cholesterol,

high-density lipoprotein cholesterol (HDL-C), and TG. Blood specimens were processed in

the central clinical laboratory at the Department of Population Genetics at Fuwai Hospital

of the Chinese Academy of Medical Sciences in Beijing. This laboratory participates in the

Lipid Standardization Program of the US Centers for Disease Control and Prevention. Total

cholesterol, HDL-C, and TG were analyzed enzymatically on a Hitachi 7060 Clinical

Analyzer (Hitachi High-Technologies Corp). The LDL-C concentrations were calculated by

use of the Friedewald equation.

BAS

The Beijing Atherosclerosis Study (BAS) comprised 466 individuals who were randomly

selected from subjects participating in a community-based survey of cardiovascular risk

factors in Beijing2. Details of medical history, cigarette smoking, and alcohol consumption

were obtained from all participants by standardized questionnaire. Overnight fasting blood

samples were drawn by venipuncture to measure serum biochemical measurements

including TC, HDL-C, and TG. The LDL-C concentrations were calculated by use of the

Friedewald equation.

The GenSalt Study

The Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study was conducted at

2

six study sites located in rural areas of northern China from October 2003 to July 2005. It

was a family feeding-study designed to examine the interaction between genes and dietary

sodium and potassium intake on BP. Detailed eligibility criteria for the participants have

been reported previously3. A community-based BP screening was conducted among

persons aged 18-60 years in the study villages to identify potential probands and their

families for the study. A total of 1,906 GenSalt probands and their siblings, spouses, and

offspring were eligible and 1,881 of them took part in the dietary intervention and GWAS

genotyping. Overnight (≥8 h) fasting blood specimens were obtained for measurement of

glucose and lipid concentrations. Plasma glucose concentration was measured with a

modified hexokinase enzymatic method (Hitachi automatic clinical analyser, model 7060,

Tokyo, Japan). Concentrations of total cholesterol, HDL cholesterol, and triglycerides were

assessed enzymatically with commercially available reagents. Concentration of LDL

cholesterol was calculated with the Friedewald equation.

FAMHES

The FAMHES project was initiated in Fangchenggang city, Guangxi, southwestern China

in 2009, including 4,303 Chinese men age 17 to 88 years old4. For this study, we included

only those aged 20 to 69 years old who reported Han ethnicity (n = 2,012). After stringent

quality control, 1,999 subjects were included in the analysis. Blood specimens were

obtained after participants had fasted overnight (≥8 h). The plasma TC, TG, LDL and HDL

levels were measured with electrochemiluminescence immunoassay on the COBAS 6000

system E601 immunoassay analyzer (Roche Diagnostics, GmbH, Mannheim, Germany),

with the same batch of reagents according to the manufacturer’s instructions.

CCHS

China Cardiovascular Health Study (CCHS) project has been a population-based

investigation of risk factors for cardiovascular diseases in China since 2006. Data collected

through in-person interviews and examinations include risk factors (based on questionnaire

and blood samples), anthropometry and BP. Overnight fasting blood samples were drawn

by venipuncture to measure serum biochemical measurements including total cholesterol,

High-density lipoprotein cholesterol (HDL-C), TG and glucose (GLU). Blood specimens

were processed in the central clinical laboratory at the Department of Population Genetics

at Fuwai Hospital of the Chinese Academy of Medical Sciences in Beijing. This laboratory

participates in the Lipid Standardization Program of the US Centers for Disease Control

3

and Prevention. Total cholesterol, HDL-C, TG and GLU were analyzed enzymatically on a

Hitachi 7060 Clinical Analyzer (Hitachi High-Technologies Corp). The LDL-C

concentrations were calculated by use of the Friedewald equation.

1. Lu X, Wang L, Chen S, He L, Yang X, Shi Y, et al. Genome-wide association study

in Han Chinese identifies four new susceptibility loci for coronary artery disease.

Nat Genet. 2012;44:890-894

2. Hou L, Chen S, Yu H, Lu X, Chen J, Wang L, et al. Associations of PLA2G7 gene

polymorphisms with plasma lipoprotein-associated phospholipase A2 activity and

coronary heart disease in a Chinese Han population: the Beijing atherosclerosis

study. Hum Genet. 2009;125:11-20.

3. The GenSalt Collaborative Research Group. GenSalt: rationale, design, methods

and baseline characteristics of study participants. J Hum Hypertens.

2007;21:639-646.

4. Tan A, Sun J, Xia N, Qin X, Hu Y, Zhang S, et al. A genome-wide association and

gene-environment interaction study for serum triglycerides levels in a healthy

Chinese male population. Hum Mol Genet. 2012;21:1658-1664.

4

Supplementary Figures

Figure S1. Q-Q plots of the genome-wide association results

a. TC, b. LDL-C, c. HDL-C, d. TG

The observed P values (y-axis) were compared with the expected P values under the null

distribution (x-axis). The Q-Q plot in black includes all SNPs that passed the inclusion

criteria.

c. HDL-C λ=1.030

b. LDL-C λ=1.033 a. TC λ=1.038

d. TG λ=1.033

5

Figure S2. Manhattan plot of genome-wide association results

The genes that are genome wide significant in the discovery analysis are indicated with its

gene names.

6

Figure S3. Effect sizes for single SNPs for lipids change over time are correlated with

the original estimated effects in GWAS

7

Supplementary Tables

Table S1. Stage 1 genome-wide genotyping, imputation and genotype-phenotype analysis by study

Study

Platform

Calling

algorithm

Filtering of genotypes before imputation Number of

SNPs for

Imputation

Imputation

software

NCBI;

HapMap

CHB+JPT

Filtering of

imputed

Genotype-phenotype

software Individuals

call rate

SNP

call

rate

SNP

HWE

SNP

MAF

CAS Axiom

CHB1 BRLMM ≥0.97 ≥0.95 >1.0E-5 ≥0.01 621,779

MACH

v1.0.16 36 r22

Rsq≥0.3;

MAF≥0.01; PLINK v1.07

BAS Affymetrix

500K BRLMM ≥ 0.97 ≥0.95 > 1.0E-4 ≥0.01 367,129

MACH

v1.0.16 36 r22

Rsq≥0.3;

MAF≥0.01; PLINK v1.07

GenSalt Affymetrix

6.0 Birdseed v2 None ≥0.95 >1.0E-5 ≥0.01 662,049

MACH

v1.0.16 36 r22

Rsq≥0.3;

MAF≥0.01; GWAF

FAMHES Illumina

Omin

Beadstudio/

Genomestudio ≥ 0.95 ≥0.95 >1.0E-3 ≥0.01 709,211

IMPUTE

V2.1.2 36 r22

info≥0.9;

MAF≥0.01; SNPTEST

CAS, China atherosclerosis study; BAS, Beijing atherosclerosis study; GenSalt, the Genetic Epidemiology Network of Salt-Sensitivity study; FAMHES, Guangxi Fangchenggang Area

Male Health and Examination Survey; MAF, minor allele frequency

8

Table S2. Association results for follow-up genotyping

SNP CHR BP Code/

other allele

Code allele

Frequency Trait P_discovery P_replication

Meta-analysis

Beta(SE) P N

rs7525649 1 55271744 T/C 0.72 LDL-C 1.19E-05 4.65E-07 1.954(0.29) 2.69E-11 22376

TC 4.25E-05 8.66E-07 2.212(0.34) 1.62E-10 22612

rs12042319 1 62822407 G/A 0.81 TC 4.74E-07 2.36E-03 2.229(0.404) 4.85E-08 23028

TG 1.49E-04 1.22E-08 0.02(0.003) 8.33E-12 23027

rs312949 2 21187788 C/G 0.28 TC 1.50E-04 5.53E-03 1.594(0.355) 6.92E-06 23031

LDL-C 1.25E-04 2.97E-03 1.415(0.303) 2.87E-06 22788

rs1260333 2 27602128 A/G 0.52 TC 2.18E-04 3.22E-05 1.773(0.32) 2.94E-08 22873

TG 2.52E-08 1.44E-14 0.022(0.002) 2.16E-21 22873

rs6871667 5 74640498 A/G 0.55 TC 1.33E-08 4.07E-06 2.214(0.324) 7.99E-12 22668

LDL-C 2.15E-07 3.65E-07 2.014(0.278) 7.41E-13 22427

rs1042391 6 16398740 A/T 0.89 TC 8.85E-04 8.14E-01 1.088(0.508) 2.82E-02 22824

LDL-C 7.07E-04 6.26E-01 1.018(0.435) 1.49E-02 22587

rs9357121 6 31348458 T/G 0.85 TC 2.17E-06 8.25E-05 2.062(0.330) 1.50E-10 23001

LDL-C 4.01E-04 3.40E-07 1.842(0.301) 3.28E-10 22759

rs3127569 6 160837549 T/C 0.14 TC 1.05E-04 1.22E-01 2.148(0.545) 9.90E-05 15530

TG 3.15E-04 8.13E-01 0.009(0.004) 1.33E-02 15530

rs13231516 7 72501185 T/G 0.87 TG 4.49E-05 2.35E-13 0.03(0.004) 9.65E-17 23039

rs12678919 8 19888502 A/G 0.91 HDL-C 1.76E-08 1.80E-14 -1.954(0.204) 1.86E-21 22648

TG 1.91E-10 3.03E-16 0.044(0.004) 3.86E-25 22649

rs2954029 8 126560154 A/T 0.41 TC 1.84E-05 1.37E-07 2.218(0.324) 1.16E-11 23008

9

TG 2.45E-06 7.90E-16 0.023(0.002) 1.86E-20 23008

rs2575876 9 106705560 G/A 0.79 TC 1.12E-06 2.00E-11 3.224(0.392) 1.17E-16 23027

HDL-C 1.41E-04 5.14E-10 1.006(0.14) 4.03E-13 23027

rs10980596 9 112659578 A/C 0.29 HDL-C 1.30E-04 2.11E-01 -0.447(0.126) 9.71E-04 22955

rs579459 9 135143989 C/T 0.8 TC 1.38E-07 1.74E-04 3.038(0.48) 2.72E-10 18474

LDL-C 8.77E-09 6.26E-06 2.853(0.392) 5.70E-13 18259

rs662799 11 116168917 G/A 0.28 TG 9.81E-76 2.11E-139 0.081(0.003) 4.18E-213 23021

HDL-C 2.63E-29 4.79E-58 -2.516(0.126) 1.84E-85 23021

rs671134 11 121558270 A/G 0.08 TC 2.25E-05 7.72E-01 1.418(0.598) 2.06E-02 23036

LDL-C 1.13E-04 2.96E-01 0.79(0.509) 1.37E-01 22792

rs11068274 12 108708557 A/G 0.46 TG 5.08E-03 3.28E-01 0(0.002) 3.66E-01 22984

rs11066280 12 111302166 T/A 0.22 TG 3.25E-05 5.14E-05 0.017(0.003) 9.79E-09 22589

rs4924334 15 37154191 A/C 0.63 HDL-C 8.48E-03 6.03E-01 0.255(0.117) 4.58E-02 22973

rs1800588 15 56510967 T/C 0.38 TC 3.40E-06 4.87E-11 2.719(0.33) 8.45E-16 22845

HDL-C 8.54E-16 1.88E-20 1.462(0.118) 1.90E-34 22844

TG 5.63E-05 7.89E-11 0.018(0.002) 2.52E-14 22845

rs11634431 15 60977088 T/G 0.45 HDL-C 8.24E-04 7.68E-02 -0.084(0.115) 5.61E-01 22875

rs3764261 16 55550825 A/C 0.17 TC 9.46E-03 1.64E-23 4.439(0.447) 9.69E-23 20576

HDL-C 5.60E-23 7.74E-77 3.353(0.159) 2.00E-97 20575

rs12927205 16 70582578 A/G 0.74 TC 1.03E-05 5.70E-04 1.784(0.362) 6.94E-07 23005

LDL-C 4.35E-05 9.06E-04 1.462(0.309) 8.02E-07 22763

rs12453914 17 64650473 A/C 0.42 TC 2.23E-05 1.59E-02 1.237(0.279) 1.67E-06 23032

10

LDL-C 5.57E-04 1.76E-02 0.993(0.250) 3.66E-05 22788

rs12970066 18 45361150 G/C 0.28 HDL-C 5.23E-09 1.67E-03 0.784(0.128) 2.35E-09 22850

TC 4.02E-05 8.42E-02 1.4(0.359) 1.23E-04 22850

rs7258950 19 11111139 G/A 0.78 TC 1.03E-08 2.28E-09 3.168(0.383) 2.39E-16 22495

LDL-C 4.21E-12 2.72E-14 3.387(0.326) 1.44E-24 22252

rs157582 19 50088059 T/C 0.18 TG 2.58E-07 1.73E-16 0.078(0.008) 3.92E-22 12320

11

Table S3. Single variants and lipid levels change and incident hyperlipidemia

Lipids Change Incident Hyperlipidemia

SNP Gene CHR Position Code allele other allele Beta(SE) P OR(SE) P

TC change and incident hypercholesterolemia

rs7525649 PCSK9 1 55271744 T C 0.889(0.591) 1.32 × 10-01 1.131(0.082) 1.34 × 10-01

rs12042319 ANGPTL3 1 62822407 G A 1.13(0.698) 1.06 × 10-01 1.052(0.095) 5.93 × 10-01

rs312949 APOB 2 21187788 C G 1.73(0.614) 4.87 × 10-03 1.231(0.080) 9.16 × 10-03

rs1260333 GCKR 2 27602128 A G 1.052(0.555) 5.83 × 10-02 1.196(0.075) 1.71 × 10-02

rs6871667 HMGCR 5 74640498 A G 1.151(0.553) 3.76 × 10-02 1.133(0.075) 9.49 × 10-02

rs9357121 HLA-C 6 31348458 T G 1.248(0.789) 1.14 × 10-01 1.064(0.109) 5.68 × 10-01

rs2954029 TRIB1 8 126560154 A T 1.344(0.561) 1.67 × 10-02 1.193(0.075) 1.85 × 10-02

rs2575876 ABCA1 9 106705560 G A 2.061(0.676) 2.31 × 10-03 1.143(0.094) 1.53 × 10-01

rs579459 ABO 9 135143989 C T 1.304(0.963) 1.76 × 10-01 1.020(0.131) 8.80 × 10-01

rs1800588 LIPC 15 56510967 T C 1.985(0.569) 4.84 × 10-04 1.222(0.075) 7.76 × 10-03

rs3764261 CETP 16 55550825 A C 2.596(0.730) 3.83 × 10-04 1.281(0.092) 7.15 × 10-03

rs12927205 HPR 16 70582578 A G 0.760(0.627) 2.25 × 10-01 1.188(0.088) 4.92 × 10-02

rs12453914 ABCA8 17 64650473 A C 0.938(0.561) 9.48 × 10-02 1.178(0.075) 2.85 × 10-02

rs7258950 LDLR 19 11111139 G A 2.849(0.655) 1.39 × 10-05 1.240(0.093) 2.08 × 10-02

LDL-C change and incident high levels of low-density lipoprotein cholesterol

rs7525649 PCSK9 1 55271744 T C 0.990(0.497) 4.65 × 10-02 1.139(0.110) 2.38 × 10-01

rs312949 APOB 2 21187788 C G 1.331(0.520) 1.05 × 10-02 1.214(0.107) 6.97 × 10-02

rs6871667 HMGCR 5 74640498 A G 1.322(0.470) 4.90 × 10-03 1.252(0.102) 2.68 × 10-02

rs9357121 HLA-C 6 31348458 T G 1.129(0.665) 8.94 × 10-02 1.093(0.147) 5.45 × 10-01

rs579459 ABO 9 135143989 C T 1.675(0.807) 3.81 × 10-02 1.278(0.164) 1.34 × 10-01

12

SNP IDs and chromosomal positions are based on NCBI Build 36 of the genome. CHR, chromosome.

rs12927205 HPR 16 70582578 A G 1.001(0.531) 5.95 × 10-02 1.340(0.122) 1.63 × 10-02

rs12453914 ABCA8 17 64650473 A C 0.664(0.474) 1.61 × 10-01 1.101(0.101) 3.39 × 10-01

rs7258950 LDLR 19 11111139 G A 2.464(0.552) 8.27 × 10-06 1.317(0.127) 3.02 × 10-02

HDL-C change and incident low levels of high-density lipoprotein cholesterol

rs12678919 LPL 8 19888502 A G -0.884(0.391) 2.37 × 10-02 1.294(0.134) 5.45 × 10-02

rs2575876 ABCA1 9 106705560 A G -0.847(0.271) 1.77 × 10-03 1.310(0.082) 9.14 × 10-04

rs662799 APOA1-C3-A4-A5 11 116168917 G A -1.256(0.252) 6.24 × 10-07 1.282(0.077) 1.20 × 10-03

rs1800588 LIPC 15 56510967 C T -1.246(0.225) 3.35 × 10-08 1.266(0.074) 1.38 × 10-03

rs3764261 CETP 16 55550825 C A -1.959(0.284) 5.62 × 10-12 1.452(0.100) 1.80 × 10-04

rs12970066 LIPG 18 45361150 C G -0.460(0.248) 6.36 × 10-02 1.039(0.079) 6.29 × 10-01

TG change and incident hypertriglyceridemia

rs12042319 ANGPTL3 1 62822407 G A 0.014(0.005) 2.49 × 10-03 1.118(0.071) 1.17 × 10-01

rs1260333 GCKR 2 27602128 A G 0.011(0.004) 1.91 × 10-03 1.114(0.055) 5.11 × 10-02

rs13231516 MLXIPL 7 72501185 T G 0.013(0.005) 1.23 × 10-02 1.297(0.085) 2.33 × 10-03

rs12678919 LPL 8 19888502 A G 0.030(0.006) 3.31 × 10-06 1.453(0.110) 6.40 × 10-04

rs2954029 TRIB1 8 126560154 A T 0.017(0.004) 6.72 × 10-06 1.303(0.056) 2.33 × 10-06

rs662799 APOA1-C3-A4-A5 11 116168917 G A 0.036(0.004) 4.62 × 10-18 1.629(0.060) 4.67 × 10-16

rs11066280 ALDH2 12 111302166 T A 0.008(0.005) 9.85 × 10-02 1.184(0.076) 2.64 × 10-02

rs1800588 LIPC 15 56510967 T C 0.005(0.004) 1.67 × 10-01 1.104(0.057) 8.14 × 10-02

rs157582 APOE 19 50088059 T C 0.021(0.009) 1.76 × 10-02 1.576(0.118) 1.21 × 10-04

13

Table S4. Discrimination after addition of GRS to traditional risk factors

Discrimination

Model without GRS Model with GRS C index (95%CI) C index (95%CI) P

TC GRS vs HyperTC 0.75 (0.73- 0.78) 0.76 (0.74- 0.78) 0.035

LDL-C GRS vs HyperLDL-C 0.76 (0.732- 0.795) 0.77 (0.74- 0.80) 0.122

HDL-C GRS vs LowHDL-C 0.67 (0.65- 0.70) 0.69 (0.67- 0.71) 0.005

TG GRS vs HyperTG 0.68 (0.66- 0.70) 0.70 (0.68- 0.72) 0.0002

GRS, Genetic Risk Score. HyperTC: hypercholesterolemia, HyperTG:hypertriglyceridemia, LowHDL-C: low levels of high-density lipoprotein cholesterol, HyperLDL-C: high levels of

low-density lipoprotein cholesterol

*The covariates were sex, age, BMI, and the baseline lipid levels corresponding to the genetic risk scores.

14

Table S5. The associations of novel Chinese-specific variants and lead SNPs in Europeans in GLGC datasets

Gene Note SNP LD(r2) CHR BP A1/A2 Freq.A1

in Europeans

Freq.A1

in Chinese N_TC P _TC N_LDL P_LDL N_HDL P_HDL N_TG P_TG

HLA Chinese specific SNPs rs9357121 ref 6 31348458 G/T 0.02 0.19 89534 7.29E-01 84967 6.05E-01 89252 6.54E-01 85952 5.03E-01

lead SNPs in Europeans rs3177928 0.02 6 32520413 A/G 0.18 0.05 179996 9.78E-22 165751 3.10E-17 179805 2.93E-03 170516 3.18E-03

LIPG Chinese specific SNPs rs12970066 ref 18 45361150 G/C 0.28 0.31 93067 5.79E-02 88433 4.19E-01 92820 4.77E-07 89485 2.12E-02

lead SNPs in Europeans rs7241918 0.017 18 45414951 G/T 0.19 0.09 93067 3.62E-18 88433 2.62E-04 92820 1.11E-44 89485 4.37E-01

LDLR Chinese specific SNPs rs7258950 ref 19 11111139 A/G 0.11 0.23 8867 2.02E-01 8762 2.33E-02 8868 2.33E-02 8871 6.97E-01

lead SNPs in Europeans rs6511720 0 19 11063306 T/G 0.11 0 184764 5.43E-202 170608 3.85E-262 184617 6.32E-05 175280 1.04E-01

GLGC, Global Lipids Genetics Consortium

All the LD data with r2 are obtained from 1000 Genomes data in JPT+CHB.

15

Table S6. Effects of lipid loci on established cardiovascular risk factors in replication samples

SBP DBP Hypertension BMI FPG

Gene SNP CHR BP

Code

allele

Other

allele Beta(SE) P Beta(SE) P OR(SE) P Beta(SE) P Beta(SE) P

PCSK9 rs7525649 1 55271744 T C -0.11(0.26) 6.67E-01 -0.07(0.15) 6.55E-01 1.01(0.03) 7.26E-01 0(0.04) 9.79E-01 -0.08(0.37) 8.21E-01

ANGPTL3 rs12042319 1 62822407 G A 0.37(0.31) 2.27E-01 0.04(0.17) 8.16E-01 1.02(0.03) 5.77E-01 0.03(0.05) 5.11E-01 -0.3(0.44) 4.99E-01

APOB rs312949 2 21187788 C G -0.37(0.27) 1.79E-01 -0.24(0.15) 1.20E-01 0.97(0.03) 2.77E-01 -0.05(0.05) 2.42E-01 0.49(0.39) 2.05E-01

GCKR rs1260333 2 27602128 A G -0.15(0.25) 5.48E-01 0.05(0.14) 7.26E-01 0.99(0.03) 7.42E-01 -0.05(0.04) 2.67E-01 -1.38(0.35) 7.93E-05

HMGCR rs6871667 5 74640498 A G -0.05(0.25) 8.47E-01 0.14(0.14) 2.92E-01 1(0.03) 9.46E-01 -0.07(0.04) 9.83E-02 0.24(0.35) 4.85E-01

HLA rs9357121 6 31348458 T G -0.55(0.36) 1.20E-01 -0.06(0.2) 7.72E-01 0.96(0.04) 2.12E-01 0.11(0.06) 6.43E-02 0.16(0.51) 7.46E-01

MLXIPL rs13231516 7 72501185 T G 0.2(0.36) 5.85E-01 0.24(0.2) 2.25E-01 1.05(0.04) 2.19E-01 -0.08(0.06) 2.06E-01 0(0.51) 9.98E-01

LPL rs12678919 8 19888502 A G 1.05(0.45) 1.83E-02 0.79(0.25) 1.27E-03 1.15(0.05) 2.42E-03 -0.07(0.08) 3.26E-01 -0.08(0.62) 8.96E-01

TRIB1 rs2954029 8 126560154 A T 0.13(0.25) 5.98E-01 0.03(0.14) 8.49E-01 1.01(0.03) 7.68E-01 -0.06(0.04) 1.34E-01 -0.02(0.36) 9.50E-01

ABCA1 rs2575876 9 106705560 G A 0.52(0.3) 8.75E-02 0.17(0.17) 3.07E-01 1.03(0.03) 3.41E-01 -0.05(0.05) 3.71E-01 0.11(0.43) 8.06E-01

ABO rs579459 9 135143989 C T -0.28(0.78) 7.20E-01 -0.53(0.47) 2.62E-01 0.95(0.09) 5.68E-01 -0.03(0.13) 8.37E-01 -0.64(1.17) 5.85E-01

APOA1-C3-A4-A5 rs662799 11 116168917 G A 0.2(0.27) 4.63E-01 0.11(0.15) 4.53E-01 1.03(0.03) 3.71E-01 0.05(0.05) 2.64E-01 0.12(0.39) 7.48E-01

ALDH2 rs11066280 12 111302166 T A 0.01(0.33) 9.78E-01 0.2(0.18) 2.68E-01 1.03(0.03) 4.31E-01 0.13(0.06) 2.44E-02 -0.06(0.47) 8.92E-01

LIPC rs1800588 15 56510967 T C 0.15(0.25) 5.53E-01 -0.09(0.14) 5.19E-01 0.99(0.03) 8.03E-01 0.09(0.04) 3.98E-02 0.05(0.36) 8.88E-01

CETP rs3764261 16 55550825 A C 0.04(0.33) 9.13E-01 -0.23(0.18) 2.03E-01 0.98(0.03) 5.91E-01 -0.03(0.06) 6.43E-01 0.38(0.46) 4.11E-01

HPR rs12927205 16 70582578 A G -0.29(0.28) 2.97E-01 -0.07(0.15) 6.38E-01 0.98(0.03) 5.33E-01 -0.04(0.05) 3.71E-01 0.81(0.4) 3.99E-02

ABCA8 rs12453914 17 64650473 A C 0.33(0.25) 1.88E-01 0.24(0.14) 7.75E-02 1.02(0.03) 3.91E-01 0.04(0.04) 3.83E-01 -0.75(0.35) 3.42E-02

LIPG rs12970066 18 45361150 G C -0.2(0.28) 4.65E-01 -0.09(0.15) 5.37E-01 0.96(0.03) 1.58E-01 -0.01(0.05) 8.77E-01 -0.16(0.39) 6.76E-01

LDLR rs7258950 19 11111139 G A -0.65(0.29) 2.49E-02 -0.43(0.16) 8.14E-03 0.94(0.03) 4.86E-02 0.06(0.05) 2.46E-01 0.52(0.41) 2.08E-01

APOE rs157582 19 50088059 T C -0.48(0.8) 5.51E-01 -0.46(0.48) 3.34E-01 0.96(0.09) 6.78E-01 -0.12(0.13) 3.68E-01 1.11(1.21) 3.57E-01

Information for SNP ID and chromosomal position is based on NCBI genome build 36. CHR, chromosome; FPG, fasting plasma glucose; BMI, body mass index; SBP, systolic blood

pressure; DBP, diastolic blood pressure. Hypertension was defined by the presence of SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg or self-reported of taking a medication for the treatment of

hypertension. Variables adjusted in the regression models are sex and age.

Documents

Genetic Susceptibility to Lipid Levels and Lipid Change ...circgenetics.ahajournals.org/content/circcvg/early/2015/11/18/CIRC... · of Incident Hyperlipidemia in Chinese Populations