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Exome chip meta-analysis identifies novel loci and East Asian–specific coding variants that contribute to lipid levels and coronary artery disease

Nature Genetics volume 49, pages 17221730 (2017) | Download Citation


Most genome-wide association studies have been of European individuals, even though most genetic variation in humans is seen only in non-European samples. To search for novel loci associated with blood lipid levels and clarify the mechanism of action at previously identified lipid loci, we used an exome array to examine protein-coding genetic variants in 47,532 East Asian individuals. We identified 255 variants at 41 loci that reached chip-wide significance, including 3 novel loci and 14 East Asian–specific coding variant associations. After a meta-analysis including >300,000 European samples, we identified an additional nine novel loci. Sixteen genes were identified by protein-altering variants in both East Asians and Europeans, and thus are likely to be functional genes. Our data demonstrate that most of the low-frequency or rare coding variants associated with lipids are population specific, and that examining genomic data across diverse ancestries may facilitate the identification of functional genes at associated loci.

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  1. 1.

    et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

  2. 2.

    et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  3. 3.

    et al. The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589–597 (2015).

  4. 4.

    et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am. J. Hum. Genet. 91, 823–838 (2012).

  5. 5.

    et al. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat. Genet. 43, 990–995 (2011).

  6. 6.

    et al. Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained. PLoS Genet. 9, e1003379 (2013).

  7. 7.

    et al. Risk factors and biomarkers of age-related macular degeneration. Prog. Retin. Eye Res. 54, 64–102 (2016).

  8. 8.

    , , & The potential of novel biomarkers to improve risk prediction of type 2 diabetes. Diabetologia 57, 16–29 (2014).

  9. 9.

    & Nonalcoholic fatty liver disease and lipids. Curr. Opin. Lipidol. 23, 345–352 (2012).

  10. 10.

    A PCSK9 missense variant associated with a reduced risk of early-onset myocardial infarction. N. Engl. J. Med. 358, 2299–2300 (2008).

  11. 11.

    et al. Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese. Nat. Commun. 6, 10206 (2015).

  12. 12.

    et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am. J. Hum. Genet. 94, 223–232 (2014).

  13. 13.

    et al. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am. J. Hum. Genet. 94, 233–245 (2014).

  14. 14.

    et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. (2017).

  15. 15.

    et al. Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nat. Genet. 46, 345–351 (2014).

  16. 16.

    , , , & Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

  17. 17.

    et al. Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet. 7, e1002198 (2011).

  18. 18.

    et al. A comprehensive 1,000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

  19. 19.

    et al. Cohort profile: the HUNT Study, Norway. Int. J. Epidemiol. 42, 968–977 (2013).

  20. 20.

    et al. Genetic susceptibility to lipid levels and lipid change over time and risk of incident hyperlipidemia in Chinese populations. Circ. Cardiovasc. Genet. 9, 37–44 (2016).

  21. 21.

    , & Mitochondrial calcium and the regulation of metabolism in the heart. J. Mol. Cell. Cardiol. 78, 35–45 (2015).

  22. 22.

    et al. Soluble CD163: a biomarker linking macrophages and insulin resistance. Diabetologia 55, 1856–1862 (2012).

  23. 23.

    et al. ALK7 expression is specific for adipose tissue, reduced in obesity and correlates to factors implicated in metabolic disease. Biochem. Biophys. Res. Commun. 382, 309–314 (2009).

  24. 24.

    et al. Linkage and genome-wide association analysis of obesity-related phenotypes: association of weight with the MGAT1 gene. Obesity (Silver Spring) 18, 803–808 (2010).

  25. 25.

    et al. Association analyses of East Asian individuals and transancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum. Mol. Genet. 26, 1770–1784 (2017).

  26. 26.

    et al. A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47, 955–961 (2015).

  27. 27.

    et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45, 1345–1352 (2013).

  28. 28.

    , , , & Genetics and causality of triglyceride-rich lipoproteins in atherosclerotic cardiovascular disease. J. Am. Coll. Cardiol. 64, 2525–2540 (2014).

  29. 29.

    et al. Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease. Nat. Genet. 48, 634–639 (2016).

  30. 30.

    et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380, 572–580 (2012).

  31. 31.

    et al. Rare coding variants and breast cancer risk: evaluation of susceptibility loci identified in genome-wide association studies. Cancer Epidemiol. Biomarkers Prev. 23, 622–628 (2014).

  32. 32.

    et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).

  33. 33.

    et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204 (2014).

  34. 34.

    , , , & RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30, 2828–2829 (2014).

  35. 35.

    et al. Coding-sequence variants are associated with blood lipid levels in 14,473 Chinese. Hum. Mol. Genet. 25, 4107–4116 (2016).

  36. 36.

    et al. Genome-wide association study in Han Chinese identifies four new susceptibility loci for coronary artery disease. Nat. Genet. 44, 890–894 (2012).

  37. 37.

    , & METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

  38. 38.

    GenSalt Collaborative Research Group. GenSalt: rationale, design, methods and baseline characteristics of study participants. J. Hum. Hypertens. 21, 639–646 (2007).

  39. 39.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  40. 40.

    et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

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We thank all the participants of this study for their contributions. X. Lu is supported by the CAMS Innovation Fund for Medical Sciences (grants 2016-I2M-1-009, 2017-I2M-1-004, and 2016-I2M-1-011) and the National Science Foundation of China (grants 81422043, 91439202, 81370002, 81773537, and 81230069). C.J.W. is supported by the National Institutes of Health (grant HL135824). S.K. and C.J.W. are supported by the National Institutes of Health (grant HL127564). P.C.S. was supported by the Hong Kong Research Grants Council (grants TRS T12/705/11 and GRF 17128515). G.M.P. is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (award K01HL125751). We thank P. Marshall for professional editing. Additional acknowledgments of funding sources for the primary studies are provided in Supplementary Note 1.

Author information

Author notes

    • Pak Chung Sham
    • , Dongfeng Gu
    •  & Cristen J Willer

    These authors jointly supervised this work.


  1. Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

    • Xiangfeng Lu
    • , Laiyuan Wang
    • , Jianfeng Huang
    • , Shufeng Chen
    • , Xueli Yang
    •  & Dongfeng Gu
  2. Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.

    • Xiangfeng Lu
    • , He Zhang
    • , Santhi K Ganesh
    • , Jonas Bille Nielsen
    • , Y Eugene Chen
    •  & Cristen J Willer
  3. Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA.

    • Xiangfeng Lu
    • , He Zhang
    • , Santhi K Ganesh
    •  & Cristen J Willer
  4. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.

    • Gina M Peloso
    •  & Sekar Kathiresan
  5. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.

    • Gina M Peloso
  6. Department of Public Health Sciences, Institute of Personalized Medicine, Penn State University, University Park, Pennsylvania, USA.

    • Dajiang J Liu
  7. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.

    • Ying Wu
    • , Cassandra N Spracklen
    •  & Karen L Mohlke
  8. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

    • Wei Zhou
    •  & Cristen J Willer
  9. MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China.

    • Jun Li
    • , Xuezhen Liu
    • , Kuai Yu
    • , Meian He
    •  & Tangchun Wu
  10. Department of Surgery, Li KaShing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Dr. Li Dak-Sum Research Centre, The University of Hong Kong–Karolinska Institutet Collaboration in Regenerative Medicine, Hong Kong, China.

    • Clara Sze-man Tang
  11. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.

    • Rajkumar Dorajoo
    • , Chiea Chuen Khor
    •  & Jianjun Liu
  12. Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, China.

    • Huaixing Li
    • , Liang Sun
    • , Yao Hu
    • , Yiqin Wang
    • , Feijie Wang
    •  & Xu Lin
  13. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

    • Jirong Long
    • , Qiuyin Cai
    • , Xiao-Ou Shu
    •  & Wei Zheng
  14. Institute for Translational Genomics and Population Sciences, LABioMed at Harbor–UCLA Medical Center, Los Angeles, California, USA.

    • Xiuqing Guo
    •  & Yii-Der Ida Chen
  15. Department of Cardiology, Institute of Vascular Medicine, Peking University Third Hospital, Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Beijing, China.

    • Ming Xu
    •  & Wei Gao
  16. Center for Genomic and Personalized Medicine, Medical Scientific Research Center and Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.

    • Yang Chen
    •  & Zengnan Mo
  17. Department of Cardiology, Peking University First Hospital, Beijing, China.

    • Yan Zhang
    •  & Yong Huo
  18. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore.

    • Chiea Chuen Khor
    •  & Tien Yin Wong
  19. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.

    • Chiea Chuen Khor
    • , Qiao Fan
    • , Wanting Zhao
    •  & Ching-Yu Cheng
  20. Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.

    • Yu-Tang Gao
  21. Department of Medicine, the University of Hong Kong, Hong Kong, China.

    • Chloe Yu Yan Cheung
    • , Karen Siu Ling Lam
    •  & Hung-fat Tse
  22. Community Health Center, The 3rd Affiliated Hospital of Shenzhen University, Shenzhen, China.

    • Jianfeng Huang
  23. Duke–National University of Singapore Graduate Medical School, Singapore, Singapore.

    • Qiao Fan
    • , Tien Yin Wong
    •  & E Shyong Tai
  24. Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai, China.

    • Jinxiu Shi
    •  & Wei Huang
  25. Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.

    • Wayne H-H Sheu
  26. Department of Psychiatry, the University of Hong Kong, Hong Kong, China.

    • Stacey Shawn Cherny
    •  & Pak Chung Sham
  27. Centre for Genomic Sciences, Li KaShing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

    • Stacey Shawn Cherny
    •  & Pak Chung Sham
  28. State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.

    • Stacey Shawn Cherny
    •  & Pak Chung Sham
  29. USC–Office of Population Studies Foundation, University of San Carlos, Cebu City, Philippines.

    • Alan B Feranil
  30. Department of Anthropology, Sociology, and History, University of San Carlos, Cebu City, Philippines.

    • Alan B Feranil
  31. Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.

    • Linda S Adair
    • , Penny Gordon-Larsen
    •  & Shufa Du
  32. Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA.

    • Penny Gordon-Larsen
    •  & Shufa Du
  33. USC Eye Institute, Department of Ophthalmology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.

    • Rohit Varma
  34. Research Centre of Heart, Brain, Hormone and Healthy Aging, Li KaShing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

    • Karen Siu Ling Lam
    •  & Hung-fat Tse
  35. State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China.

    • Karen Siu Ling Lam
  36. Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.

    • Tien Yin Wong
    •  & E Shyong Tai
  37. Department of Ophthalmology, National University of Singapore, Singapore, Singapore.

    • Tien Yin Wong
    •  & Ching-Yu Cheng
  38. HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway.

    • Kristian Hveem
    •  & Lars G Fritsche
  39. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim, Norway.

    • Kristian Hveem
    •  & Lars G Fritsche
  40. Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.

    • Kristian Hveem
  41. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

    • Lars G Fritsche
    •  & Goncalo Abecasis
  42. Hong Kong–Guangdong Joint Laboratory on Stem Cell and Regenerative Medicine, the University of Hong Kong, Hong Kong, China.

    • Hung-fat Tse
  43. Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore.

    • Ching-Yu Cheng
  44. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore.

    • E Shyong Tai
  45. Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Sekar Kathiresan


  1. GLGC Consortium

    A full list of members and affiliations appears in Supplementary Note 1.


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X. Lu, C.J.W., G.M.P., D.J.L., D.G., and K.L.M. drafted the manuscript. C.J.W., D.G., X. Lu, P.C.S., S.K., K.L.M., and Y.E.C. coordinated the project. X. Lu, D.J.L., G.M.P., and H.Z. served as the central meta-analysis group. X. Lu and J.B.N. carried out eQTL analysis. X. Lu and W. Zhou carried out DeltaSVM analysis. X. Lu, G.M.P., D.J.L., Y. Wu, H.Z., J. Li, C.S.T., R.D., J. Long, X.G., C.N.S., Y.C., Y. Wang, C.Y.Y.C, Q.F., J.S., X.Y., W. Zhao, M.H., and J.B.N. carried out cohort data analysis. W. Zhou, H.L., C.C.K., J. Liu, L.W., F.W., J.S., and W.H. carried out cohort genotyping. H.L., M.X., X. Liu, Y.Z., L.S., Y.G., Y. Hu, K.Y., J.H., Q.C., S.C., A.B.F., L.S.A., P.G.-L., S.D., K.H., and L.G.F. carried out cohort phenotyping. X. Lu, W.H.-H.S., S.S.C., A.B.F., L.S.A., P.G.-L., S.D., R.V., Y.-D.I.C., X.-O.S., K.S.L.L., T.Y.W., S.K.G., Z.M., K.H., L.G.F., H.T., Y. Huo, C.Y.C., Y.E.C., W. Zheng, E.S.T., W.G., X. Lin, W.H., G.A., S.K., K.L.M., T.W., P.C.S., D.G., and C.J.W. were the principal investigators for the cohort.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Pak Chung Sham or Dongfeng Gu or Cristen J Willer.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8, Supplementary Tables 1, 2, 4–10, 12,13 and Supplementary Note

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 3

    Association summary statistics at 38 previously known loci where lead variants reached exome-wide significance

  2. 2.

    Supplementary Table 11

    The association of 363 independent variants in the known loci identified by GLGC exome chip study in the East Asian samples

  3. 3.

    Supplementary Table 14

    Studies contributing to East Asian meta-analysis

  4. 4.

    Supplementary Table 15

    Descriptive statistics for lipid levels across GLGC exome contributing studies

  5. 5.

    Supplementary Table 16

    Contributing studies genotyping and analysis information

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