Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program

Abstract

The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of US military veterans. Here we genotyped 312,571 MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least one blood lipid measurement, including 57,332 black and 24,743 Hispanic participants, we tested up to around 32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total n > 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes) and PDE3B (triglycerides and coronary disease).

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Fig. 1: GWAS study design.
Fig. 2: Comparison of 354 independent lipid-associated variants across ethnicities.
Fig. 3: PDE3B loss of gene function, lipids and coronary disease.
Fig. 4: ANGPTL4 40Lys carrier disease associations.
Fig. 5: PCSK9 46Leu carrier disease associations.
Fig. 6: Lipid associations with abdominal aortic aneurysm.

Data availability

The full summary-level association data from the trans-ancestry meta-analysis for each lipid trait from this report are available through dbGaP, with accession number phs001672.v1.p1.

References

  1. 1.

    Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).

    Article  Google Scholar 

  2. 2.

    Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    Article  Google Scholar 

  3. 3.

    The Emerging Risk Factors Collaboration. Major lipids, apolipoproteins, and risk of vascular disease. J. Am. Med. Assoc. 302, 1993–2000 (2009).

    Article  Google Scholar 

  4. 4.

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

    CAS  Article  Google Scholar 

  5. 5.

    Global Lipids Genetics Consortium.. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    Article  Google Scholar 

  6. 6.

    Chasman, D. I. et al. Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet. 5, e1000730 (2009).

    Article  Google Scholar 

  7. 7.

    Albrechtsen, A. et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (2013).

    CAS  Article  Google Scholar 

  8. 8.

    Peloso, G. M. 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).

    CAS  Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  10. 10.

    Below, J. E. et al. Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects, and tissue-specific enrichment of eQTLs. Sci. Rep. 6, 19429 (2016).

    CAS  Article  Google Scholar 

  11. 11.

    Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).

    CAS  Article  Google Scholar 

  12. 12.

    Lu, X. et al. Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat. Genet. 49, 1722–1730 (2017).

    CAS  Article  Google Scholar 

  13. 13.

    Sabatine, M. S. et al. Evolocumab and clinical outcomes in patients with cardiovascular disease. N. Engl. J. Med. 376, 1713–1722 (2017).

    CAS  Article  Google Scholar 

  14. 14.

    Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N. Engl. J. Med. 374, 1134–1144 (2016).

    Article  Google Scholar 

  15. 15.

    Dewey, F. E. et al. Inactivating variants in ANGPTL4 and risk of coronary artery disease. N. Engl. J. Med. 374, 1123–1133 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Barter, P. J. et al. Effects of torcetrapib in patients at high risk for coronary events. N. Engl. J. Med. 357, 2109–2122 (2007).

    CAS  Article  Google Scholar 

  17. 17.

    Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1111 (2013).

    CAS  Article  Google Scholar 

  18. 18.

    The TG and HDL Working Group of the Exome Sequencing Project, National Heart, Lung, and Blood Institute.. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N. Engl. J. Med. 371, 22–31 (2014).

    Article  Google Scholar 

  19. 19.

    Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr. & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).

    CAS  Article  Google Scholar 

  20. 20.

    Abul-Husn, N. S. et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 354, aaf7000 (2016).

    Article  Google Scholar 

  21. 21.

    The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  22. 22.

    Tishkoff, S. A. et al. The genetic structure and history of Africans and African Americans. Science 324, 1035–1044 (2009).

    CAS  Article  Google Scholar 

  23. 23.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  Article  Google Scholar 

  24. 24.

    Wright, F. A. et al. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 46, 430–437 (2014).

    CAS  Article  Google Scholar 

  25. 25.

    GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  26. 26.

    Mancuso, N. et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  Article  Google Scholar 

  28. 28.

    Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

    CAS  Article  Google Scholar 

  29. 29.

    McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

    CAS  Article  Google Scholar 

  30. 30.

    Khera, A. V. et al. Association of rare and common variation in the lipoprotein lipase gene with coronary artery disease. J. Am. Med. Assoc. 317, 937–946 (2017).

    CAS  Article  Google Scholar 

  31. 31.

    Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).

    Article  Google Scholar 

  32. 32.

    Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281 (2015).

    CAS  Article  Google Scholar 

  33. 33.

    Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

    CAS  Article  Google Scholar 

  34. 34.

    Diogo, D. et al. Phenome-wide association studies (PheWAS) across large “real-world data” population cohorts support drug target validation. Preprint at https://www.biorxiv.org/content/early/2017/11/13/218875 (2017).

  35. 35.

    Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018).

    CAS  Article  Google Scholar 

  36. 36.

    Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  Google Scholar 

  37. 37.

    Klarin, D. et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat. Genet. 49, 1392–1397 (2017).

  38. 38.

    Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).

    CAS  Article  Google Scholar 

  39. 39.

    Gandotra, S. et al. Perilipin deficiency and autosomal dominant partial lipodystrophy. N. Engl. J. Med. 364, 740–748 (2011).

    CAS  Article  Google Scholar 

  40. 40.

    Rani, J. et al. T2DiACoD: a gene atlas of type 2 diabetes mellitus associated complex disorders. Sci. Rep. 7, 6892 (2017).

    Article  Google Scholar 

  41. 41.

    Musunuru, K. et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N. Engl. J. Med. 363, 2220–2227 (2010).

    CAS  Article  Google Scholar 

  42. 42.

    Graham, M. J. et al. Cardiovascular and metabolic effects of ANGPTL3 antisense oligonucleotides. N. Engl. J. Med. 377, 222–232 (2017).

    CAS  Article  Google Scholar 

  43. 43.

    Zhang, W. & Colman, R. W. Thrombin regulates intracellular cyclic AMP concentration in human platelets through phosphorylation/activation of phosphodiesterase 3A. Blood 110, 1475–1482 (2007).

    CAS  Article  Google Scholar 

  44. 44.

    Maass, P. G. et al. PDE3A mutations cause autosomal dominant hypertension with brachydactyly. Nat. Genet. 47, 647–653 (2015).

    CAS  Article  Google Scholar 

  45. 45.

    Vandeput, F. et al. Selective regulation of cyclic nucleotide phosphodiesterase PDE3A isoforms. Proc. Natl Acad. Sci. USA 110, 19778–19783 (2013).

    CAS  Article  Google Scholar 

  46. 46.

    Bedenis, R. et al. Cilostazol for intermittent claudication. Cochrane Database Syst. Rev. 10, CD003748 (2014).

    Google Scholar 

  47. 47.

    Tsuchikane, E. et al. Impact of cilostazol on restenosis after percutaneous coronary balloon angioplasty. Circulation 100, 21–26 (1999).

    CAS  Article  Google Scholar 

  48. 48.

    Shinohara, Y. et al. Cilostazol for prevention of secondary stroke (CSPS 2): an aspirin-controlled, double-blind, randomised non-inferiority trial. Lancet Neurol. 9, 959–968 (2010).

    CAS  Article  Google Scholar 

  49. 49.

    Ahmad, F. et al. Phosphodiesterase 3B (PDE3B) regulates NLRP3 inflammasome in adipose tissue. Sci. Rep. 6, 28056 (2016).

    CAS  Article  Google Scholar 

  50. 50.

    Chung, Y. W. et al. Targeted disruption of PDE3B, but not PDE3A, protects murine heart from ischemia/reperfusion injury. Proc. Natl Acad. Sci. USA 112, E2253–E2262 (2015).

    CAS  Article  Google Scholar 

  51. 51.

    Harrison, S. C. et al. Genetic association of lipids and lipid drug targets with abdominal aortic aneurysm: a meta-analysis. JAMA Cardiol. 3, 26–33 (2018).

    Article  Google Scholar 

  52. 52.

    Lu, H. et al. Hypercholesterolemia induced by a PCSK9 gain-of-function mutation augments angiotensin II-induced abdominal aortic aneurysms in C57BL/6 mice—brief report. Arterioscler. Thromb. Vasc. Biol. 36, 1753–1757 (2016).

    CAS  Article  Google Scholar 

  53. 53.

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

    CAS  Article  Google Scholar 

  54. 54.

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

    CAS  Article  Google Scholar 

  55. 55.

    Loh, P. R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    CAS  Article  Google Scholar 

  56. 56.

    Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  Article  Google Scholar 

  57. 57.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  Article  Google Scholar 

  58. 58.

    Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    Article  Google Scholar 

  59. 59.

    Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    CAS  Article  Google Scholar 

  60. 60.

    Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

    CAS  Article  Google Scholar 

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Acknowledgements

Data on patients with coronary artery disease and myocardial infarctions have been contributed by the CARDIoGRAMplusC4D investigators and the Myocardial Infarction Genetics and CARDIoGRAM Exome investigators. Both datasets were obtained online (see URLs). This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration, and was supported by the Department of Veterans Affairs Cooperative Studies Program award G002. This research was also supported by three additional Department of Veterans Affairs awards (1I0101BX003340, 1I01BX003362, and 1I01CX001025) and the NIH (T32 HL007734, K01HL125751, R01HL127564). The content of this manuscript does not represent the views of the Department of Veterans Affairs or the United States Government.

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Contributions

Concept and design: D.K., T.L.A., S.M.D., K.C., K.-M.C., P.S.T., S.K., D.J.R., P.W.F.W., J.C. and J.M.G. Acquisition, analysis or interpretation of data: D.K., S.M.D., Y.V.S., K.C., T.M.T., J.Ho., D.R.G., S.L.D., J.L., G.M.P., M.C., A.M.S., J.Hu., H.T., J.S.L., Y.-L.H., D.J.L., C.A.E., A.H.L., J.A.L., R.C., P.N., D.S., M.V., A.B., S.P., E.D.A., B.M.N., A.N., A.V.K., J.D., K.-M.C., G.A., C.W., F.E.D., J.E.H. and D.J.C. Drafting of the manuscript: D.K. and T.L.A. Critical revision of the manuscript for important intellectual content: S.M.D., Y.V.S., K.C., P.N., C.W., J.A.L., F.E.D., S.L.D., K.-M.C., C.J.O., P.S.T., S.K., D.J.R. and P.W.W. Administrative, technical or material support: D.K., Y.V.S., K.C., J.Ho., D.R.G., S.L.D., J.A.L., Y.H., J.C., J.M.G., C.J.O., P.S.T, J.E.H., and P.W.W.

Corresponding author

Correspondence to Themistocles L. Assimes.

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Competing interests

S.K. reports grant support from Regeneron and Bayer, grant support and personal fees from Aegerion, personal fees from Regeneron Genetics Center, Merck, Celera, Novartis, Bristol-Myers Squibb, Sanofi, AstraZeneca, Alnylam, Eli Lilly and Leerink Partners, personal fees and other support from Catabasis, and other support from San Therapeutics outside the submitted work. He is also the chair of the scientific advisory board at Genomics Plc. T.M.T., A.H.L., A.B., F.E.D. and D.J.C. are employees of Regeneron Pharmaceuticals. G.A. has received consulting income from Regeneron Genetics Center, 23andMe and Helix. S.L.D. has received research grant support from the following for-profit companies through the University of Utah or the Western Institute for Biomedical Research (VA Salt Lake City’s affiliated non-profit): AbbVie Inc., Anolinx LLC, Astellas Pharma Inc., AstraZeneca Pharmaceuticals LP, Boehringer Ingelheim International GmbH, Celgene Corporation, Eli Lilly and Company, Genentech Inc., Genomic Health Inc., Gilead Sciences Inc., GlaxoSmithKline PLC, Innocrin Pharmaceuticals Inc., Janssen Pharmaceuticals Inc., Kantar Health, Myriad Genetic Laboratories Inc., Novartis International AG and PAREXEL International Corporation.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17 and Supplementary Note

Reporting Summary

Supplementary Tables 1–4

Supplementary Tables 5–8

Supplementary Table 9

826 independent genome-wide lipid variants (across 1,014 associations) identified with GCTA-COJO software

Supplementary Table 10

Variance explained for 444 previously mapped independent exome-wide variants, 118 novel loci identified in this study, and 826 independent lipid genome-wide variants identified on conditional analysis in this study

Supplementary Table 11

223 variants (across 223 distinct loci) used for weighted genetic risk score

Supplementary Table 12

Increase in variance explained as a function of the number of repeated measures in MVP non-Hispanic whites (for a fixed sample size of 171,314 MVP participants; only individuals with five or more measures were included)

Supplementary Tables 13–16

Supplementary Table 17

Transcriptome-wide association study

Supplementary Table 18

Black-specific novel low-frequency protein-altering variants associated with lipids

Supplementary Table 19

Hispanic-specific novel low-frequency protein-altering variants associated with lipids

Supplementary Table 20

Genome-wide significant pLOF variants for lipids in the MVP discovery analysis

Supplementary Table 21

Exemplar list of 47 rare damaging mutations in PDE3B from Myocardial Infarction Genetics Consortium exome sequencing data used for coronary artery disease analysis

Supplementary Table 22

Statistically significant (P < 4.98 × 10–5) phenome-wide association results for DNA sequence variants within genes targeted by lipid-lowering medicines

Supplementary Table 23

Association of 223-variant lipid genetic risk score with abdominal aortic aneurysm (AAA) risk

Supplementary Tables 24–29

Supplementary Table 30

CAD association statistics for 118 novel genome-wide significant loci in the MVP lipids GWAS

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Klarin, D., Damrauer, S.M., Cho, K. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet 50, 1514–1523 (2018). https://doi.org/10.1038/s41588-018-0222-9

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