Article | Published:

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

Nature Geneticsvolume 50pages15141523 (2018) | Download Citation


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).

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

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

  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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  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).

  7. 7.

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

  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).

  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).

  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).

  11. 11.

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

  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).

  13. 13.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  21. 21.

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

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  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).

  27. 27.

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

  28. 28.

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

  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).

  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).

  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).

  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).

  33. 33.

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

  34. 34.

    Diogo, D. et al. Phenome-wide association studies (PheWAS) across large “real-world data” population cohorts support drug target validation. Preprint at (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).

  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).

  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).

  39. 39.

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

  40. 40.

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

  41. 41.

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

  42. 42.

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

  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).

  44. 44.

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

  45. 45.

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

  46. 46.

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

  47. 47.

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

  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).

  49. 49.

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

  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).

  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).

  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).

  53. 53.

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

  54. 54.

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

  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).

  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).

  57. 57.

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

  58. 58.

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

  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).

  60. 60.

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

Download references


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.

Author information

Author notes

  1. These authors contributed equally: Derek Klarin, Scott M. Damrauer.

  2. These authors jointly supervised: Christopher J. O’Donnell, Philip S. Tsao, Sekar Kathiresan, Daniel J. Rader, Peter W. F. Wilson, Themistocles L. Assimes.

  3. A list of members and affiliations appears in the Supplementary Note.


  1. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    • Derek Klarin
    • , Connor A. Emdin
    • , Pradeep Natarajan
    • , Amit V. Khera
    •  & Sekar Kathiresan
  2. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Derek Klarin
    • , Mark Chaffin
    • , Connor A. Emdin
    • , Pradeep Natarajan
    • , Benjamin M. Neale
    • , Amit V. Khera
    •  & Sekar Kathiresan
  3. Boston VA Healthcare System, Boston, MA, USA

    • Derek Klarin
  4. Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA

    • Scott M. Damrauer
    • , Aeron M. Small
    • , Danish Saleheen
    • , Marijana Vujkovic
    •  & Kyong-Mi Chang
  5. Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Scott M. Damrauer
  6. Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA

    • Kelly Cho
    • , Jacqueline Honerlaw
    • , David R. Gagnon
    • , Jie Huang
    • , Yuk-Lam Ho
    • , Jennifer E. Huffman
    • , Saiju Pyarajan
    • , J. Michael Gaziano
    •  & Christopher J. O’Donnell
  7. Department of Epidemiology, Rollins School of Public Health, Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA

    • Yan V. Sun
  8. Regeneron Genetics Center, Tarrytown, NY, USA

    • Tanya M. Teslovich
    • , Alexander H. Li
    • , Aris Baras
    •  & Frederick E. Dewey
  9. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    • David R. Gagnon
    •  & Gina M. Peloso
  10. VA Salt Lake City Health Care System, Salt Lake City, UT, USA

    • Scott L. DuVall
    •  & Julie A. Lynch
  11. Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA

    • Scott L. DuVall
  12. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    • Jin Li
    • , Jennifer S. Lee
    • , Philip S. Tsao
    •  & Themistocles L. Assimes
  13. VA Palo Alto Health Care System, Palo Alto, CA, USA

    • Jin Li
    • , Jennifer S. Lee
    • , Philip S. Tsao
    •  & Themistocles L. Assimes
  14. Department of Medicine, Yale School of Medicine, New Haven, CT, USA

    • Aeron M. Small
    •  & John Concato
  15. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    • Hua Tang
  16. University of Massachusetts College of Nursing and Health Sciences, Boston, MA, USA

    • Julie A. Lynch
  17. Department of Public Health Sciences, Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA

    • Dajiang J. Liu
  18. Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    • Pradeep Natarajan
  19. Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    • Rajiv Chowdhury
    • , Emanuele Di Angelantonio
    •  & John Danesh
  20. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Danish Saleheen
    •  & Marijana Vujkovic
  21. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    • Saiju Pyarajan
    •  & J. Michael Gaziano
  22. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    • Benjamin M. Neale
  23. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Benjamin M. Neale
  24. Initiative for Noncommunicable Diseases, Health Systems and Population Studies Division, International Centre for Diarrheal Disease Research, Dhaka, Bangladesh

    • Aliya Naheed
  25. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Kyong-Mi Chang
    •  & Daniel J. Rader
  26. Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    • Gonçalo Abecasis
  27. Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA

    • Cristen Willer
  28. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    • Cristen Willer
  29. Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA

    • Cristen Willer
  30. Geisinger Health System, Danville, PA, USA

    • David J. Carey
  31. Clinical Epidemiology Research Center, VA Connecticut Healthcare System, West Haven, CT, USA

    • John Concato
  32. Department of Medicine, Harvard Medical School, Boston, MA, USA

    • J. Michael Gaziano
    • , Christopher J. O’Donnell
    •  & Daniel J. Rader
  33. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Daniel J. Rader
  34. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Daniel J. Rader
  35. Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Daniel J. Rader
  36. Atlanta VA Medical Center, Decatur, GA, USA

    • Peter W. F. Wilson
  37. Emory Clinical Cardiovascular Research Institute, Atlanta, GA, USA

    • Peter W. F. Wilson


  1. Search for Derek Klarin in:

  2. Search for Scott M. Damrauer in:

  3. Search for Kelly Cho in:

  4. Search for Yan V. Sun in:

  5. Search for Tanya M. Teslovich in:

  6. Search for Jacqueline Honerlaw in:

  7. Search for David R. Gagnon in:

  8. Search for Scott L. DuVall in:

  9. Search for Jin Li in:

  10. Search for Gina M. Peloso in:

  11. Search for Mark Chaffin in:

  12. Search for Aeron M. Small in:

  13. Search for Jie Huang in:

  14. Search for Hua Tang in:

  15. Search for Julie A. Lynch in:

  16. Search for Yuk-Lam Ho in:

  17. Search for Dajiang J. Liu in:

  18. Search for Connor A. Emdin in:

  19. Search for Alexander H. Li in:

  20. Search for Jennifer E. Huffman in:

  21. Search for Jennifer S. Lee in:

  22. Search for Pradeep Natarajan in:

  23. Search for Rajiv Chowdhury in:

  24. Search for Danish Saleheen in:

  25. Search for Marijana Vujkovic in:

  26. Search for Aris Baras in:

  27. Search for Saiju Pyarajan in:

  28. Search for Emanuele Di Angelantonio in:

  29. Search for Benjamin M. Neale in:

  30. Search for Aliya Naheed in:

  31. Search for Amit V. Khera in:

  32. Search for John Danesh in:

  33. Search for Kyong-Mi Chang in:

  34. Search for Gonçalo Abecasis in:

  35. Search for Cristen Willer in:

  36. Search for Frederick E. Dewey in:

  37. Search for David J. Carey in:

  38. Search for John Concato in:

  39. Search for J. Michael Gaziano in:

  40. Search for Christopher J. O’Donnell in:

  41. Search for Philip S. Tsao in:

  42. Search for Sekar Kathiresan in:

  43. Search for Daniel J. Rader in:

  44. Search for Peter W. F. Wilson in:

  45. Search for Themistocles L. Assimes in:


  1. Global Lipids Genetics Consortium

    1. Myocardial Infarction Genetics (MIGen) Consortium

      1. The Geisinger-Regeneron DiscovEHR Collaboration

        1. The VA Million Veteran Program


          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.

          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.

          Corresponding author

          Correspondence to Themistocles L. Assimes.

          Supplementary information

          1. Supplementary Text and Figures

            Supplementary Figures 1–17 and Supplementary Note

          2. Reporting Summary

          3. Supplementary Tables 1–4

          4. Supplementary Tables 5–8

          5. Supplementary Table 9

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

          6. 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

          7. Supplementary Table 11

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

          8. 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)

          9. Supplementary Tables 13–16

          10. Supplementary Table 17

            Transcriptome-wide association study

          11. Supplementary Table 18

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

          12. Supplementary Table 19

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

          13. Supplementary Table 20

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

          14. 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

          15. 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

          16. Supplementary Table 23

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

          17. Supplementary Tables 24–29

          18. Supplementary Table 30

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

          About this article

          Publication history