1932

Abstract

Polygenic scores quantify inherited risk by integrating information from many common sites of DNA variation into a single number. Rapid increases in the scale of genetic association studies and new statistical algorithms have enabled development of polygenic scores that meaningfully measure—as early as birth—risk of coronary artery disease. These newer-generation polygenic scores identify up to 8% of the population with triple the normal risk based on genetic variation alone, and these individuals cannot be identified on the basis of family history or clinical risk factors alone. For those identified with increased genetic risk, evidence supports risk reduction with at least two interventions, adherence to a healthy lifestyle and cholesterol-lowering therapies, that can substantially reduce risk. Alongside considerable enthusiasm for the potential of polygenic risk estimation to enable a new era of preventive clinical medicine is recognition of a need for ongoing research into how best to ensure equitable performance across diverse ancestries, how and in whom to assess the scores in clinical practice, as well as randomized trials to confirm clinical utility.

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2023-01-27
2024-04-13
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Literature Cited

  1. 1.
    Roth GA, Abate D, Abate KH et al. 2018. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392:101591736–88
    [Google Scholar]
  2. 2.
    White PD. 1957. Genes, the heart and destiny. N. Engl. J. Med. 256:21965–69
    [Google Scholar]
  3. 3.
    Gertler MM, Garn SM, White PD. 1951. Young candidates for coronary heart disease. JAMA 147:7621–25
    [Google Scholar]
  4. 4.
    Russek HI, Zohman BL. 1958. Relative significance of heredity, diet and occupational stress in coronary heart disease of young adults; based on an analysis of 100 patients between the ages of 25 and 40 years and a similar group of 100 normal control subjects. Am. J. Med. Sci. 235:3266–77
    [Google Scholar]
  5. 5.
    Lloyd-Jones DM, Nam B-H, D'Agostino RB et al. 2004. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA 291:182204–11
    [Google Scholar]
  6. 6.
    Marenberg ME, Risch N, Berkman LF et al. 1994. Genetic susceptibility to death from coronary heart disease in a study of twins. N. Engl. J. Med. 330:151041–46
    [Google Scholar]
  7. 7.
    Wienke A, Holm NV, Skytthe A, Yashin AI. 2001. The heritability of mortality due to heart diseases: a correlated frailty model applied to Danish twins. Twin Res. 4:4266–74
    [Google Scholar]
  8. 8.
    Zdravkovic S, Wienke A, Pedersen NL et al. 2002. Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J. Intern. Med. 252:3247–54
    [Google Scholar]
  9. 9.
    Lehrman MA, Schneider WJ, Südhof TC et al. 1985. Mutation in LDL receptor: Alu-Alu recombination deletes exons encoding transmembrane and cytoplasmic domains. Science 227:4683140–46
    [Google Scholar]
  10. 10.
    Soria LF, Ludwig EH, Clarke HR et al. 1989. Association between a specific apolipoprotein B mutation and familial defective apolipoprotein B-100. PNAS 86:2587–91
    [Google Scholar]
  11. 11.
    Brown MS, Goldstein JL. 1986. A receptor-mediated pathway for cholesterol homeostasis. Science 232:474634–47
    [Google Scholar]
  12. 12.
    Khera AV, Chaffin M, Aragam KG et al. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50:91219–24
    [Google Scholar]
  13. 13.
    Khera AV, Chaffin M, Zekavat SM et al. 2019. Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction. Circulation 139:131593–602
    [Google Scholar]
  14. 14.
    Patel AP, Wang M, Fahed AC et al. 2020. Association of rare pathogenic DNA variants for familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and Lynch syndrome with disease risk in adults according to family history. JAMA Netw. Open 3:4e203959
    [Google Scholar]
  15. 15.
    Aragam KG, Jiang T, Goel A et al. 2021. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. medRxiv 2021.05.24.21257377
    [Google Scholar]
  16. 16.
    Samani NJ, Erdmann J, Hall AS et al. 2007. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357:5443–53
    [Google Scholar]
  17. 17.
    Schunkert H, König IR, Kathiresan S et al. 2011. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43:4333–38
    [Google Scholar]
  18. 18.
    CARDIoGRAMplusC4D Consortium, Deloukas P, Kanoni S et al. 2013. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45:125–33
    [Google Scholar]
  19. 19.
    Henderson CR. 1975. Use of relationships among sires to increase accuracy of sire evaluation. J. Dairy Sci. 58:111731–38
    [Google Scholar]
  20. 20.
    Hazel LN. 1943. The genetic basis for constructing selection indexes. Genetics 28:6476–90
    [Google Scholar]
  21. 21.
    Altshuler D, Daly MJ, Lander ES. 2008. Genetic mapping in human disease. Science 322:5903881–88
    [Google Scholar]
  22. 22.
    Euesden J, Lewis CM, O'Reilly PF. 2015. PRSice: polygenic risk score software. Bioinformatics 31:91466–68
    [Google Scholar]
  23. 23.
    Choi SW, Mak TS-H, O'Reilly PF 2020. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15:92759–72
    [Google Scholar]
  24. 24.
    Vilhjálmsson BJ, Yang J, Finucane HK et al. 2015. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97:4576–92
    [Google Scholar]
  25. 25.
    Mak TSH, Porsch RM, Choi SW et al. 2017. Polygenic scores via penalized regression on summary statistics. Genet. Epidemiol. 41:6469–80
    [Google Scholar]
  26. 26.
    Ge T, Chen C-Y, Ni Y et al. 2019. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10:11776
    [Google Scholar]
  27. 27.
    Newcombe PJ, Nelson CP, Samani NJ et al. 2019. A flexible and parallelizable approach to genome-wide polygenic risk scores. Genet. Epidemiol. 43:7730–41
    [Google Scholar]
  28. 28.
    Lloyd-Jones LR, Zeng J, Sidorenko J et al. 2019. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10:15086
    [Google Scholar]
  29. 29.
    Abraham G, Havulinna AS, Bhalala OG et al. 2016. Genomic prediction of coronary heart disease. Eur. Heart J. 37:433267–78
    [Google Scholar]
  30. 30.
    Ripatti S, Tikkanen E, Orho-Melander M et al. 2010. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376:97501393–400
    [Google Scholar]
  31. 31.
    Inouye M, Abraham G, Nelson CP et al. 2018. Genomic risk prediction of coronary artery disease in 480,000 adults. J. Am. Coll. Cardiol. 72:161883–93
    [Google Scholar]
  32. 32.
    Ganna A, Magnusson PKE, Pedersen NL et al. 2013. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler. Thromb. Vasc. Biol. 33:92267–72
    [Google Scholar]
  33. 33.
    Tada H, Melander O, Louie JZ et al. 2016. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37:6561–67
    [Google Scholar]
  34. 34.
    Patel RS, Sun YV, Hartiala J et al. 2012. Association of a genetic risk score with prevalent and incident myocardial infarction in subjects undergoing coronary angiography. Circ. Cardiovasc. Genet. 5:4441–49
    [Google Scholar]
  35. 35.
    Tikkanen E, Havulinna AS, Palotie A et al. 2013. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 33:92261–66
    [Google Scholar]
  36. 36.
    Thanassoulis G. 2019. Using genetics to plan future randomized trials of lipoprotein(a) lowering—how much reduction, for how long, and in whom?. JAMA Cardiol. 4:6513–14
    [Google Scholar]
  37. 37.
    Hughes MF, Saarela O, Stritzke J et al. 2012. Genetic markers enhance coronary risk prediction in men: the MORGAM prospective cohorts. PLOS ONE 7:7e40922
    [Google Scholar]
  38. 38.
    de Vries PS, Kavousi M, Ligthart S et al. 2015. Incremental predictive value of 152 single nucleotide polymorphisms in the 10-year risk prediction of incident coronary heart disease: the Rotterdam Study. Int. J. Epidemiol. 44:2682–88
    [Google Scholar]
  39. 39.
    Krarup NT, Borglykke A, Allin KH et al. 2015. A genetic risk score of 45 coronary artery disease risk variants associates with increased risk of myocardial infarction in 6041 Danish individuals. Atherosclerosis 240:2305–10
    [Google Scholar]
  40. 40.
    Weijmans M, de Bakker PIW, van der Graaf Y et al. 2015. Incremental value of a genetic risk score for the prediction of new vascular events in patients with clinically manifest vascular disease. Atherosclerosis 239:2451–58
    [Google Scholar]
  41. 41.
    Hu Y, Lu Q, Liu W et al. 2017. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genet. 13:6e1006836
    [Google Scholar]
  42. 42.
    Hu Y, Lu Q, Powles R et al. 2017. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Comput. Biol. 13:6e1005589
    [Google Scholar]
  43. 43.
    Márquez-Luna C, Gazal S, Loh P-R et al. 2021. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nat. Commun. 12:16052
    [Google Scholar]
  44. 44.
    Weissbrod O, Hormozdiari F, Benner C et al. 2020. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52:121355–63
    [Google Scholar]
  45. 45.
    Chen W, McDonnell SK, Thibodeau SN et al. 2016. Incorporating functional annotations for fine-mapping causal variants in a Bayesian framework using summary statistics. Genetics 204:3933–58
    [Google Scholar]
  46. 46.
    Finucane HK, Bulik-Sullivan B, Gusev A et al. 2015. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47:111228–35
    [Google Scholar]
  47. 47.
    Kichaev G, Yang W-Y, Lindstrom S et al. 2014. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLOS Genet. 10:10e1004722
    [Google Scholar]
  48. 48.
    Chung W, Chen J, Turman C et al. 2019. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat. Commun. 10:1569
    [Google Scholar]
  49. 49.
    Turley P, Walters RK, Maghzian O et al. 2018. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50:2229–37
    [Google Scholar]
  50. 50.
    Márquez-Luna C, Loh P-R, South Asian Type 2 Diabetes (SAT2D) Consort., et al. 2017. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41:8811–23
    [Google Scholar]
  51. 51.
    Amariuta T, Ishigaki K, Sugishita H et al. 2020. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52:121346–54
    [Google Scholar]
  52. 52.
    Maier RM, Zhu Z, Lee SH et al. 2018. Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat. Commun. 9:1989
    [Google Scholar]
  53. 53.
    Weissbrod O, Kanai M, Shi H et al. 2022. Leveraging fine-mapping and non-European training data to improve trans-ethnic polygenic risk scores. Nat. Genet. 54:4450–58
    [Google Scholar]
  54. 54.
    Wand H, Lambert SA, Tamburro C et al. 2021. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591:7849211–19
    [Google Scholar]
  55. 55.
    Adeyemo A, Balaconis MK, Darnes DR et al. 2021. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat. Med. 27:111876–84
    [Google Scholar]
  56. 56.
    Lambert SA, Gil L, Jupp S et al. 2021. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53:4420–25
    [Google Scholar]
  57. 57.
    Carey DJ, Fetterolf SN, Davis FD et al. 2016. The Geisinger MyCode Community Health Initiative: an electronic health record-linked biobank for precision medicine research. Genet. Med. 18:9906–13
    [Google Scholar]
  58. 58.
    Chen Z, Chen J, Collins R et al. 2011. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int. J. Epidemiol. 40:61652–66
    [Google Scholar]
  59. 59.
    Gaziano JM, Concato J, Brophy M et al. 2016. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70:214–23
    [Google Scholar]
  60. 60.
    Karlson EW, Boutin NT, Hoffnagle AG et al. 2016. Building the Partners HealthCare Biobank at Partners Personalized Medicine: informed consent, return of research results, recruitment lessons and operational considerations. J. Pers. Med. 6:12
    [Google Scholar]
  61. 61.
    Leitsalu L, Haller T, Esko T et al. 2015. Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44:41137–47
    [Google Scholar]
  62. 62.
    Pulley J, Clayton E, Bernard GR et al. 2010. Principles of human subjects protections applied in an opt-out, de-identified biobank. Clin. Transl. Sci. 3:142–48
    [Google Scholar]
  63. 63.
    Bycroft C, Freeman C, Petkova D et al. 2018. The UK Biobank resource with deep phenotyping and genomic data. Nature 562:7726203–9
    [Google Scholar]
  64. 64.
    Sudlow C, Gallacher J, Allen N et al. 2015. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 12:3e1001779
    [Google Scholar]
  65. 65.
    Aragam KG, Dobbyn A, Judy R et al. 2020. Limitations of contemporary guidelines for managing patients at high genetic risk of coronary artery disease. J. Am. Coll. Cardiol. 75:222769–80
    [Google Scholar]
  66. 66.
    Hindy G, Aragam KG, Ng K et al. 2020. Genome-wide polygenic score, clinical risk factors, and long-term trajectories of coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 40:112738–46
    [Google Scholar]
  67. 67.
    Goff DC Jr., Lloyd-Jones DM, Bennett G et al. 2014. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129:25 Suppl. 2S49–73
    [Google Scholar]
  68. 68.
    Nurnberg ST, Zhang H, Hand NJ et al. 2016. From loci to biology: functional genomics of genome-wide association for coronary disease. Circ. Res. 118:4586–606
    [Google Scholar]
  69. 69.
    Chen Z, Schunkert H. 2021. Genetics of coronary artery disease in the post-GWAS era. J. Int. Med. 290:5980–92
    [Google Scholar]
  70. 70.
    Elliott J, Bodinier B, Bond TA et al. 2020. Predictive accuracy of a polygenic risk score-enhanced prediction model versus a clinical risk score for coronary artery disease. JAMA 323:7636–45
    [Google Scholar]
  71. 71.
    Manikpurage HD, Eslami A, Perrot N et al. 2021. Polygenic risk score for coronary artery disease improves the prediction of early-onset myocardial infarction and mortality in men. Circ. Genom. Precis. Med. 14:6e003452
    [Google Scholar]
  72. 72.
    Mosley JD, Gupta DK, Tan J et al. 2020. Predictive accuracy of a polygenic risk score compared with a clinical risk score for incident coronary heart disease. JAMA 323:7627–35
    [Google Scholar]
  73. 73.
    Khan SS, Page C, Wojdyla DM et al. 2022. Predictive utility of a validated polygenic risk score for long-term risk of coronary heart disease in young and middle-aged adults. Circulation 146:8587–96
    [Google Scholar]
  74. 74.
    Mars N, Koskela JT, Ripatti P et al. 2020. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat. Med. 26:4549–57
    [Google Scholar]
  75. 75.
    Sun L, Pennells L, Kaptoge S et al. 2021. Polygenic risk scores in cardiovascular risk prediction: a cohort study and modelling analyses. PLOS Med. 18:1e1003498
    [Google Scholar]
  76. 76.
    Khera AV, Chaffin M, Wade KH et al. 2019. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell 177:3587–96.e9
    [Google Scholar]
  77. 77.
    Emdin CA, Xia R, Agrawal S et al. 2022. Polygenic score assessed in young adulthood and onset of subclinical atherosclerosis and coronary heart disease. J. Am. Coll. Cardiol. 80:3280–82
    [Google Scholar]
  78. 78.
    Trinder M, Li X, DeCastro ML et al. 2019. Risk of premature atherosclerotic disease in patients with monogenic versus polygenic familial hypercholesterolemia. J. Am. Coll. Cardiol. 74:4512–22
    [Google Scholar]
  79. 79.
    Oetjens MT, Kelly MA, Sturm AC et al. 2019. Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders. Nat. Commun. 10:14897
    [Google Scholar]
  80. 80.
    Nauffal V, Morrill VN, Jurgens SJ et al. 2022. Monogenic and polygenic contributions to QTc prolongation in the population. Circulation 145:201524–33
    [Google Scholar]
  81. 81.
    Fahed AC, Wang M, Homburger JR et al. 2020. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat. Commun. 11:13635
    [Google Scholar]
  82. 82.
    Brockman DG, Petronio L, Dron JS et al. 2021. Design and user experience testing of a polygenic score report: a qualitative study of prospective users. BMC Med. Genom. 14:1238
    [Google Scholar]
  83. 83.
    Maamari DJ, Brockman DG, Aragam K et al. 2022. Clinical implementation of a combined monogenic and polygenic risk disclosure for coronary artery disease. JACC Advances 3:1–11
    [Google Scholar]
  84. 84.
    Khera AV, Emdin CA, Drake I et al. 2016. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375:242349–58
    [Google Scholar]
  85. 85.
    Hasbani NR, Ligthart S, Brown MR et al. 2022. American Heart Association's Life's Simple 7: lifestyle recommendations, polygenic risk, and lifetime risk of coronary heart disease. Circulation 145:11808–18
    [Google Scholar]
  86. 86.
    Mega J, Stitziel N, Smith J et al. 2015. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy. Lancet 385:99842264–71
    [Google Scholar]
  87. 87.
    Damask A, Steg PG, Schwartz GG et al. 2020. Patients with high genome-wide polygenic risk scores for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial. Circulation 141:8624–36
    [Google Scholar]
  88. 88.
    Marston NA, Kamanu FK, Nordio F et al. 2020. Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score: results from the FOURIER TRIAL. Circulation 141:8616–23
    [Google Scholar]
  89. 89.
    Natarajan P, Young R, Stitziel NO et al. 2017. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135:222091–101
    [Google Scholar]
  90. 90.
    Kullo IJ, Jouni H, Austin EE et al. 2016. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES clinical trial). Circulation 133:121181–88
    [Google Scholar]
  91. 91.
    Knowles JW, Zarafshar S, Pavlovic A et al. 2017. Impact of a genetic risk score for coronary artery disease on reducing cardiovascular risk: a pilot randomized controlled study. Front. Cardiovasc. Med. 4:53
    [Google Scholar]
  92. 92.
    Fahed AC, Philippakis AA, Khera AV. 2022. The potential of polygenic scores to improve cost and efficiency of clinical trials. Nat. Commun. 13:12922
    [Google Scholar]
  93. 93.
    Ritchie SC, Lambert SA, Arnold M et al. 2021. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat. Metab. 3:111476–83
    [Google Scholar]
  94. 94.
    Voight BF, Peloso GM, Orho-Melander M et al. 2012. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380:9841572–80
    [Google Scholar]
  95. 95.
    Do R, Willer CJ, Schmidt EM et al. 2013. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45:111345–52
    [Google Scholar]
  96. 96.
    Sharp SA, Jones SE, Kimmitt RA et al. 2020. A single nucleotide polymorphism genetic risk score to aid diagnosis of coeliac disease: a pilot study in clinical care. Aliment. Pharmacol. Ther. 52:71165–73
    [Google Scholar]
  97. 97.
    Martin AR, Kanai M, Kamatani Y et al. 2019. Current clinical use of polygenic scores will risk exacerbating health disparities. Nat. Genet. 51:4584–91
    [Google Scholar]
  98. 98.
    Dikilitas O, Schaid DJ, Kosel ML et al. 2020. Predictive utility of polygenic risk scores for coronary heart disease in three major racial and ethnic groups. Am. J. Hum. Genet. 106:5707–16
    [Google Scholar]
  99. 99.
    Kim MS, Patel KP, Teng AK et al. 2018. Genetic disease risks can be misestimated across global populations. Genome Biol 19:1179
    [Google Scholar]
  100. 100.
    Martin AR, Gignoux CR, Walters RK et al. 2017. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100:4635–49
    [Google Scholar]
  101. 101.
    Ruan Y, Feng Y-CA, Chen C-Y et al. 2022. Improving polygenic prediction in ancestrally diverse populations. Nat. Genet. 54:5573–80
    [Google Scholar]
  102. 102.
    Ishigaki K, Akiyama M, Kanai M et al. 2020. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat. Genet. 52:7669–79
    [Google Scholar]
  103. 103.
    Gurdasani D, Carstensen T, Fatumo S et al. 2019. Uganda genome resource enables insights into population history and genomic discovery in Africa. Cell 179:4984–1002.e36
    [Google Scholar]
  104. 104.
    All of Us Research Program Investig., Denny JC, Rutter JL et al. 2019. The “All of Us” research program. N. Engl. J. Med. 381:7668–76
    [Google Scholar]
  105. 105.
    Taliun D, Harris DN, Kessler MD et al. 2021. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590:7845290–99
    [Google Scholar]
  106. 106.
    Wall JD, Stawiski EW, Ratan A et al. 2019. The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature 576:7785106–11
    [Google Scholar]
  107. 107.
    Wang M, Menon R, Mishra S et al. 2020. Validation of a genome-wide polygenic score for coronary artery disease in South Asians. J. Am. Coll. Cardiol. 76:6703–14
    [Google Scholar]
  108. 108.
    Riveros-Mckay F, Weale ME, Moore R et al. 2021. Integrated polygenic tool substantially enhances coronary artery disease prediction. Circ. Genom. Precis. Med. 14:2e003304
    [Google Scholar]
  109. 109.
    Weale ME, Riveros-Mckay F, Selzam S et al. 2021. Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries. Am. J. Cardiol. 148:157–64
    [Google Scholar]
  110. 110.
    Widén E, Junna N, Ruotsalainen S et al. 2022. How communicating polygenic and clinical risk for atherosclerotic cardiovascular disease impacts health behavior: an observational follow-up study. Circ. Genom. Precis. Med. 15:2e003459
    [Google Scholar]
  111. 111.
    McCarty CA, Chisholm RL, Chute CG et al. 2011. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genom. 4:13
    [Google Scholar]
  112. 112.
    Hao L, Kraft P, Berriz GF et al. 2022. Development of a clinical polygenic risk score assay and reporting workflow. Nat. Med. 28:51006–13
    [Google Scholar]
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