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Clinical utility of polygenic risk scores for coronary artery disease

Abstract

Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.

Key points

  • Genome-wide association studies demonstrate that multiple common genetic variants predispose individuals to coronary artery disease (CAD).

  • Polygenic risk scores (PRS) are singular, quantitative metrics for genetic susceptibility to a disease such as CAD.

  • The predictive performance of PRS for CAD is improved by incorporating evidence for association, linkage disequilibrium, anticipated functional impact and pleiotropy; trans-ancestry data improve the use of PRS in populations of diverse ancestry.

  • Post hoc analyses from completed clinical trials indicate that individuals with a high PRS for CAD derive the greatest relative and absolute benefit from LDL cholesterol-lowering strategies.

  • PRS for CAD could be used to identify individuals who would benefit from intensive lifestyle modification, imaging surveillance and early statin therapy.

  • PRS for CAD could be used to identify individuals at high risk for efficient clinical trial enrolment, and evidence of heterogeneous treatment benefit could be assessed through innovative trial designs.

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Fig. 1: The liability threshold model.
Fig. 2: Effect estimates for PRS.
Fig. 3: Event rates for clinical trials of coronary artery disease.
Fig. 4: Leveraging PRS for clinical trial design.
Fig. 5: Prioritization of patients with a high PRS for invasive therapy.

References

  1. White, P. D. Genes, the heart and destiny. N. Engl. J. Med. 256, 965–969 (1957).

    CAS  PubMed  Google Scholar 

  2. Gertler, M. M., Garn, S. M. & White, P. D. Young candidates for coronary heart disease. JAMA 147, 621–625 (1951).

    CAS  Google Scholar 

  3. Marenberg, M. E., Risch, N., Berkman, L. F., Floderus, B. & de Faire, U. Genetic susceptibility to death from coronary heart disease in a study of twins. N. Engl. J. Med. 330, 1041–1046 (1994).

    CAS  PubMed  Google Scholar 

  4. Natarajan, P. Polygenic risk scoring for coronary heart disease: the first risk factor. J. Am. Coll. Cardiol. 72, 1894–1897 (2018).

    PubMed  PubMed Central  Google Scholar 

  5. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).

    PubMed  PubMed Central  Google Scholar 

  6. Aragam, K. G. & Natarajan, P. Polygenic scores to assess atherosclerotic cardiovascular disease risk: clinical perspectives and basic implications. Cir. Res. 126, 1159–1177 (2020).

    CAS  Google Scholar 

  7. Nsengiman, J. et al. Enhanced linkage of a locus on chromosome 2 to premature coronary artery disease in the absence of hypercholesterolemia. Eur. J. Hum. Genet. 15, 313–319 (2007).

    Google Scholar 

  8. Khera, A. V. et al. Diagnostic yield of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia. J. Am. Coll. Cardiol. 67, 2578–2589 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Hu, P. et al. Prevalence of familial hypercholesterolemia among the general population and patients with atherosclerotic cardiovascular disease: a systematic review and meta-analysis. Circulation 141, 1742–1759 (2020).

    PubMed  Google Scholar 

  10. Rader, D. J., Cohen, J. & Hobbs, H. H. Monogenic hypercholesterolemia: new insights in pathogenesis and treatment. J. Clin. Invest. 111, 1795–1803 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Sturm, A. C. et al. Clinical genetic testing for familial hypercholesterolemia: JACC Scientific Expert Panel. J. Am. Coll. Cardiol. 72, 662–680 (2018).

    PubMed  Google Scholar 

  12. Grundy, S. M. et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 73, e285–e350 (2019).

    PubMed  Google Scholar 

  13. Samani, N. J. et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357, 443–453 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. McPherson, R. et al. A common allele on chromosome 9 associated with coronary heart disease. Science 316, 1488–1491 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Helgadottir, A. et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316, 1491–1493 (2007).

    CAS  PubMed  Google Scholar 

  16. Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Tobacco and Genetics Consortium.Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

    Google Scholar 

  18. Köttgen, A. et al. New loci associated with kidney function and chronic kidney disease. Nat. Genet. 42, 376–384 (2010).

    PubMed  PubMed Central  Google Scholar 

  19. Hellwege, J. N. et al. Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program. Nat. Commun. 10, 3842 (2019).

    PubMed  PubMed Central  Google Scholar 

  20. Warren, H. R. et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat. Genet. 49, 403–415 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Ehret, G. B. et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat. Genet. 48, 1171–1184 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  24. Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Gibson, G. Rare and common variants: twenty arguments. Nat. Rev. Genet. 13, 135–145 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Pe’er, I., Yelensky, R., Altshuler, D. & Daly, M. J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  28. Koyama, S. et al. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat. Genet. 52, 1169–1177 (2020).

    CAS  PubMed  Google Scholar 

  29. Abraham, G. & Inouye, M. Genomic risk prediction of complex human disease and its clinical application. Curr. Opin. Genet. Dev. 33, 10–16 (2015).

    CAS  PubMed  Google Scholar 

  30. Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Jostins, L. & Barrett, J. C. Genetic risk prediction in complex disease. Hum. Mol. Genet. 20, R182–R188 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).

    CAS  PubMed  Google Scholar 

  33. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. Mak, T. S. H., Porsch, R. M., Choi, S. W., Zhou, X. & Sham, P. C. Polygenic scores via penalized regression on summary statistics. Genet. Epidemiol. 41, 469–480 (2017).

    PubMed  Google Scholar 

  35. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Pattee, J. & Pan, W. Penalized regression and model selection methods for polygenic scores on summary statistics. PLoS Comput. Biol. 16, e1008271 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Márquez-Luna, C., Loh, P. R. & Price, A. L. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017).

    PubMed  PubMed Central  Google Scholar 

  38. Hu, Y. et al. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genet. 13, e1006836 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. Hu, Y. et al. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Comput. Biol. 13, e1005589 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. Ye, Y. et al. Interactions between enhanced polygenic risk scores and lifestyle for cardiovascular disease, diabetes, and lipid levels. Circ. Genom. Precis. Med. 14, e003128 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Márquez-Luna, C. et al. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nat. Commun. 12, 6052 (2021).

    PubMed  PubMed Central  Google Scholar 

  42. Amariuta, T. et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52, 1346–1354 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Wand, H. et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591, 211–219 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Riaz, M. et al. Effect of APOE and a polygenic risk score on incident dementia and cognitive decline in a healthy older population. Aging Cell 20, e13384 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Neumann, J. T. et al. Predictive performance of a polygenic risk score for incident ischemic stroke in a healthy older population. Stroke 52, 2882–2891 (2021).

    CAS  PubMed  Google Scholar 

  47. Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. Mars, N. et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat. Med. 26, 549–557 (2020).

    CAS  PubMed  Google Scholar 

  49. Jiang, X., Holmes, C. & McVean, G. The impact of age on genetic risk for common diseases. PLoS Genet. 17, e1009723 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Stone, N. J. et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129 (Suppl. 2), S1–S45 (2014).

    PubMed  Google Scholar 

  51. Hindy, G. et al. Genome-wide polygenic score, clinical risk factors, and long-term trajectories of coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 40, 2738–2746 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Elliott, J. et al. Predictive accuracy of a polygenic risk score-enhanced prediction model vs a clinical risk score for coronary artery disease. JAMA 323, 636–645 (2020).

    PubMed  PubMed Central  Google Scholar 

  53. Mosley, J. D. et al. Predictive accuracy of a polygenic risk score compared with a clinical risk score for incident coronary heart disease. JAMA 323, 627–635 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Riveros-Mckay, F. et al. Integrated polygenic tool substantially enhances coronary artery disease prediction. Circ. Genom. Precis. Med. 14, e003304 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Khera, A. V. et al. Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction. Circulation 139, 1593–1602 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Homburger, J. R. et al. Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores. Genome Med. 11, 74 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Arnett, D. K. et al. 2019 ACC/AHA Guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 74, e177–e232 (2019).

    PubMed  PubMed Central  Google Scholar 

  58. Baigent, C. et al. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 366, 1267–1278 (2005).

    CAS  PubMed  Google Scholar 

  59. Mihaylova, B. et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 380, 581–590 (2012).

    CAS  PubMed  Google Scholar 

  60. Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Aragam, K. G. et al. Limitations of contemporary guidelines for managing patients at high genetic risk of coronary artery disease. J. Am. Coll. Cardiol. 75, 2769–2780 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Kullo, I. J. et al. 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, 1181–1188 (2016).

    PubMed  PubMed Central  Google Scholar 

  63. Tada, H. et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37, 561–567 (2016).

    CAS  PubMed  Google Scholar 

  64. Ripatti, P. et al. Polygenic hyperlipidemias and coronary artery disease risk. Circ. Genom. Precis. Med. 13, e002725 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. CARDIoGRAMplusC4D Consortium. A comprehensive 1,000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    Google Scholar 

  66. Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Preprint at medRxiv https://doi.org/10.1101/2021.05.24.21257377 (2021).

    Article  Google Scholar 

  67. Karmali, K. N., Goff, D. C. Jr, Ning, H. & Lloyd-Jones, D. M. A systematic examination of the 2013 ACC/AHA pooled cohort risk assessment tool for atherosclerotic cardiovascular disease. J. Am. Coll. Cardiol. 64, 959–968 (2014).

    PubMed  Google Scholar 

  68. Goldstein, B. A., Knowles, J. W., Salfati, E., Ioannidis, J. P. & Assimes, T. L. Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example. Front. Genet. 5, 254 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. Said, M. A., Verweij, N. & van der Harst, P. Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK Biobank Study. JAMA Cardiol. 3, 693–702 (2018).

    PubMed  PubMed Central  Google Scholar 

  70. Merino, J. et al. Interaction between type 2 diabetes prevention strategies and genetic determinants of coronary artery disease on cardiometabolic risk factors. Diabetes 69, 112–120 (2020).

    CAS  PubMed  Google Scholar 

  71. Robinson, C. L. et al. Disclosing genetic risk for coronary heart disease: effects on perceived personal control and genetic counseling satisfaction. Clin. Genet. 89, 251–257 (2016).

    CAS  PubMed  Google Scholar 

  72. Brown, S. N., Jouni, H., Marroush, T. S. & Kullo, I. J. Effect of disclosing genetic risk for coronary heart disease on information seeking and sharing: the MI-GENES Study (Myocardial Infarction Genes). Circ. Cardiovasc. Genet. 10, e001613 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Grant, R. W. et al. Personalized genetic risk counseling to motivate diabetes prevention: a randomized trial. Diabetes Care 36, 13–19 (2013).

    PubMed  Google Scholar 

  74. Widén, E. et al. Communicating polygenic and non-genetic risk for atherosclerotic cardiovascular disease — an observational follow-up study. Preprint at medRxiv https://doi.org/10.1101/2020.09.18.20197137 (2020).

    Article  Google Scholar 

  75. Zhou, X. et al. Cost-effectiveness of diabetes prevention interventions targeting high-risk individuals and whole populations: a systematic review. Diabetes Care 43, 1593–1616 (2020).

    PubMed  Google Scholar 

  76. Herman, W. H. et al. The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann. Intern. Med. 142, 323–332 (2005).

    PubMed  PubMed Central  Google Scholar 

  77. Zeng, W. et al. Benefits and costs of intensive lifestyle modification programs for symptomatic coronary disease in Medicare beneficiaries. Am. Heart J. 165, 785–792 (2013).

    PubMed  Google Scholar 

  78. Detrano, R. et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N. Engl. J. Med. 358, 1336–1345 (2008).

    CAS  PubMed  Google Scholar 

  79. McClelland, R. L., Chung, H., Detrano, R., Post, W. & Kronmal, R. A. Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 113, 30–37 (2006).

    PubMed  Google Scholar 

  80. Miedema, M. D. et al. Association of coronary artery calcium with long-term, cause-specific mortality among young adults. JAMA Netw. Open 2, e197440 (2019).

    PubMed  PubMed Central  Google Scholar 

  81. Paixao, A. R. et al. Coronary artery calcium improves risk classification in younger populations. JACC Cardiovasc. Imaging 8, 1285–1293 (2015).

    PubMed  Google Scholar 

  82. Tota-Maharaj, R. et al. Coronary artery calcium for the prediction of mortality in young adults <45 years old and elderly adults >75 years old. Eur. Heart J. 33, 2955–2962 (2012).

    CAS  PubMed  Google Scholar 

  83. Nasir, K. et al. Coronary artery calcification and family history of premature coronary heart disease: sibling history is more strongly associated than parental history. Circulation 110, 2150–2156 (2004).

    PubMed  Google Scholar 

  84. Klarin, D. et al. Genetic architecture of abdominal aortic aneurysm in the Million Veteran Program. Circulation 142, 1633–1646 (2020).

    PubMed  PubMed Central  Google Scholar 

  85. Owens, D. K. et al. Screening for abdominal aortic aneurysm: US Preventive Services Task Force Recommendation Statement. JAMA 322, 2211–2218 (2019).

    PubMed  Google Scholar 

  86. Besseling, J., Hovingh, G. K., Huijgen, R., Kastelein, J. J. P. & Hutten, B. A. Statins in familial hypercholesterolemia: consequences for coronary artery disease and all-cause mortality. J. Am. Coll. Cardiol. 68, 252–260 (2016).

    CAS  PubMed  Google Scholar 

  87. Klarin, D. et al. Genome-wide association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease. Nat. Genet. 51, 1574–1579 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Muse, E. D. et al. Response to polygenic risk: results of the mygenerank mobile application-based coronary artery disease study. Preprint at medRxiv https://doi.org/10.1101/2021.04.26.21256141 (2021).

    Article  Google Scholar 

  89. Mach, F. et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur. Heart J. 41, 111–188 (2020).

    PubMed  Google Scholar 

  90. Pearson, G. J. et al. 2021 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention of cardiovascular disease in the adult. Can. J. Cardiol. 37, 1129–1150 (2021).

    PubMed  Google Scholar 

  91. Marston, N. A. et al. Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score: results from the FOURIER trial. Circulation 141, 616–623 (2020).

    PubMed  Google Scholar 

  92. Damask, A. et al. 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, 624–636 (2020).

    PubMed  Google Scholar 

  93. Knowles, J. W. et al. Impact of a genetic risk score for coronary artery disease on reducing cardiovascular risk: a pilot randomized controlled study. Front. Cardiovasc. Med. 4, 53 (2017).

    PubMed  PubMed Central  Google Scholar 

  94. Moore, T. J., Zhang, H., Anderson, G. & Alexander, G. C. Estimated costs of pivotal trials for novel therapeutic agents approved by the US Food and Drug Administration, 2015-2016. JAMA Intern. Med. 178, 1451–1457 (2018).

    PubMed  PubMed Central  Google Scholar 

  95. Schwartz, G. G. et al. Alirocumab and cardiovascular outcomes after acute coronary syndrome. N. Engl. J. Med. 379, 2097–2107 (2018).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  97. Kent, D. M., Steyerberg, E. & van Klaveren, D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ 363, k4245 (2018).

    PubMed  PubMed Central  Google Scholar 

  98. Bothwell, L. E., Avorn, J., Khan, N. F. & Kesselheim, A. S. Adaptive design clinical trials: a review of the literature and ClinicalTrials.gov. BMJ Open 8, e018320 (2018).

    PubMed  PubMed Central  Google Scholar 

  99. Holmes, D. R. Jr et al. The 21st Century Cures Act and and early feasibility studies for cardiovascular devices: what have we learned, where do we need to go? JACC Cardiovasc. Interv. 11, 2220–2225 (2018).

    PubMed  Google Scholar 

  100. D’Agostino, R. B. Sr. The delayed-start study design. N. Engl. J. Med. 361, 1304–1306 (2009).

    PubMed  Google Scholar 

  101. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT04331535 (2021).

  102. Tunis, S. R., Stryer, D. B. & Clancy, C. M. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA 290, 1624–1632 (2003).

    CAS  PubMed  Google Scholar 

  103. Maron, D. J. et al. Initial invasive or conservative strategy for stable coronary disease. N. Engl. J. Med. 382, 1395–1407 (2020).

    PubMed  PubMed Central  Google Scholar 

  104. Al-Lamee, R. et al. Percutaneous coronary intervention in stable angina (ORBITA): a double-blind, randomised controlled trial. Lancet 391, 31–40 (2018).

    PubMed  Google Scholar 

  105. Boden, W. E. et al. Optimal medical therapy with or without PCI for stable coronary disease. N. Engl. J. Med. 356, 1503–1516 (2007).

    CAS  PubMed  Google Scholar 

  106. Franklin, B. A. Lessons learned from the COURAGE trial: generalizability, limitations, and implications. Prev. Cardiol. 10, 117–120 (2007).

    PubMed  Google Scholar 

  107. Weintraub, W. S. et al. Effect of PCI on quality of life in patients with stable coronary disease. N. Engl. J. Med. 359, 677–687 (2008).

    CAS  PubMed  Google Scholar 

  108. Levin, M. G. et al. Genomic risk stratification predicts all-cause mortality after cardiac catheterization. Circ. Genom. Precis. Med. 11, e002352 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Ford, I. & Norrie, J. Pragmatic trials. N. Engl. J. Med. 375, 454–463 (2016).

    PubMed  Google Scholar 

  110. Pereira, N. L. et al. Effect of genotype-guided oral P2Y12 inhibitor selection vs conventional clopidogrel therapy on ischemic outcomes after percutaneous coronary intervention: the TAILOR-PCI randomized clinical trial. JAMA 324, 761–771 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Pereira, N. L. et al. Effect of CYP2C19 genotype on ischemic outcomes during oral P2Y12 inhibitor therapy: a meta-analysis. JACC Cardiovasc. Interv. 14, 739–750 (2021).

    PubMed  Google Scholar 

  112. Executive Committee for the Asymptomatic Carotid Atherosclerosis Study. Endarterectomy for asymptomatic carotid artery stenosis. JAMA 273, 1421–1428 (1995).

    Google Scholar 

  113. Halliday, A. et al. Prevention of disabling and fatal strokes by successful carotid endarterectomy in patients without recent neurological symptoms: randomised controlled trial. Lancet 363, 1491–1502 (2004).

    CAS  PubMed  Google Scholar 

  114. Brott, T. G. et al. Stenting versus endarterectomy for treatment of carotid-artery stenosis. N. Engl. J. Med. 363, 11–23 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Schermerhorn, M. L. et al. Association of transcarotid artery revascularization vs transfemoral carotid artery stenting with stroke or death among patients with carotid artery stenosis. JAMA 322, 2313–2322 (2019).

    PubMed  PubMed Central  Google Scholar 

  116. Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Curtis, D. Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatr. Genet. 28, 85–89 (2018).

    PubMed  Google Scholar 

  118. Schultz, L. M. et al. Stability of polygenic scores across discovery genome-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2021.06.18.449060 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Ruan, Y. et al. Improving polygenic prediction in ancestrally diverse populations. Preprint at medRxiv https://doi.org/10.1101/2020.12.27.20248738 (2021).

    Article  Google Scholar 

  120. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed program. Nature 590, 290–299 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 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).

    PubMed  Google Scholar 

  122. All of Us Research Program Investigators. The “All of Us” research program. N. Engl. J. Med. 381, 668–676 (2019).

    Google Scholar 

  123. McCarty, C. A. et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genomics 4, 13 (2011).

    PubMed  PubMed Central  Google Scholar 

  124. Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Chen, S. F. et al. Genotype imputation and variability in polygenic risk score estimation. Genome Med. 12, 100 (2020).

    PubMed  PubMed Central  Google Scholar 

  126. Kowalski, M. H. et al. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet. 15, e1008500 (2019).

    PubMed  PubMed Central  Google Scholar 

  127. Marnetto, D. et al. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals. Nat. Commun. 11, 1628 (2020).

    PubMed  PubMed Central  Google Scholar 

  128. Lambert, S. A. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53, 420–425 (2021).

    CAS  PubMed  Google Scholar 

  129. Carver, R. B., Castéra, J., Gericke, N., Evangelista, N. A. & El-Hani, C. N. Young adults’ belief in genetic determinism, and knowledge and attitudes towards modern genetics and genomics: the PUGGS questionnaire. PLoS ONE 12, e0169808 (2017).

    PubMed  PubMed Central  Google Scholar 

  130. Kaufman, D. J., Baker, R., Milner, L. C., Devaney, S. & Hudson, K. L. A survey of US adults’ opinions about conduct of a Nationwide Precision Medicine Initiative® cohort study of genes and environment. PLoS ONE 11, e0160461 (2016).

    PubMed  PubMed Central  Google Scholar 

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Both authors contributed substantially to all aspects of the article.

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Correspondence to Pradeep Natarajan.

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Nature Reviews Cardiology thanks Guillaume Paré, Ali Torkamani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

All Of Us: https://allofus.nih.gov/

eMERGE: https://emerge-network.org/

MVP: https://www.research.va.gov/mvp/

Polygenic Score Catalog: https://www.pgscatalog.org/

TOPMed: https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program

Glossary

Genome-wide association studies

(GWAS). Studies that search, in an agnostic fashion, for allelic association with a particular phenotype by genotyping tag single-nucleotide polymorphisms across the entire genome.

Single-nucleotide polymorphism

(SNP). Specific substitution of a single nucleotide at a specific position in the genome.

Imputation

A technique that leverages the linkage disequilibrium between genotyped and ungenotyped variants to statistically infer missing genotypes using a reference panel of genotyped individuals.

Non-genotyped alleles

Single-nucleotide polymorphisms that can be inferred through statistical imputation but not directly observed on a genotype array.

Genome-wide significance

A level of statistical significance required to establish association for a common variant in genome-wide association studies (P = 5 × 10−8).

Linkage disequilibrium

(LD). The non-random association of alleles at two or more loci because of infrequent recombination events between them.

Least absolute shrinkage and selection operator

A modelling procedure for linear regression encouraging model sparsity.

Elastic net

Regularized regression method combining penalties of least absolute shrinkage and selection operator and ridge methods in a linear fashion.

Haplotypes

Combinations of alleles transmitted together on a single chromosome.

Pleiotropy

A single gene or variant yielding two or more apparently unrelated effects.

Whole-genome sequencing

Identification of all base pairs for an individual, with subsequent mapping of contiguous reads to a reference genome sequence (for next-generation sequencing).

Admixed populations

Populations in which previously diverged genetic lineages are mixed, for example, the AMR (admixed American) superpopulation in the 1,000 Genomes reference panel.

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Klarin, D., Natarajan, P. Clinical utility of polygenic risk scores for coronary artery disease. Nat Rev Cardiol 19, 291–301 (2022). https://doi.org/10.1038/s41569-021-00638-w

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