Skip to main content

Advertisement

SpringerLink
Log in

Monogenic and Polygenic Models of Coronary Artery Disease

  • Cardiovascular Genomics (P Natarajan, Section Editor)
  • Published:
Current Cardiology Reports Aims and scope Submit manuscript

Abstract

Purpose of the Review

Coronary artery disease (CAD) is a common disease globally attributable to the interplay of complex genetic and lifestyle factors. Here, we review how genomic sequencing advances have broadened the fundamental understanding of the monogenic and polygenic contributions to CAD and how these insights can be utilized, in part by creating polygenic risk estimates, for improved disease risk stratification at the individual patient level.

Recent Findings

Initial studies linking premature CAD with rare familial cases of elevated blood lipids highlighted high-risk monogenic contributions, predominantly presenting as familial hypercholesterolemia (FH). More commonly CAD genetic risk is a function of multiple, higher frequency variants each imparting lower magnitude of risk, which can be combined to form polygenic risk scores (PRS) conveying significant risk to individuals at the extremes. However, gaps remain in clinical validation of PRSs, most notably in non-European populations.

Summary

With improved and more broadly utilized genomic sequencing technologies, the genetic underpinnings of coronary artery disease are being unraveled. As a result, polygenic risk estimation is poised to become a widely used and powerful tool in the clinical setting. While the use of PRSs to augment current clinical risk stratification for optimization of cardiovascular disease risk by lifestyle change or therapeutic targeting is promising, we await adequately powered, prospective studies, demonstrating the clinical utility of polygenic risk estimation in practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Abbreviations

CAD:

coronary artery disease

EHR:

electronic health record

FH:

familial hypercholesterolemia

GWAS:

genome-wide association study

LD:

linkage disequilibrium

LDL:

low-density lipoprotein

LDLR:

low-density lipoprotein receptor

PRS:

polygenic risk score

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation. 2021. https://doi.org/10.1161/CIR.0000000000000950.

  2. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7.

    Article  CAS  PubMed  Google Scholar 

  3. Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet. 2017;18(6):331–44. https://doi.org/10.1038/nrg.2016.160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Erdmann J, Kessler T, Munoz Venegas L, Schunkert H. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc Res. 2018;114(9):1241–57. https://doi.org/10.1093/cvr/cvy084.

    Article  CAS  PubMed  Google Scholar 

  5. Musunuru K, Kathiresan S. Genetics of common, complex coronary artery disease. Cell. 2019;177(1):132–45. https://doi.org/10.1016/j.cell.2019.02.015.

    Article  CAS  PubMed  Google Scholar 

  6. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336(7659):1475–82. https://doi.org/10.1136/bmj.39609.449676.25.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, 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. 2019;74(10):e177–232. https://doi.org/10.1016/j.jacc.2019.03.010.

    Article  PubMed  PubMed Central  Google Scholar 

  8. ten Kate LP, Boman H, Daiger SP, Motulsky AG. Familial aggregation of coronary heart disease and its relation to known genetic risk factors. Am J Cardiol. 1982;50(5):945–53. https://doi.org/10.1016/0002-9149(82)90400-3.

    Article  PubMed  Google Scholar 

  9. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018;19(9):581–90. https://doi.org/10.1038/s41576-018-0018-x.

    Article  CAS  PubMed  Google Scholar 

  10. Marenberg ME, Risch N, Berkman LF, Floderus B, de Faire U. Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med. 1994;330(15):1041–6. https://doi.org/10.1056/NEJM199404143301503.

    Article  CAS  PubMed  Google Scholar 

  11. Lloyd-Jones DM, Nam BH, D'Agostino RB Sr, Levy D, Murabito JM, Wang TJ, et al. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA. 2004;291(18):2204–11. https://doi.org/10.1001/jama.291.18.2204.

    Article  CAS  PubMed  Google Scholar 

  12. Churko JM, Mantalas GL, Snyder MP, Wu JC. Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases. Circ Res. 2013;112(12):1613–23. https://doi.org/10.1161/CIRCRESAHA.113.300939.

    Article  CAS  PubMed  Google Scholar 

  13. Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J. 2013;34(45):3478–90a. https://doi.org/10.1093/eurheartj/eht273.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Henderson R, O'Kane M, McGilligan V, Watterson S. The genetics and screening of familial hypercholesterolaemia. J Biomed Sci. 2016;23:39. https://doi.org/10.1186/s12929-016-0256-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Akioyamen LE, Genest J, Shan SD, Reel RL, Albaum JM, Chu A, et al. Estimating the prevalence of heterozygous familial hypercholesterolaemia: a systematic review and meta-analysis. BMJ Open. 2017;7(9):e016461. https://doi.org/10.1136/bmjopen-2017-016461.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Benn M, Watts GF, Tybjaerg-Hansen A, Nordestgaard BG. Mutations causative of familial hypercholesterolaemia: screening of 98 098 individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217. Eur Heart J. 2016;37(17):1384–94. https://doi.org/10.1093/eurheartj/ehw028.

    Article  CAS  PubMed  Google Scholar 

  17. Lehrman MA, Schneider WJ, Sudhof TC, Brown MS, Goldstein JL, Russell DW. Mutation in LDL receptor: Alu-Alu recombination deletes exons encoding transmembrane and cytoplasmic domains. Science. 1985;227(4683):140–6. https://doi.org/10.1126/science.3155573.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Soutar AK, Naoumova RP. Mechanisms of disease: genetic causes of familial hypercholesterolemia. Nat Clin Pract Cardiovasc Med. 2007;4(4):214–25. https://doi.org/10.1038/ncpcardio0836.

    Article  CAS  PubMed  Google Scholar 

  19. Soria LF, Ludwig EH, Clarke HR, Vega GL, Grundy SM, McCarthy BJ. Association between a specific apolipoprotein B mutation and familial defective apolipoprotein B-100. Proc Natl Acad Sci U S A. 1989;86(2):587–91. https://doi.org/10.1073/pnas.86.2.587.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Defesche JC, Gidding SS, Harada-Shiba M, Hegele RA, Santos RD, Wierzbicki AS. Familial hypercholesterolaemia. Nat Rev Dis Primers. 2017;3:17093. https://doi.org/10.1038/nrdp.2017.93.

    Article  PubMed  Google Scholar 

  21. Abifadel M, Varret M, Rabes JP, Allard D, Ouguerram K, Devillers M, et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. 2003;34(2):154–6. https://doi.org/10.1038/ng1161.

    Article  CAS  PubMed  Google Scholar 

  22. Sabatine MS. PCSK9 inhibitors: clinical evidence and implementation. Nat Rev Cardiol. 2019;16(3):155–65. https://doi.org/10.1038/s41569-018-0107-8.

    Article  CAS  PubMed  Google Scholar 

  23. Garcia CK, Wilund K, Arca M, Zuliani G, Fellin R, Maioli M, et al. Autosomal recessive hypercholesterolemia caused by mutations in a putative LDL receptor adaptor protein. Science. 2001;292(5520):1394–8. https://doi.org/10.1126/science.1060458.

    Article  CAS  PubMed  Google Scholar 

  24. Awan Z, Choi HY, Stitziel N, Ruel I, Bamimore MA, Husa R, et al. APOE p.Leu167del mutation in familial hypercholesterolemia. Atherosclerosis. 2013;231(2):218–22. https://doi.org/10.1016/j.atherosclerosis.2013.09.007.

    Article  CAS  PubMed  Google Scholar 

  25. Fouchier SW, Dallinga-Thie GM, Meijers JC, Zelcer N, Kastelein JJ, Defesche JC, et al. Mutations in STAP1 are associated with autosomal dominant hypercholesterolemia. Circ Res. 2014;115(6):552–5. https://doi.org/10.1161/CIRCRESAHA.115.304660.

    Article  CAS  PubMed  Google Scholar 

  26. Rios J, Stein E, Shendure J, Hobbs HH, Cohen JC. Identification by whole-genome resequencing of gene defect responsible for severe hypercholesterolemia. Hum Mol Genet. 2010;19(22):4313–8. https://doi.org/10.1093/hmg/ddq352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Musunuru K, Hershberger RE, Day SM, Klinedinst NJ, Landstrom AP, Parikh VN, et al. Genetic Testing for inherited cardiovascular diseases: a scientific statement from the American Heart Association. Circ Genom Precis Med. 2020;13(4):e000067. https://doi.org/10.1161/HCG.0000000000000067.

    Article  PubMed  Google Scholar 

  28. Talmud PJ, Shah S, Whittall R, Futema M, Howard P, Cooper JA, et al. Use of low-density lipoprotein cholesterol gene score to distinguish patients with polygenic and monogenic familial hypercholesterolaemia: a case-control study. Lancet. 2013;381(9874):1293–301. https://doi.org/10.1016/s0140-6736(12)62127-8.

    Article  CAS  PubMed  Google Scholar 

  29. Besseling J, Hovingh GK, Huijgen R, Kastelein JJP, Hutten BA. Statins in familial hypercholesterolemia: consequences for coronary artery disease and all-cause mortality. J Am Coll Cardiol. 2016;68(3):252–60. https://doi.org/10.1016/j.jacc.2016.04.054.

    Article  CAS  PubMed  Google Scholar 

  30. Abul-Husn NS, Manickam K, Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C, et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science. 2016;354(6319). https://doi.org/10.1126/science.aaf7000.

  31. Alver M, Palover M, Saar A, Lall K, Zekavat SM, Tonisson N, et al. Recall by genotype and cascade screening for familial hypercholesterolemia in a population-based biobank from Estonia. Genet Med. 2019;21(5):1173–80. https://doi.org/10.1038/s41436-018-0311-2.

    Article  CAS  PubMed  Google Scholar 

  32. Sturm AC, Knowles JW, Gidding SS, Ahmad ZS, Ahmed CD, Ballantyne CM, et al. Clinical genetic testing for familial hypercholesterolemia: JACC Scientific Expert Panel. J Am Coll Cardiol. 2018;72(6):662–80. https://doi.org/10.1016/j.jacc.2018.05.044.

    Article  PubMed  Google Scholar 

  33. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5–22. https://doi.org/10.1016/j.ajhg.2017.06.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;316(5830):1491–3. https://doi.org/10.1126/science.1142842.

    Article  CAS  PubMed  Google Scholar 

  35. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, et al. A common allele on chromosome 9 associated with coronary heart disease. Science. 2007;316(5830):1488–91. https://doi.org/10.1126/science.1142447.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, et al. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007;357(5):443–53. https://doi.org/10.1056/NEJMoa072366.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lo Sardo V, Chubukov P, Ferguson W, Kumar A, Teng EL, Duran M, et al. Unveiling the Role of the Most Impactful Cardiovascular Risk Locus through Haplotype Editing. Cell. 2018;175(7):1796–810 e20. https://doi.org/10.1016/j.cell.2018.11.014.

    Article  CAS  PubMed  Google Scholar 

  38. Kathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med. 2008;358(12):1240–9. https://doi.org/10.1056/NEJMoa0706728.

    Article  CAS  PubMed  Google Scholar 

  39. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40(2):161–9. https://doi.org/10.1038/ng.76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Clarke R, Peden JF, Hopewell JC, Kyriakou T, Goel A, Heath SC, et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N Engl J Med. 2009;361(26):2518–28. https://doi.org/10.1056/NEJMoa0902604.

    Article  CAS  PubMed  Google Scholar 

  41. Erdmann J, Grosshennig A, Braund PS, Konig IR, Hengstenberg C, Hall AS, et al. New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat Genet. 2009;41(3):280–2. https://doi.org/10.1038/ng.307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Myocardial Infarction Genetics C, Kathiresan S, Voight BF, Purcell S, Musunuru K, Ardissino D, et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet. 2009;41(3):334–41. https://doi.org/10.1038/ng.327.

    Article  CAS  Google Scholar 

  43. Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature. 2010;466(7307):714–9. https://doi.org/10.1038/nature09266.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet. 2012;380(9841):572–80. https://doi.org/10.1016/s0140-6736(12)60312-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Do R, Stitziel NO, Won HH, Jorgensen AB, Duga S, Angelica Merlini P, et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature. 2015;518(7537):102–6. https://doi.org/10.1038/nature13917.

    Article  CAS  PubMed  Google Scholar 

  46. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013;45(11):1345–52. https://doi.org/10.1038/ng.2795.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Helgadottir A, Gretarsdottir S, Thorleifsson G, Hjartarson E, Sigurdsson A, Magnusdottir A, et al. Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease. Nat Genet. 2016;48(6):634–9. https://doi.org/10.1038/ng.3561.

    Article  CAS  PubMed  Google Scholar 

  48. Khera AV, Won HH, Peloso GM, O'Dushlaine C, Liu D, Stitziel NO, et al. Association of rare and common variation in the lipoprotein lipase gene with coronary artery disease. JAMA. 2017;317(9):937–46. https://doi.org/10.1001/jama.2017.0972.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83. https://doi.org/10.1038/ng.2797.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Coronary Artery Disease Genetics C. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet. 2011;43(4):339–44. https://doi.org/10.1038/ng.782.

    Article  CAS  Google Scholar 

  51. Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011;43(4):333–8. https://doi.org/10.1038/ng.784.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Consortium CAD, Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45(1):25–33. https://doi.org/10.1038/ng.2480.

    Article  CAS  Google Scholar 

  53. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47(10):1121–30. https://doi.org/10.1038/ng.3396.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Howson JMM, Zhao W, Barnes DR, Ho WK, Young R, Paul DS, et al. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat Genet. 2017;49(7):1113–9. https://doi.org/10.1038/ng.3874.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Klarin D, Zhu QM, Emdin CA, Chaffin M, Horner S, McMillan BJ, et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet. 2017;49(9):1392–7. https://doi.org/10.1038/ng.3914.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Nelson CP, Goel A, Butterworth AS, Kanoni S, Webb TR, Marouli E, et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat Genet. 2017;49(9):1385–91. https://doi.org/10.1038/ng.3913.

    Article  CAS  PubMed  Google Scholar 

  57. van der Harst P, Verweij N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ Res. 2018;122(3):433–43. https://doi.org/10.1161/CIRCRESAHA.117.312086.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. • Koyama S, Ito K, Terao C, Akiyama M, Horikoshi M, Momozawa Y, et al. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat Genet. 2020;52(11):1169–77. https://doi.org/10.1038/s41588-020-0705-3This study highlights the importance of ethnicity in PRS derivation when translating findings into non-European populations. One of the largest trans-ethnic meta-analysis to date for discovery of novel loci associated with CAD.

    Article  CAS  PubMed  Google Scholar 

  59. Chatterjee N, Shi J, Garcia-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016;17(7):392–406. https://doi.org/10.1038/nrg.2016.27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chen H, Liu X, Brendler CB, Ankerst DP, Leach RJ, Goodman PJ, et al. Adding genetic risk score to family history identifies twice as many high-risk men for prostate cancer: Results from the prostate cancer prevention trial. Prostate. 2016;76(12):1120–9. https://doi.org/10.1002/pros.23200.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24. https://doi.org/10.1038/s41588-018-0183-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. DeFilippis AP, Young R, Carrubba CJ, McEvoy JW, Budoff MJ, Blumenthal RS, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med. 2015;162(4):266–75. https://doi.org/10.7326/M14-1281.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Rana JS, Tabada GH, Solomon MD, Lo JC, Jaffe MG, Sung SH, et al. Accuracy of the atherosclerotic cardiovascular risk equation in a large contemporary, multiethnic population. J Am Coll Cardiol. 2016;67(18):2118–30. https://doi.org/10.1016/j.jacc.2016.02.055.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Thanassoulis G, Peloso GM, Pencina MJ, Hoffmann U, Fox CS, Cupples LA, et al. A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart Study. Circ Cardiovasc Genet. 2012;5(1):113–21. https://doi.org/10.1161/CIRCGENETICS.111.961342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tikkanen E, Havulinna AS, Palotie A, Salomaa V, Ripatti S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33(9):2261–6. https://doi.org/10.1161/ATVBAHA.112.301120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR, et al. Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N Engl J Med. 2016;375(24):2349–58. https://doi.org/10.1056/NEJMoa1605086.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield MJ, Devlin JJ, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015;385(9984):2264–71. https://doi.org/10.1016/s0140-6736(14)61730-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Natarajan P, Young R, Stitziel NO, Padmanabhan S, Baber U, Mehran R, 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. 2017;135(22):2091–101. https://doi.org/10.1161/CIRCULATIONAHA.116.024436.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Tada H, Melander O, Louie JZ, Catanese JJ, Rowland CM, Devlin JJ, et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur Heart J. 2016;37(6):561–7. https://doi.org/10.1093/eurheartj/ehv462.

    Article  CAS  PubMed  Google Scholar 

  70. • Inouye M, Abraham G, Nelson CP, Wood AM, Sweeting MJ, Dudbridge F, et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J Am Coll Cardiol. 2018;72(16):1883–93. https://doi.org/10.1016/j.jacc.2018.07.079The findings presented here integrate large genomic datasets to show the benefit of polygenic risk scoring for risk stratification above and beyond traditional risk factors.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Aragam KG, Dobbyn A, Judy R, Chaffin M, Chaudhary K, Hindy G, et al. Limitations of contemporary guidelines for managing patients at high genetic risk of coronary artery disease. J Am Coll Cardiol. 2020;75(22):2769–80. https://doi.org/10.1016/j.jacc.2020.04.027.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. • Damask A, Steg PG, Schwartz GG, Szarek M, Hagstrom E, Badimon L, 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. 2020;141(8):624–36. https://doi.org/10.1161/CIRCULATIONAHA.119.044434The findings presented here with PSCK9 inhibitor therapy extend the statin findings from Mega et al 2015 showing the utility of CAD PRS to identify patients who receive the greatest benefit from therapy.

    Article  PubMed  Google Scholar 

  73. Dikilitas O, Schaid DJ, Kosel ML, Carroll RJ, Chute CG, Denny JA, et al. Predictive Utility of polygenic risk scores for coronary heart disease in three major racial and ethnic groups. Am J Hum Genet. 2020;106(5):707–16. https://doi.org/10.1016/j.ajhg.2020.04.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Emdin CA, Bhatnagar P, Wang M, Pillai SG, Li L, Qian HR, et al. Genome-wide polygenic score and cardiovascular outcomes with evacetrapib in patients with high-risk vascular disease: a nested case-control study. Circ Genom Precis Med. 2020;13(1):e002767. https://doi.org/10.1161/CIRCGEN.119.002767.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Hindy G, Aragam KG, Ng K, Chaffin M, Lotta LA, Baras A, et al. Genome-wide polygenic score, clinical risk factors, and long-term trajectories of coronary artery disease. Arterioscler Thromb Vasc Biol. 2020;40(11):2738–46. https://doi.org/10.1161/ATVBAHA.120.314856.

    Article  CAS  PubMed  Google Scholar 

  76. Marston NA, Kamanu FK, Nordio F, Gurmu Y, Roselli C, Sever PS, et al. Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score: results from the FOURIER trial. Circulation. 2020;141(8):616–23. https://doi.org/10.1161/CIRCULATIONAHA.119.043805.

    Article  PubMed  Google Scholar 

  77. Wunnemann F, Sin Lo K, Langford-Avelar A, Busseuil D, Dube MP, Tardif JC, et al. Validation of genome-wide polygenic risk scores for coronary artery disease in French Canadians. Circ Genom Precis Med. 2019;12(6):e002481. https://doi.org/10.1161/CIRCGEN.119.002481.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Ntalla I, Kanoni S, Zeng L, Giannakopoulou O, Danesh J, Watkins H, et al. Genetic risk score for coronary disease identifies predispositions to cardiovascular and noncardiovascular diseases. J Am Coll Cardiol. 2019;73(23):2932–42. https://doi.org/10.1016/j.jacc.2019.03.512.

    Article  Google Scholar 

  79. Timmerman N, de Kleijn DPV, de Borst GJ, den Ruijter HM, Asselbergs FW, Pasterkamp G, et al. Family history and polygenic risk of cardiovascular disease: independent factors associated with secondary cardiovascular events in patients undergoing carotid endarterectomy. Atherosclerosis. 2020;307:121–9. https://doi.org/10.1016/j.atherosclerosis.2020.04.013.

    Article  CAS  PubMed  Google Scholar 

  80. Abraham G, Havulinna AS, Bhalala OG, Byars SG, De Livera AM, Yetukuri L, et al. Genomic prediction of coronary heart disease. Eur Heart J. 2016;37(43):3267–78. https://doi.org/10.1093/eurheartj/ehw450.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Elliott J, Bodinier B, Bond TA, Chadeau-Hyam M, Evangelou E, Moons KGM, et al. Predictive accuracy of a polygenic risk score-enhanced prediction model vs a clinical risk score for coronary artery disease. JAMA. 2020;323(7):636–45. https://doi.org/10.1001/jama.2019.22241.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Mars N, Koskela JT, Ripatti P, Kiiskinen TTJ, Havulinna AS, Lindbohm JV, et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med. 2020;26(4):549–57. https://doi.org/10.1038/s41591-020-0800-0.

    Article  CAS  PubMed  Google Scholar 

  83. Mosley JD, Gupta DK, Tan J, Yao J, Wells QS, Shaffer CM, et al. Predictive accuracy of a polygenic risk score compared with a clinical risk score for incident coronary heart disease. JAMA. 2020;323(7):627–35. https://doi.org/10.1001/jama.2019.21782.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Riveros-Mckay F, Weale ME, Moore R, Selzam S, Krapohl E, Sivley RM, et al. An integrated polygenic tool substantially enhances coronary artery disease prediction. Circ Genom Precis Med. 2021. https://doi.org/10.1161/CIRCGEN.120.003304.

  85. Jarmul J, Pletcher MJ, Hassmiller Lich K, Wheeler SB, Weinberger M, Avery CL, et al. Cardiovascular genetic risk testing for targeting statin therapy in the primary prevention of atherosclerotic cardiovascular disease: a cost-effectiveness analysis. Circ Cardiovasc Qual Outcomes. 2018;11(4):e004171. https://doi.org/10.1161/CIRCOUTCOMES.117.004171.

    Article  PubMed  Google Scholar 

  86. Knowles JW, Zarafshar S, Pavlovic A, Goldstein BA, Tsai S, Li J, et al. Impact of a genetic risk score for coronary artery disease on reducing cardiovascular risk: a pilot randomized controlled study. Front Cardiovasc Med. 2017;4:53. https://doi.org/10.3389/fcvm.2017.00053.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Kullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN, 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. 2016;133(12):1181–8. https://doi.org/10.1161/CIRCULATIONAHA.115.020109.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Severance LM, Carter H, Contijoch FJ, McVeigh ER. Targeted coronary artery calcium screening in high-risk younger individuals using consumer genetic screening results. JACC Cardiovasc Imaging. 2021. https://doi.org/10.1016/j.jcmg.2020.11.013.

  89. Severance LM, Contijoch FJ, Carter H, Fan CC, Seibert TM, Dale AM, et al. Using a genetic risk score to calculate the optimal age for an individual to undergo coronary artery calcium screening. J Cardiovasc Comput Tomogr. 2019;13(4):203–10. https://doi.org/10.1016/j.jcct.2019.05.005.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Fahed AC, Wang M, Homburger JR, Patel AP, Bick AG, Neben CL, et al. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat Commun. 2020;11(1):3635. https://doi.org/10.1038/s41467-020-17374-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Hindy G, Wiberg F, Almgren P, Melander O, Orho-Melander M. Polygenic risk score for coronary heart disease modifies the elevated risk by cigarette smoking for disease incidence. Circ Genom Precis Med. 2018;11(1):e001856. https://doi.org/10.1161/CIRCGEN.117.001856.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Huang Y, Hui Q, Gwinn M, Hu YJ, Quyyumi AA, Vaccarino V, et al. Sexual differences in genetic predisposition of coronary artery disease. Circ Genom Precis Med. 2020. https://doi.org/10.1161/CIRCGEN.120.003147.

  93. • Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–91. https://doi.org/10.1038/s41588-019-0379-xAn important perspective addresssing the Eurocentric bias in current genomic studies calling for improved diversity in future studies.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Gola D, Erdmann J, Lall K, Magi R, Muller-Myhsok B, Schunkert H, et al. Population bias in polygenic risk prediction models for coronary artery disease. Circ Genom Precis Med. 2020;13(6):e002932. https://doi.org/10.1161/CIRCGEN.120.002932.

    Article  PubMed  Google Scholar 

  95. Skoglund P, Mathieson I. Ancient genomics of modern humans: the first secade. Annu Rev Genomics Hum Genet. 2018;19:381–404. https://doi.org/10.1146/annurev-genom-083117-021749.

    Article  CAS  PubMed  Google Scholar 

  96. Wang M, Menon R, Mishra S, Patel AP, Chaffin M, Tanneeru D, et al. Validation of a genome-wide polygenic score for coronary artery disease in South Asians. J Am Coll Cardiol. 2020;76(6):703–14. https://doi.org/10.1016/j.jacc.2020.06.024.

    Article  CAS  PubMed  Google Scholar 

  97. Abul-Husn NS, Kenny EE. Personalized medicine and the power of electronic health records. Cell. 2019;177(1):58–69. https://doi.org/10.1016/j.cell.2019.02.039.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature. 2021;590(7845):290–9. https://doi.org/10.1038/s41586-021-03205-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19(8):491–504. https://doi.org/10.1038/s41576-018-0016-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Weissbrod O, Hormozdiari F, Benner C, Cui R, Ulirsch J, Gazal S, et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat Genet. 2020;52(12):1355–63. https://doi.org/10.1038/s41588-020-00735-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M, Dey KK, et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat Genet. 2020;52(12):1346–54. https://doi.org/10.1038/s41588-020-00740-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Atkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ, Santoro ML, et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat Genet. 2021;53(2):195–204. https://doi.org/10.1038/s41588-020-00766-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Maples BK, Gravel S, Kenny EE, Bustamante CD. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am J Hum Genet. 2013;93(2):278–88. https://doi.org/10.1016/j.ajhg.2013.06.020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Engelhard MM, Oliver JA, McClernon FJ. Digital envirotyping: quantifying environmental determinants of health and behavior. NPJ Digit Med. 2020;3:36. https://doi.org/10.1038/s41746-020-0245-3.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet. 2020;21(8):493–502. https://doi.org/10.1038/s41576-020-0224-1.

    Article  CAS  PubMed  Google Scholar 

  106. Rao DC, Sung YJ, Winkler TW, Schwander K, Borecki I, Cupples LA, et al. Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale. Circ Cardiovasc Genet. 2017;10(3). https://doi.org/10.1161/CIRCGENETICS.116.001649.

  107. Ye Y, Chen X, Han J, Jiang W, Natarajan P, Zhao H. Interactions between enhanced polygenic risk scores and lifestyle for cardiovascular disease, diabetes, and lipid levels. Circ Genom Precis Med. 2021;14(1):e003128. https://doi.org/10.1161/CIRCGEN.120.003128.

    Article  CAS  PubMed  Google Scholar 

  108. Bolli A, Di Domenico P, Pastorino R, Busby GB, Botta G. Risk of coronary artery disease conferred by low-density lipoprotein cholesterol depends on polygenic background. Circulation. 2021. https://doi.org/10.1161/CIRCULATIONAHA.120.051843.

  109. Wand H, Lambert SA, Tamburro C, Iacocca MA, O'Sullivan JW, Sillari C, et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature. 2021;591(7849):211–9. https://doi.org/10.1038/s41586-021-03243-6.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

AT acknowledges additional support from NHGRI/NIH HG010881.

Funding

EDM and AT are supported by UL1TR002550 from NCATS/NIH to the Scripps Research Institute.

Author information

Authors and Affiliations

  1. Scripps Research Translational Institute, Scripps Research, 3344 N Torrey Pines Court, Suite 300, La Jolla, CA, 92037, USA

    Evan D. Muse, Shang-Fu Chen & Ali Torkamani

  2. Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA, 92037, USA

    Evan D. Muse

Authors
  1. Evan D. Muse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shang-Fu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Torkamani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Torkamani.

Ethics declarations

Conflict of Interest

Evan D. Muse and Ali Torkamani are co-founders of GeneXwell. In addition, Dr. Torkamani has a patent pending on polygenic risk score generation and adjustment. Mr. Chen has nothing to disclose.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Cardiovascular Genomics

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Muse, E.D., Chen, SF. & Torkamani, A. Monogenic and Polygenic Models of Coronary Artery Disease. Curr Cardiol Rep 23, 107 (2021). https://doi.org/10.1007/s11886-021-01540-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11886-021-01540-0

Keywords

Search

Navigation