Cardiovascular disease: The rise of the genetic risk score

1. Kannel WB. Factors of Risk in the Development of Coronary Heart Disease—Six-Year Follow-up Experience: The Framingham Study. Ann Intern Med. 1961;55 : 33. 13751193

2. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97 : 1837–1847. 9603539

3. Gluckman TJ, Kovacs RJ, Stone NJ, Damalas D, Mullen JB, Oetgen WJ. The ASCVD Risk Estimator App: From Concept to the Current State. J Am Coll Cardiol. 2016;67 : 350–352. doi: 10.1016/j.jacc.2015.10.068 26796407

4. 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 : 572–580. doi: 10.1016/S0140-6736(12)60312-2 22607825

5. Morrison AC, Bare LA, Chambless LE, Ellis SG, Malloy M, Kane JP, et al. Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2007;166 : 28–35. doi: 10.1093/aje/kwm060 17443022

6. Liu C, Kraja AT, Smith JA, Brody JA, Franceschini N, Bis JC, et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet. 2016;48 : 1162–1170. doi: 10.1038/ng.3660 27618448

7. Preuss M, König IR, Thompson JR, Erdmann J, Absher D, Assimes TL, et al. Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) StudyClinical Perspective: A Genome-Wide Association Meta-analysis Involving More Than 22 000 Cases and 60 000 Controls. Circulation: Genomic and Precision Medicine. 2010;3 : 475–483.

8. Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376 : 1393–1400. doi: 10.1016/S0140-6736(10)61267-6 20971364

9. Ganna A, Magnusson PKE, Pedersen NL, de Faire U, Reilly M, Arnlöv J, et al. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler Thromb Vasc Biol. 2013;33 : 2267–2272. doi: 10.1161/ATVBAHA.113.301218 23685553

10. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27 : 112–157.

11. Pepe MS, Fan J, Feng Z, Gerds T, Hilden J. The Net Reclassification Index (NRI): a Misleading Measure of Prediction Improvement Even with Independent Test Data Sets. Stat Biosci. 2015;7 : 282–295. doi: 10.1007/s12561-014-9118-0 26504496

12. Hilden J, Gerds TA. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med. 2014;33 : 3405–3414. doi: 10.1002/sim.5804 23553436

13. Kerr KF, Janes H. First things first: risk model performance metrics should reflect the clinical application. Stat Med. 2017;36 : 4503–4508. doi: 10.1002/sim.7341 29156498

14. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461 : 747–753. doi: 10.1038/nature08494 19812666

15. A Decade Later, Human Genome Project Yields Few New Cures. The New York Times. 12 Jun 2010. Available from: http://www.nytimes.com/2010/06/13/health/research/13genome.html?pagewanted=all. Accessed 19 Feb 2018.

16. Collins R. What makes UK Biobank special? Lancet. 2012;379 : 1173–1174. doi: 10.1016/S0140-6736(12)60404-8 22463865

17. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med. 2015;12(3): e1001779. doi: 10.1371/journal.pmed.1001779 25826379

18. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over one million people identifies 535 novel loci for blood pressure [Internet]. bioRxiv. 2017. p. 198234. doi: 10.1101/198234

19. Zhang Y, Qi G, Park J-H, Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future [Internet]. bioRxiv. 2017. p. 175406. doi: 10.1101/175406

20. 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 : 2349–2358. doi: 10.1056/NEJMoa1605086 27959714

21. Khera AV, Chaffin M, Aragam K, Emdin CA, Klarin D, Haas M, et al. Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease [Internet]. bioRxiv. 2017. p. 218388. doi: 10.1101/218388

22. Inouye M, Abraham G, Nelson CP, Wood AM, Sweeting MJ, Dudbridge F, et al. Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention [Internet]. bioRxiv. 2018. doi: 10.1101/250712

23. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB. Rare variants create synthetic genome-wide associations. PLoS Biol. 2010;8(1): e1000294. doi: 10.1371/journal.pbio.1000294 20126254

24. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536 : 41–47. doi: 10.1038/nature18642 27398621

25. Assimes TL, Roberts R. Genetics: Implications for Prevention and Management of Coronary Artery Disease. J Am Coll Cardiol. 2016;68 : 2797–2818. doi: 10.1016/j.jacc.2016.10.039 28007143

26. Fontaine-Bisson B, Renström F, Rolandsson O, MAGIC, Payne F, Hallmans G, et al. Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population. Diabetologia. 2010;53 : 2155–2162. doi: 10.1007/s00125-010-1792-y 20571754

27. de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, Grant RW, et al. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care. 2011;34 : 121–125. doi: 10.2337/dc10-1265 20889853

28. Dewey FE, Grove ME, Pan C, Goldstein BA, Bernstein JA, Chaib H, et al. Clinical interpretation and implications of whole-genome sequencing. JAMA. 2014;311 : 1035–1045. doi: 10.1001/jama.2014.1717 24618965

29. Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, et al. Clinical assessment incorporating a personal genome. Lancet. 2010;375 : 1525–1535. doi: 10.1016/S0140-6736(10)60452-7 20435227

30. Whelton PK, Carey RM, Aronow WS, Casey DE Jr, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2017; doi: 10.1016/j.jacc.2017.11.006 29146535

31. Nayor M, Vasan RS. Recent Update to the US Cholesterol Treatment Guidelines: A Comparison With International Guidelines. Circulation. 2016;133 : 1795–1806. doi: 10.1161/CIRCULATIONAHA.116.021407 27143546

32. Stone NJ, Robinson JG, Lichtenstein AH, Merz CNB, Blum CB, Eckel RH, 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. J Am Coll Cardiol. 2014;63 : 2889–2934. doi: 10.1016/j.jacc.2013.11.002 24239923

33. Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. 2011;364 : 524–534. doi: 10.1056/NEJMoa1011893 21226570

34. Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, et al. Genetic Predisposition to High Blood Pressure and Lifestyle Factors: Associations With Midlife Blood Pressure Levels and Cardiovascular Events. Circulation. 2018;137 : 653–661. doi: 10.1161/CIRCULATIONAHA.117.030898 29254930

35. 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. doi: 10.3389/fcvm.2017.00053 28856136

36. Goldstein BA, Knowles JW, Salfati E, Ioannidis JPA, Assimes TL. Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example. Front Genet. 2014;5 : 254. doi: 10.3389/fgene.2014.00254 25136350

37. Knowles JW, Assimes TL, Kiernan M, Pavlovic A, Goldstein B, Yank V, et al. Randomized trial of personal genomics for preventive cardiology: design and challenges. Circ Cardiovasc Genet. 2012;5 : 368–376. doi: 10.1161/CIRCGENETICS.112.962746 22715281

38. Kullo IJ, Jouni H, Austin EE, Brown S-A, Kruisselbrink TM, Isseh IN, et al. Incorporating a Genetic Risk Score into Coronary Heart Disease Risk Estimates: Effect on LDL Cholesterol Levels (the MIGENES Clinical Trial). Circulation. 2016; CIRCULATIONAHA.115.020109.

39. O’Malley PG, Feuerstein IM, Taylor AJ. Impact of electron beam tomography, with or without case management, on motivation, behavioral change, and cardiovascular risk profile: a randomized controlled trial. JAMA. 2003;289 : 2215–2223. doi: 10.1001/jama.289.17.2215 12734132

40. Johnson HM, Einerson J, Korcarz CE, Aeschlimann SE, Stein JH. Long-term effects of carotid screening on patient outcomes and behaviors. Arch Intern Med. 2011;171 : 589–591. doi: 10.1001/archinternmed.2011.90 21444853

41. McEvoy JW, Blaha MJ, Nasir K, Yoon YE, Choi E-K, Cho I-S, et al. Impact of coronary computed tomographic angiography results on patient and physician behavior in a low-risk population. Arch Intern Med. 2011;171 : 1260–1268. doi: 10.1001/archinternmed.2011.204 21606093

42. McConnell MV, Shcherbina A, Pavlovic A, Homburger JR, Goldfeder RL, Waggot D, et al. Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study. JAMA Cardiol. 2017;2 : 67–76. doi: 10.1001/jamacardio.2016.4395 27973671

43. MyGeneRank | Unlock your genetic risk [Internet]. [cited 19 Feb 2018]. Available from: https://mygenerank.scripps.edu/

44. Grady C, Cummings SR, Rowbotham MC, McConnell MV, Ashley EA, Kang G. Informed Consent. N Engl J Med. 2017;376 : 856–867. doi: 10.1056/NEJMra1603773 28249147

45. Ashley EA, Hershberger RE, Caleshu C, Ellinor PT, Garcia JGN, Herrington DM, et al. Genetics and cardiovascular disease: a policy statement from the American Heart Association. Circulation. 2012;126 : 142–157. doi: 10.1161/CIR.0b013e31825b07f8 22645291

46. Ng PC, Murray SS, Levy S, Venter JC. An agenda for personalized medicine. Nature. 2009;461 : 724–726. doi: 10.1038/461724a 19812653

47. Regalado A. 23andMe to Share DNA Data with Researchers Using Apple iPhone. MIT Technology Review. 21 Mar 2016. Available from: https://www.technologyreview.com/s/601082/23andme-to-share-dna-data-with-researchers-using-apple-iphone/. Accessed 20 Feb 2018.

48. Hudson KL, Holohan MK. Keeping pace with the times—the Genetic Information Nondiscrimination Act of 2008. N Engl J Med 2008; 358 : 2661–2663 Available from: http://www.nejm.org/doi/full/10.1056/NEJMp0803964 doi: 10.1056/NEJMp0803964 18565857

49. Kraft SA, Constantine M, Magnus D, Porter KM, Lee SS-J, Green M, et al. A randomized study of multimedia informational aids for research on medical practices: Implications for informed consent. Clin Trials. 2017;14 : 94–102. doi: 10.1177/1740774516669352 27625314

50. Dewey FE, Chen R, Cordero SP, Ormond KE, Caleshu C, Karczewski KJ, et al. Phased Whole-Genome Genetic Risk in a Family Quartet Using a Major Allele Reference Sequence. PLoS Genet. 2011;7(9): e1002280. doi: 10.1371/journal.pgen.1002280 21935354