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Genetic testing for early prediction of cardiovascular diseases: genome-wide association studies and polygenic score


Authors: Jaroslav A. Hubáček
Authors‘ workplace: III. interní klinika – klinika endokrinologie a metabolismu 1. LF UK a VFN v Praze ;  Centrum experimentální medicíny, Institut klinické a experimentální medicíny, Praha
Published in: AtheroRev 2020; 5(2): 88-92
Category:

Overview

Despite the important shifts in both knowledge and technological possibilities in the field of molecular genetics, there are still considerable reserves in their use in clinical practice. Whole-genome association studies detected a high number of previously unknown variants associated with cardiovascular disease and its risk factors. Some of them pointed to new important points in the metabolic pathways. The strongest risk variants increase CVD by approximately 40% (most by 5–25% “only”) and a similar effect is observed for genetic variants associated with CVD risk factors. The maximum individual SNP effect on cholesterol levels is about 0.3 mmol/l and for triglycerides about 0.25 mmol/l, for obesity usually 300–500 grams of body weight per risk allele, and in the case of smoking the risk for becoming a smoker increases by about 40%. Recently, the research is focused on simultaneous analysis of number of genetic variants to subsequently produce polygenic genetic risk scores that should be more precise in early (at 18–25 years of age?) disease prediction. These scores either use a simple sum of the risk alleles present (unweighted score) or take into account the relative risks (weighted gene score), usually based on OR, HR or β coefficients.

Keywords:

CVD – genetic – genome wide association studies – prediction


Sources
  1. Hubáček JA. Základy genetické determinace civilizačních onemocnění. Postgrad Med 2018; 20(1)1: 6–10.
  2. McPherson R, Tybjaerg-Hansen A. Genetics of coronary artery disease. Circ Res 2016; 118(4): 564–578. Dostupné z DOI: <http://dx.doi.org/10.1161/CIRCRESAHA.115.306566>.
  3. Rogers J. The finished genome sequence of Homo sapiens. Cold Spring Harb Symp Quant Biol 2003; 68(1): 1–11. Dostupné z DOI: <http://dx.doi.org/10.1101/sqb.2003.68.1>.
  4. Dehghan A. Genome-wide association studies. Methods Mol Biol 2018; 1793: 37–49. Dostupné z DOI: <http://dx.doi.org/10.1007/978–1-4939–7868–7_4>.
  5. Uitterlinden AG. An introduction to genome-wide association studies: GWAS for dummies. Semin Reprod Med 2016; 34(4): 196–204. Dostupné z DOI: <http://dx.doi.org/10.1055/s-0036–1585406>.
  6. Hubáček JA. Genetická determinace dyslipidemií – co přinesly výsledky celogenomových screeningů a další směry výzkumu. Vnitř Lék 2016; 62(11): 868–876.
  7. Dlouhá D, Hubáček JA. Gen pro FTO a jeho role v genetické determinaci obezity. Vnitř Lék 2012; 58(3): 208–215.
  8. Zhu Y, Zhou G, Yu X et al. LC-MS-MS quantitative analysis reveals the association between FTO and DNA methylation. PLoS One 2017; 12(4): e0175849. Dostupné z DOI: <http://dx.doi.org/10.1371/journal.pone.0175849>.
  9. Palomaki GE, Melillo S, Bradley LA. Association between 9p21 genomic markers and heart disease: a meta-analysis. JAMA 2010; 303(7): 648–656. Dostupné z DOI: <http://dx.doi.org/10.1001/jama.2010.118.
  10. Goettsch C, Kjolby M, Aikawa E. Sortilin and its multiple roles in cardiovascular and metabolic diseases. Arterioscler Thromb Vasc Biol 2018; 38(1): 19–25. Dostupné z DOI: <http://dx.doi.org/10.1161/ATVBAHA.117.310292>.
  11. Erdmann J, Kessler T, Munoz Venegas L et al. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc Res 2018; 114(9): 1241–1257. Dostupné z DOI: <http://dx.doi.org/10.1093/cvr/cvy084>.
  12. Clarke SL, Assimes TL. Genome-wide association studies of coronary artery disease: Recent progress and challenges ahead. Curr Atheroscler Rep 2018; 20(9): 47. Dostupné z DOI: <http://dx.doi.org/10.1007/s11883–018–0748–4>.
  13. Teslovich TM, Musunuru K, Smith AV et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010; 466(7307): 707–713. Dostupné z DOI: <http://dx.doi.org/10.1038/nature09270>.
  14. Needham M, Mastaglia FL. Statin myotoxicity: a review of genetic susceptibility factors. Neuromuscul Disord 2014; 24(1): 4–15. Dostupné z DOI: <http://dx.doi.org/10.1016/j.nmd.2013.09.011>.
  15. Tragante V, Hemerich D, Alshabeeb M et al. Druggability of coronary artery disease risk loci. Circ Genom Precis Med 2018; 11(8): e001977. Dostupné z DOI: <http://dx.doi.org/10.1161/CIRCGEN.117.001977>.
  16. Sarraju A, Knowles JW. Genetic testing and risk scores: Impact on familial hypercholesterolemia. Front Cardiovasc Med 2019; 6: 5. Dostupné z DOI: <http://dx.doi.org/10.3389/fcvm.2019.00005>.
  17. Krarup NT, Borglykke A, Allin KH et al. A genetic risk score of 45 coronary artery disease risk variants associates with increased risk of myocardial infarction in 6041 Danish individuals. Atherosclerosis 2015; 240(2): 305–310. Dostupné z DOI: <http://dx.doi.org/10.1016/j.atherosclerosis.2015.03.022>.
  18. Talmud PJ, Shah S, Whittall R 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): 1293–1301. Dostupné z DOI: <http://dx.doi.org/10.1016/S0140–6736(12)62127–8>.
  19. Hubáček JA, Vrablík M. Genetika dyslipidemií včera, dnes a zítra. Vnitř Lék 2007; 53(4): 371–376.
  20. Morris RW, Cooper JA, Shah T et al. Marginal role for 53 common genetic variants in cardiovascular disease prediction. Heart 2016; 102(20): 1640–1647. Dostupné z DOI: <http://dx.doi.org/10.1136/heartjnl-2016–309298>.
  21. Talmud PJ, Cooper JA, Morris RW et al. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes. 2015; 64(5): 1830–1840. Dostupné z DOI: <http://dx.doi.org/10.2337/db14–1504>.
  22. Hubacek JA, Dlouha D, Adamkova V et al. The gene score for predicting hypertriglyceridemia: New insights from a Czech case-control study. Mol Diagn Ther 2019; 23(4): 555–562. Dostupné z DOI: <http://dx.doi.org/10.1007/s40291–019–00412–2>.
  23. Thériault S, Lali R, Chong M et al. Polygenic contribution in individuals with early-onset coronary artery disease. Circ Genom Precis Med 2018; 11(1): e001849. Dostupné z DOI: <http://dx.doi.org/10.1161/CIRCGEN.117.001849>.
  24. Talmud PJ, Cooper JA, Morris RW et al. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes 2015; 64(5): 1830–1840. Dostupné z DOI: <http://dx.doi.org/10.2337/db14–1504>.
  25. Day FR, Loos RJ. Developments in obesity genetics in the era of genome-wide association studies. J Nutrigenet Nutrigenomics 2011; 4(4): 222–238. Dostupné z DOI: <http://dx.doi.org/1010.1159/000332158>.
  26. Hubáček JA. Apolipoprotein L1 – etnicky specifický determinant renálního a srdečního selhání. AtheroRev 2019; 4(3): 159–161.
  27. Benes LB, Brandt DJ, Brandt EJ et al. How genomics is personalizing the management of dyslipidemia and cardiovascular disease prevention. Curr Cardiol Rep 2018; 20(12): 138. Dostupné z DOI: <http://dx.doi.org/10.1007/s11886–018–1079–3>.
  28. Whayne TF Jr, Saha SP. Genetic risk, adherence to a healthy lifestyle, and ischemic heart disease. Curr Cardiol Rep 2019; 21(1): 1. Dostupné z DOI: <http://dx.doi.org/10.1007/s11886–019–1086-z>.
  29. Corella D, Coltell O, Mattingley G et al. Utilizing nutritional genomics to tailor diets for the prevention of cardiovascular disease: a guide for upcoming studies and implementations. Expert Rev Mol Diagn 2017; 17(5): 495–513. Dostupné z DOI: <http://dx.doi.org/10.1080/14737159.2017.1311208>.
Labels
Angiology Diabetology Internal medicine Cardiology General practitioner for adults

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