Common Variants for Cardiovascular Disease
2017; Lippincott Williams & Wilkins; Volume: 135; Issue: 22 Linguagem: Inglês
10.1161/circulationaha.117.027798
ISSN1524-4539
Autores Tópico(s)RNA modifications and cancer
ResumoHomeCirculationVol. 135, No. 22Common Variants for Cardiovascular Disease Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBCommon Variants for Cardiovascular DiseaseClinical Utility Confirmed Steve E. Humphries, PhD Steve E. HumphriesSteve E. Humphries From the Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, UK. Originally published30 May 2017https://doi.org/10.1161/CIRCULATIONAHA.117.027798Circulation. 2017;135:2102–2105Article, see p 2091The increase in sample size of genome-wide association study (GWAS) meta-analyses has led to the identification now of >150 common variants/single-nucleotide polymorphisms (SNPs) that are robustly associated with cardiovascular disease (CVD), coronary heart disease (CHD), or CVD traits such as coronary calcification (http://www.ebi.ac.uk/gwas/). For example, in 2013, the CARDIoGRAMplusC4D Consortium, with 63 746 cases with coronary artery disease and 130 681 controls,1 identified 15 novel loci, taking the number of statistically robust CVD SNPs to 46, with a further 104 SNPs associated with CVD at a 5% false discovery rate. Together, these variants explained ≈10.6% of CVD heritability. Many of these loci identified are known to be involved in lipid metabolism,2 as would be expected from our knowledge of the importance of dyslipidemia in the development of CVD, with 12 of 46 CARDIoGRAMplusC4D SNPs showing a significant association with a lipid trait.These SNPs, located throughout the human genome, are common (frequency >5% for all), with the odds ratio for disease ranging from ≈30% higher risk for carriers of top-ranking GWAS risk variants at the chromosome 9p CDKN2A/2B locus to only 7% to 8% higher risk for carriers of SNPs at the loci for PCSK9 (chromosome 2) or for HNF1A (chromosome 12). Because all of these odds ratios are modest, questions arise as to what the potential clinical utility of these SNPs is and how we can use these genetic tools to explore the inherited contribution to CVD.Given the relatively small effect sizes associated with these risk loci, it is unsurprising that the addition of 1 variant into a classic risk factor (CRF) risk score does not result in improved predictive ability.3 This has led to the development of so-called genetic risk scores (GRSs) for which SNPs at independent loci are combined. A GRS can be unweighted, that is, the number of risk alleles carried by an individual at each locus is simply summed, but this assumes that the risk effect associated with each SNP is equal (and additive), and this clearly is not the case. A more accurate GRS can be constructed if carriage of the individual SNPs is weighted by use of the published effect size. An individual's GRS can then be combined with a CRF score such as the Framingham score or QRISK24 to give an individual's overall CVD risk estimate.Statin Benefit in Those at High Genetic RiskAn article published in this issue of Circulation5 uses ≈60 SNPs that were selected from GWASs to be significantly associated with CHD. A weighted GRS was calculated for each subject. The main analysis was carried out in 4910 subjects from the WOSCOPS (West of Scotland Coronary Prevention Study) randomized controlled trial of primary prevention with pravastatin (40 mg daily) therapy. The primary outcome was nonfatal myocardial infarction or death resulting from CHD, and subjects were followed up for a mean of 4.8 years in the trial and a further mean of 8.7 years out of the trial. Subjects designated at high genetic risk were those in the top quintile of the score, and they were compared with those in the lower 4 quintiles.In the placebo group, compared with the subjects at low genetic risk, the hazard ratio for CHD in those in the high genetic risk quintile was 1.62 (95% confidence interval, 1.29–2.05) after adjustment for CRFs. A 1-SD increase in score was associated with a 25% increase in CHD incidence. In the treated group, statin treatment reduced risk for a first CHD event in the high-score subjects by 44% compared with only a 25% reduction in the other subjects. This translated to a 7.9% risk reduction in the high-risk group compared with a 2.7% reduction in the others (P=0.04 for heterogeneity), with a number needed to treat of 13 in the high-score group versus 38 among others. It is worth noting that this difference was achieved with a similar (22%) reduction of low-density lipoprotein cholesterol in both groups.There was confirmatory analysis in 2 observational cohort studies. Each 1-SD increase in the GRS was associated with 1.32-fold (95% confidence interval, 1.04–1.68) greater likelihood of having coronary artery calcification in the CARDIA study (Coronary Artery Risk Development in Young Adults) of 1154 participants and with a 9.7% higher (95% confidence interval, 2.2–17.8) burden of carotid plaque in the BioImage Study of 4392 subjects. The authors concluded that those at highest genetic risk have a higher burden of subclinical atherosclerosis and with statin therapy will experience greater relative and absolute benefit to prevent a first CHD/CVD event.Can these data be extrapolated to other cohorts of patients? The subjects in WOSCOPS were all selected to have high low-density lipoprotein cholesterol (mean untreated levels, 192 mg/dL [5.3 mmol/L]) and thus would be expected to show a large benefit from statin therapy. It is interesting to note that the GRS was modestly associated with a family history of early CHD but not with baseline lipid levels, which is surprising because at least 15 of the included SNPs are in lipid genes (APOB, PCSK9, LDLR, APOE,etc). This perhaps reflects the utility of including so many SNPs that influence CVD through different pathways. WOSCOP subjects were all men, but there is no reason to believe that the score is not equally applicable in women, as shown in the other 2 cohorts examined here in which there was no sex difference in effects.In addition, several studies support the association of the GRS with risk of CVD in general population cohorts in both the United States6 and Europe.7,8 In the UCLEB study (University College-London School-Edinburgh-Bristol),7 data were drawn from 7 UK prospective studies including 11 851 individuals initially free of CVD with 1444 incident CVD events over 10 years of follow-up. With the use of 53 CVD GWAS SNPs and the QRISK2 CRF algorithm, the GRS showed only modest improvement in risk stratification, with additional benefit mainly in those at intermediate risk. Applying the GRS only to those with QRISK2 risk of 10% to 49 000 SNPs, the addition of the GRS to CRF scores significantly improved the 10-year risk prediction (P<0.001), particularly for individuals ≥60 years old (P<0.001). The GRS captured substantially different trajectories of absolute risk, with men in the top 20% of the GRS reaching a 10% CHD risk 12 to 18 years earlier than those in the bottom 20%.Motivation to Initiate Lipid-Lowering TherapyOne useful consequence of informing at-risk subjects of their genetic risk would be if such information motivates lifestyle changes or adherence to prescribed medication to a greater extent than CRF information alone, and a trial from the United States has examined this.9 Two hundred three subjects (mean age, 59 years) who were at intermediate risk for CHD and were not on statins were randomly assigned to receive their 10-year probability of CHD based on either a Framingham CRF score or CRF+GRS. The GRS included 28 CHD GWAS hit SNPs, with each weighted by its published odds ratio effect size. Subjects were told their risk was high, average, or low by a genetic counselor, followed by a discussion with a physician about starting statin therapy. After 6 months of follow-up, the CRF+GRS group had 9% lower low-density lipoprotein cholesterol than the CRF-only group (P=0.04), with particular benefit seen in those given a high overall risk. The reason was that more subjects in the high-risk group started statin therapy than in the CRF-only group (39% versus 22%; P 70% are intronic or intergenic and are therefore of unknown or possibly of no functional consequence. For example, using luciferase assays of promoter strength, we have recently shown that both the LDLR GWAS lead SNP rs6511720 located in intron 1 and only one of several other SNPs in the intron in complete linkage disequilibrium with this SNP are functional.12 Similarly, for the chromosome 9p21 locus, although molecular studies have reported interesting findings,13,14 the precise mechanism of action in the development of CVD remains unclear almost a decade after discovery. In particular, the actual functional SNP/SNPs at this locus have not been definitively identified. Thus, most of the SNPs included in the score are GWAS hits for which the lead SNP is unlikely to be the functional SNP at that risk locus. Linkage disequilibrium between the lead and functional SNPs may differ between ethnicities, meaning that some SNPs will be better proxies than others. This will reduce the ability of the weighted GRS to accurately reflect CVD risk, particularly in different ethnic groups if the linkage disequilibrium is less. Although analyzing every GWAS CVD hit locus in this way constitutes a considerable amount of work, this ultimately will provide the most accurate panel of functional SNPs for CVD risk prediction.Sources of FundingDr Humphries is a British Heart Foundation Professor and is funded by PG08/008 and by the National Institute for Health Research University College London Hospitals Biomedical Research Center.DisclosuresDr Humphries is the medical director and minority shareholder of a University College London spin-out company called StoreGene, which uses a 20-single-nucleotide polymorphism genetic test, in combination with the classic risk factor profile, for estimating an individual's future risk of cardiovascular disease. Dr Humphries has received no honoraria or speaker fees relevant to the topic of this article in the past 2 years.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Circulation is available at http://circ.ahajournals.org.Correspondence to: Steve E. Humphries, PhD, Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, 5 University Street, University College London, London, UK WC1E 6JF. E-mail [email protected]References1. CARDIoGRAMplusC4D Consortium, Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, Ingelsson E, Saleheen D, Erdmann J, Goldstein BA, Stirrups K, König IR, Cazier JB, Johansson A, Hall AS, Lee JY, Willer CJ, Chambers JC, Esko T, Folkersen L, Goel A, Grundberg E, Havulinna AS, Ho WK, Hopewell JC, Eriksson N, Kleber ME, Kristiansson K, Lundmark P, Lyytikäinen LP, Rafelt S, Shungin D, Strawbridge RJ, Thorleifsson G, Tikkanen E, Van Zuydam N, Voight BF, Waite LL, Zhang W, Ziegler A, Absher D, Altshuler D, Balmforth AJ, Barroso I, Braund PS, Burgdorf C, Claudi-Boehm S, Cox D, Dimitriou M, Do R; DIAGRAM Consortium; CARDIOGENICS Consortium, , Doney AS, El Mokhtari N, Eriksson P, Fischer K, Fontanillas P, Franco-Cereceda A, Gigante B, Groop L, Gustafsson S, Hager J, Hallmans G, Han BG, Hunt SE, Kang HM, Illig T, Kessler T, Knowles JW, Kolovou G, Kuusisto J, Langenberg C, Langford C, Leander K, Lokki ML, Lundmark A, McCarthy MI, Meisinger C, Melander O, Mihailov E, Maouche S, Morris AD, Müller-Nurasyid M, MuTHER Consortium, Nikus K, Peden JF, Rayner NW, Rasheed A, Rosinger S, Rubin D, Rumpf MP, Schäfer A, Sivananthan M, Song C, Stewart AF, Tan ST, Thorgeirsson G, van der Schoot CE, Wagner PJWellcome Trust Case Control Consortium, , Wells GA, Wild PS, Yang TP, Amouyel P, Arveiler D, Basart H, Boehnke M, Boerwinkle E, Brambilla P, Cambien F, Cupples AL, de Faire U, Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrières J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen SL, Holm H, Iribarren C, Jang Y, Kähönen M, Kee F, Kim HS, Klopp N, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lee JY, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Salomaa V, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Trégouët DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvänen AC, Hovingh GK, Dedoussis G, Franks PW, Lehtimäki T, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Perola M, Clarke R, Boehm BO, O'Donnell C, Reilly MP, März W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H, Samani NJ. Large-scale association analysis identifies new risk loci for coronary artery disease.Nat Genet. 2013; 45:25–33.CrossrefMedlineGoogle Scholar2. Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen LP, Magnusson PK, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O'Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney AS, Döring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJ, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin SY, Lindström J, Loos RJ, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stancáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YD, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin MR, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PE, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BH, Ordovas JM, Boerwinkle E, Palmer CN, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR. Discovery and refinement of loci associated with lipid levels.Nat Genet. 2013; 45:1274–1283.CrossrefMedlineGoogle Scholar3. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Brindle P. Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study.Heart. 2008; 94:34–39. doi: 10.1136/hrt.2007.134890.CrossrefMedlineGoogle Scholar4. Talmud PJ, Cooper JA, Palmen J, Lovering R, Drenos F, Hingorani AD, Humphries SE. Chromosome 9p21.3 coronary heart disease locus genotype and prospective risk of CHD in healthy middle-aged men.Clin Chem. 2008; 54:467–474. doi: 10.1373/clinchem.2007.095489.CrossrefMedlineGoogle Scholar5. Natarajan P, Young R, Stitziel NO, Padmanabhan S, Baber U, Mehran R, Sartori S, Fuster V, Reilly DF, Butterworth A, Rader DJ, Ford I, Sattar N, Kathiresan S. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting.Circulation. 2017; 135:2091–2101. doi: 10.1161/CIRCULATIONAHA.116.024436.LinkGoogle Scholar6. Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield MJ, Devlin JJ, Nordio F, Hyde CL, Cannon CP, Sacks FM, Poulter NR, Sever PS, Ridker PM, Braunwald E, Melander O, Kathiresan S, Sabatine MS. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials.Lancet. 2015; 385:2264–2271. doi: 10.1016/S0140-6736(14)61730-X.CrossrefMedlineGoogle Scholar7. Morris RW, Cooper JA, Shah T, Wong A, Drenos F, Engmann J, McLachlan S, Jefferis B, Dale C, Hardy R, Kuh D, Ben-Shlomo Y, Wannamethee SG, Whincup PH, Casas JP, Kivimaki M, Kumari M, Talmud PJ, Price JF, Dudbridge F, Hingorani AD, Humphries SE; UCLEB Consortium. Marginal role for 53 common genetic variants in cardiovascular disease prediction.Heart. 2016; 102:1640–1647. doi: 10.1136/heartjnl-2016-309298.CrossrefMedlineGoogle Scholar8. Abraham G, Havulinna AS, Bhalala OG, Byars SG, De Livera AM, Yetukuri L, Tikkanen E, Perola M, Schunkert H, Sijbrands EJ, Palotie A, Samani NJ, Salomaa V, Ripatti S, Inouye M. Genomic prediction of coronary heart disease.Eur Heart J. 2016; 37:3267–3278. doi: 10.1093/eurheartj/ehw450.CrossrefMedlineGoogle Scholar9. Kullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN, Haddad RA, Marroush TS, Shameer K, Olson JE, Broeckel U, Green RC, Schaid DJ, Montori VM, Bailey KR. 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; 133:1181–1188. doi: 10.1161/CIRCULATIONAHA.115.020109.LinkGoogle Scholar10. Smith AJ, Humphries SE, Talmud PJ. Identifying functional noncoding variants from genome-wide association studies for cardiovascular disease and related traits.Curr Opin Lipidol. 2015; 26:120–126. doi: 10.1097/MOL.0000000000000158.CrossrefMedlineGoogle Scholar11. MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J, Abecasis GR, Adams DR, Altman RB, Antonarakis SE, Ashley EA, Barrett JC, Biesecker LG, Conrad DF, Cooper GM, Cox NJ, Daly MJ, Gerstein MB, Goldstein DB, Hirschhorn JN, Leal SM, Pennacchio LA, Stamatoyannopoulos JA, Sunyaev SR, Valle D, Voight BF, Winckler W, Gunter C. Guidelines for investigating causality of sequence variants in human disease.Nature. 2014; 508:469–476. doi: 10.1038/nature13127.CrossrefMedlineGoogle Scholar12. Fairoozy RH, White J, Palmen J, Kalea AZ, Humphries SE. Identification of the functional variant(s) that explain the low-density lipoprotein receptor (LDLR) GWAS SNP rs6511720 association with lower LDL-C and risk of CHD.PLoS One. 2016; 11:e0167676. doi: 10.1371/journal.pone.0167676.CrossrefMedlineGoogle Scholar13. Miller CL, Pjanic M, Wang T, Nguyen T, Cohain A, Lee JD, Perisic L, Hedin U, Kundu RK, Majmudar D, Kim JB, Wang O, Betsholtz C, Ruusalepp A, Franzén O, Assimes TL, Montgomery SB, Schadt EE, Björkegren JL, Quertermous T. Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci.Nat Commun. 2016; 7:12092. doi: 10.1038/ncomms12092.CrossrefMedlineGoogle Scholar14. Almontashiri NA, Antoine D, Zhou X, Vilmundarson RO, Zhang SX, Hao KN, Chen HH, Stewart AF. 9p21.3 Coronary artery disease risk variants disrupt TEAD transcription factor-dependent transforming growth factor β regulation of p16 expression in human aortic smooth muscle cells.Circulation. 2015; 132:1969–1978. doi: 10.1161/CIRCULATIONAHA.114.015023.LinkGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Pihlstrøm H, Mjøen G, Mucha S, Franke A, Jardine A, Fellström B, Dahle D, Holdaas H and Melum E (2018) Genetic markers associated with long‐term cardiovascular outcome in kidney transplant recipients, American Journal of Transplantation, 10.1111/ajt.15191, 19:5, (1444-1451), Online publication date: 1-May-2019. 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Abouzid M, Kruszyna M, Burchardt P, Kruszyna Ł, Główka F and Karaźniewicz-Łada M (2021) Vitamin D Receptor Gene Polymorphism and Vitamin D Status in Population of Patients with Cardiovascular Disease—A Preliminary Study, Nutrients, 10.3390/nu13093117, 13:9, (3117) May 30, 2017Vol 135, Issue 22 Advertisement Article InformationMetrics © 2017 American Heart Association, Inc.https://doi.org/10.1161/CIRCULATIONAHA.117.027798PMID: 28559494 Originally publishedMay 30, 2017 KeywordsEditorialsgeneticscardiovascular diseasegenome-wide association studyPDF download Advertisement
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