Stroke Genome-Wide Association Studies
2010; Lippincott Williams & Wilkins; Volume: 41; Issue: 4 Linguagem: Inglês
10.1161/strokeaha.109.576769
ISSN1524-4628
Autores Tópico(s)Genomic variations and chromosomal abnormalities
ResumoHomeStrokeVol. 41, No. 4Stroke Genome-Wide Association Studies Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplementary MaterialsFree AccessEditorialPDF/EPUBStroke Genome-Wide Association StudiesThe Large Numbers Imperative James F. Meschia, MD James F. MeschiaJames F. Meschia From Department of Neurology, Mayo Clinic, Jacksonville, Fla. Originally published25 Feb 2010https://doi.org/10.1161/STROKEAHA.109.576769Stroke. 2010;41:579–580Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: February 25, 2010: Previous Version 1 See related article, pages 825–832.With a swiftly moving, highly technical field, it helps to have a reliable guide. In this issue of Stroke, Lanktree et al1 provide a timely overview of genomic analysis applied to stroke.1 Readers find a clear and concise synopsis of single-nucleotide polymorphisms, copy number variations, listings of the strengths and limitations of genome-wide association studies (GWAS), and a distillation of the findings from GWAS performed in 6 cohorts (5 of which were ischemic stroke cohorts). The review also covers a topic not usually found in clinical reviews, namely techniques to visually display quantitative information. Excellence in statistical graphics should, among other things, avoid distorting what the data have to say, present many numbers in a small space, and make large data sets coherent.2 The Manhattan and quantile–quantile plots are excellent examples of statistical graphics that have become invaluable for interpreting GWAS results.Along with visualization comes interpretation of data in the context of GWAS. Clinical investigators are well aware of the problem of multiple testing from such settings as interim and subgroup analyses in clinical trials, which can lead to wildly spurious conclusions, such as concluding that aspirin only helped individuals of certain astrological signs in the second International Study of Infarct Survival.3 GWAS simply escalates the problem of multiple testing by orders of magnitude. Because single-nucleotide polymorphism-based GWAS test hundreds of thousands of single-nucleotide polymorphisms per subject, a significant association requires a very low probability value. The Wellcome Trust Case Control Consortium used P<5×10−7 as the cut-off for genome-wide significance.4 Others have chosen to prespecify genome-wide significance with greater stringency at P<5×10−8, corresponding to the 5% significance level adjusting for the number of independent tests estimated in HapMap for individuals of European ancestry.5How has stroke performed in the significance "high-jump" competition? Not particularly well. Of the loci on chromosomes 4, 11, 12, 14, 16, and 22 with associations with ischemic stroke reported to have reached genome-wide significance, none has been replicated across studies.1 This may be the result of biases, including the so-called winner's curse.6 Differential effects under different exposures (eg, tobacco smoking), association with correlated phenotypes (eg, atrial fibrillation or diabetes mellitus), differences in ascertainment schemes, genotype misclassification, or marker polymorphism in variable linkage disequilibrium with the causative variant across populations may alternatively explain the heterogeneity.7There is what might be called a large numbers imperative when it comes to GWAS of a complex disorder like ischemic stroke. It is unlikely that any single study, even a multicenter study, will ever achieve the sample size necessary to yield a credible result. An uncommon level of worldwide collaboration must emerge. To put things in perspective, the Venice criteria state that earning an "A" rating in terms of amount of evidence in a genetic association study requires a sample size exceeding 1000 combined cases and controls (assuming a 1:1 ratio) in the least common genetic group of interest.8 The less common the risk allele is, the greater the sample size requirement.The stroke community is striving to meet the large numbers imperative. The Cohorts for Heart and Aging Research in Genomic Epidemiology, which consists of 5 community-based cohorts, recently reported the results of its ischemic stroke GWAS.9 The Wellcome Trust Case Control Consortium is currently conducting a 3-stage GWAS of ischemic stroke under the leadership of Dr Hugh Markus. Genome-wide genotyping has been completed for ≈4000 cases in stage 1. The Ischemic Stroke Genetics Consortium, a loose federation of investigators, first met in Boston, Massachusetts, on April 28, 2007. The Consortium now involves 73 investigators across 16 countries.10 The Consortium initially focused on large-scale candidate gene replication studies.11 It is currently turning its attention to organizing a GWAS.To meet the large numbers imperative, meta-analysis is almost unavoidable. There are reasons to believe that an appropriately powered meta-analysis in ischemic stroke is feasible. First, single-nucleotide polymorphisms imputation techniques have evolved such that results from studies using diverse gene chip platforms can be pooled without losing statistical power.12 This is important because it can save on the considerable expense of re-genotyping. Second, under certain conditions, meta-analysis of summary results can be as efficient statistically as joint analysis of individual participant data (also known as mega-analysis).13 This is important because many investigators might be unwilling or unable to share data at the individual participant level because of privacy concerns.The methodological advantages of cohort studies are well known.14 However, the large numbers imperative is not likely to be satisfied by compiling incident cases alone. Cohorts for Heart and Aging Research in Genomic Epidemiology consortium included <1200 incident cases of ischemic stroke.9 Stroke centers can more efficiently generate far larger numbers of cases of ischemic stroke, but concerns have been raised regarding the validity of case-control ischemic stroke GWAS. One concern about such studies relates to the possibility of Neyman (prevalence–incidence) bias.15 Such bias could be problematic if the genetic determinants of ischemic stroke are also those that increase risk of death from stroke and if large percentages of patients die before they have the opportunity to donate DNA. To date, no genetic variant has been shown to be an unequivocal determinant of both ischemic stroke and risk of early death from stroke. Further, the proportion of patients who die before being screened for enrollment in a genetic association study is likely small if recruitment occurs in the setting of an inpatient stroke service, particularly if surrogate consent is permitted.16Finally, sample size and phenotypic heterogeneity tend to be inversely related. Restrict the phenotype enough and stroke begins to look more like an orphan disease than a common disease. If studies include phenotypically diverse stroke, then it would behoove investigators to characterize strokes among the cases in great detail. Rather than forcing cases into a limited set of mutually exclusive categories, it might be more productive to capture within phenotypic data sets the results of studies that were performed to evaluate cardiac and cerebrovascular status along with the results of brain imaging. Semiautomated approaches like the Web-based Causative Classification of Stroke system can help to systematically structure diverse clinical data sets across multiple studies.17 However, restructuring clinical data sets with source documentation is laborious, time-consuming, and expensive. Other approaches to phenomics might be more efficient. Funding agencies also may need to consider the value of new case recruitment under a uniform protocol that includes deep phenotyping.As investigators strive to make definitive, consistently reproducible discoveries of the genetic determinants of ischemic stroke, one is likely to see GWAS of increasing sample size and increasing use of meta-analytic techniques.18The opinions in this editorial are not necessarily those of the editors or of the American Heart Association.Sources of FundingDr Meschia receives support for the Siblings with Ischemic Stroke Study (SWISS) from the National Institute of Neurological Disorders and Stroke (R01 NS39987).DisclosuresNone.FootnotesCorrespondence to James F. Meschia, MD, Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224. E-mail [email protected] References 1 Lanktree MB, Dichgans M, Hegele RA. Advances in genomic analysis of stroke: what have we learned and where are we headed? 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Int J Epidemiol. 2008; 37: 120–132.CrossrefMedlineGoogle Scholar9 Ikram MA, Seshadri S, Bis JC, Fornage M, DeStefano AL, Aulchenko YS, Debette S, Lumley T, Folsom AR, van den Herik EG, Bos MJ, Beiser A, Cushman M, Launer LJ, Shahar E, Struchalin M, Du Y, Glazer NL, Rosamond WD, Rivadeneira F, Kelly-Hayes M, Lopez OL, Coresh J, Hofman A, DeCarli C, Heckbert SR, Koudstaal PJ, Yang Q, Smith NL, Kase CS, Rice K, Haritunians T, Roks G, de Kort PL, Taylor KD, de Lau LM, Oostra BA, Uitterlinden AG, Rotter JI, Boerwinkle E, Psaty BM, Mosley TH, van Duijn CM, Breteler MM, Longstreth WT Jr, Wolf PA. Genomewide association studies of stroke. N Engl J Med. 2009; 360: 1718–1728.CrossrefMedlineGoogle Scholar10 Stroke genetics. Available at www.strokegenetics.org. Accessed January 1, 2010.Google Scholar11 Gschwendtner A, Bevan S, Cole JW, Plourde A, Matarin M, Ross-Adams H, Meitinger T, Wichmann E, Mitchell BD, Furie K, Slowik A, Rich SS, Syme PD, MacLeod MJ, Meschia JF, Rosand J, Kittner SJ, Markus HS, Muller-Myhsok B, Dichgans M. Sequence variants on chromosome 9p21.3 confer risk for atherosclerotic stroke. Ann Neurol. 2009; 65: 531–539.CrossrefMedlineGoogle Scholar12 Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007; 39: 906–913.CrossrefMedlineGoogle Scholar13 Lin DY, Zeng D. Meta-analysis of genome-wide association studies: No efficiency gain in using individual participant data. Genet Epidemiol. 34: 60–66.MedlineGoogle Scholar14 Grimes DA, Schulz KF. Cohort studies: marching towards outcomes. Lancet. 2002; 359: 341–345.CrossrefMedlineGoogle Scholar15 Grimes DA, Schulz KF. Bias and causal associations in observational research. Lancet. 2002; 359: 248–252.CrossrefMedlineGoogle Scholar16 Chen DT, Case LD, Brott TG, Brown RD Jr, Silliman SL, Meschia JF, Worrall BB. Impact of restricting enrollment in stroke genetics research to adults able to provide informed consent. Stroke. 2008; 39: 831–837.LinkGoogle Scholar17 Ay H, Benner T, Arsava EM, Furie KL, Singhal AB, Jensen MB, Ayata C, Towfighi A, Smith EE, Chong JY, Koroshetz WJ, Sorensen AG. A computerized algorithm for etiologic classification of ischemic stroke: the causative classification of stroke system. Stroke. 2007; 38: 2979–2984.LinkGoogle Scholar18 Zeggini E, Ioannidis JP. Meta-analysis in genome-wide association studies. 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April 2010Vol 41, Issue 4 Advertisement Article InformationMetrics https://doi.org/10.1161/STROKEAHA.109.576769PMID: 20185773 Originally publishedFebruary 25, 2010 Keywordsgenomicsmeta-analysisstrokegeneticssurvival biasPDF download Advertisement
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