Stroke Genome-Wide Association Studies

Abstract

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 …