title: The effect of phenotypic outliers and non-normality on rare-variant association testing.
abstract: Rare-variant association studies (RVAS) have made important contributions to human complex trait genetics. These studies rely on specialized statistical methods for analyzing rare-variant associations, both individually and in aggregate. We investigated the impact that phenotypic outliers and non-normality have on the performance of rare-variant association testing procedures. Ignoring outliers or non-normality can significantly inflate Type I error rates. We found that rank-based inverse normal transformation (INT) and trait winsorisation were both effective at maintaining Type I error control without sacrificing power in the presence of outliers. INT was the optimal method for non-normally distributed traits. For RVAS of quantitative traits with outliers or non-normality, we recommend using INT to transform phenotypic values before association testing.
authors.affiliation: Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
authors.forename: Paul L
authors.initials: PL
authors.lastname: Auer
authors.affiliation: Department of Epidemiology, University of Washington, Seattle, WA, USA.
authors.forename: Alex P
authors.initials: AP
authors.lastname: Reiner
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
authors.forename: Suzanne M
authors.initials: SM
authors.lastname: Leal
mesh_terms: D000483:Alleles; D015982:Bias; D055106:Genome-Wide Association Study / Q000379:methods* / Q000592:standards; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D059645:Mutation Rate; D010641:Phenotype