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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.

SUBMITTER: Auer PL

PROVIDER: S-EPMC4970685 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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The effect of phenotypic outliers and non-normality on rare-variant association testing.

Auer Paul L PL Reiner Alex P AP Leal Suzanne M SM

European journal of human genetics : EJHG 20160106 8

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 ...[more]

PMID: 26733287

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