Faculty Collaboration Database

The effect of phenotypic outliers and non-normality on rare-variant association testing. Eur J Hum Genet 2016 Aug;24(8):1188-94

Date

01/07/2016

Pubmed ID

26733287

Pubmed Central ID

PMC4970685

DOI

10.1038/ejhg.2015.270

Scopus ID

2-s2.0-84953258516 (requires institutional sign-in at Scopus site)   26 Citations

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.

Author List

Auer PL, Reiner AP, Leal SM

Author

Paul L. Auer PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Alleles
Bias
Genome-Wide Association Study
Humans
Models, Genetic
Mutation
Mutation Rate
Phenotype