Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies.

ABSTRACT: Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT

SUBMITTER: McCaw ZR 

PROVIDER: S-EPMC8643141 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature