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Biometrics. 2019 Dec 28. doi: 10.1111/biom.13214. [Epub ahead of print]

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

Author information

1
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
2
Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts.
3
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts.
4
Department of Statistics, Harvard University, Cambridge, Massachusetts.

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 depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN.

KEYWORDS:

direct and indirect rank-based inverse normal transformation; nonnormality; omnibus test; quantitative traits; transformation; type I error rate

PMID:
31883270
DOI:
10.1111/biom.13214

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