| Title: | Operating Characteristics of the Rank-Based Inverse Normal Transformation for Quantitative Trait Analysis in Genome-Wide Association Studies |
| Journal: | Biometrics |
| Published: | 13 Jan 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31883270/ |
| DOI: | https://doi.org/10.1111/biom.13214 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643141 |
| Citations: | 144 (83 in last 2 years) as of 8 Aug 2024 |
Publication 5501
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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.</p>
5 Authors
- Zachary R. McCaw
- Jacqueline M. Lane
- Richa Saxena
- Susan Redline
- Xihong Lin
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