A fully adjusted two-stage procedure for rank-normalization in genetic association studies

NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

When testing genotype–phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. Because genotypes are expected to have small effects (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice, no further adjustment is done in the second stage. Here, we show that this widely used approach can lead to tests with undesirable statistical properties, due to both combination of a mis-specified mean–variance relationship and remaining covariate associations between the rank-normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome-wide and whole-genome sequencing association studies. We further propose and evaluate an alternative fully adjusted two-stage approach that adjusts for covariates both when residuals are obtained and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.

Original languageEnglish (US)
Pages (from-to)263-275
Number of pages13
JournalGenetic epidemiology
Volume43
Issue number3
DOIs
StatePublished - Apr 2019
Externally publishedYes

Keywords

  • rank-normalization
  • rare variants
  • whole-genome sequencing

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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