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Genet Epidemiol. 2019 Apr;43(3):263-275. doi: 10.1002/gepi.22188. Epub 2019 Jan 17.

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

Author information

1
Department of Medicine, Harvard Medical School, Boston, Massachusetts.
2
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts.
3
Department of Biostatistics, University of Washington, Seattle, Washington.
4
Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania.
5
Department of Oral Biology, School of Dental Medicine, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania.
6
Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
7
Department of Odontology, Umeå University, Umeå, Sweden.
8
Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland.
9
Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois.
10
Department of Genetics, University of North Carolina, Chapel Hill, North Carolina.
11
Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado.
12
Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi.
13
Department of Medicine, Boston University Schools of Medicine, Boston, Massachusetts.
14
Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
15
Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts.
16
Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.
17
Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington.

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.

KEYWORDS:

rank-normalization; rare variants; whole-genome sequencing.

PMID:
30653739
PMCID:
PMC6416071
[Available on 2020-04-01]
DOI:
10.1002/gepi.22188
[Indexed for MEDLINE]

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