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Genet Epidemiol. 2019 Apr;43(3):292-299. doi: 10.1002/gepi.22186. Epub 2019 Jan 8.

A simple approximation to the bias of gene-environment interactions in case-control studies with silent disease.

Author information

1
Department of Epidemiology and Biostatistics, University of California, San Francisco, California.
2
Biostatistics Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
3
Department of Applied Mathematics and Computer Science, Belarusian State University, Minsk, Belarus.
4
Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina.
5
Department of Mathematics, San Francisco State University, San Francisco, California.
6
Department of Medicine, University of California, San Francisco, California.

Abstract

One of the most important research areas in case-control Genome-Wide Association Studies is to determine how the effect of a genotype varies across the environment or to measure the gene-environment interaction (G × E). We consider the scenario when some of the "healthy" controls actually have the disease and when the frequency of these latent cases varies by the environmental variable of interest. In this scenario, performing logistic regression with the clinically diagnosed disease status as an outcome variable and will result in biased estimates of G × E interaction. Here, we derive a general theoretical approximation to the bias in the estimates of the G × E interaction and show, through extensive simulation, that this approximation is accurate in finite samples. Moreover, we apply this approximation to evaluate the bias in the effect estimates of the genetic variants related to mitochondrial proteins a large-scale prostate cancer study.

KEYWORDS:

approximation; bias; prostate cancer; silent disease

PMID:
30623487
PMCID:
PMC6416064
[Available on 2020-04-01]
DOI:
10.1002/gepi.22186
[Indexed for MEDLINE]

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