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PLoS One. 2018 Aug 22;13(8):e0201140. doi: 10.1371/journal.pone.0201140. eCollection 2018.

Case-control studies of gene-environment interactions. When a case might not be the case.

Author information

1
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States of America.
2
National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America.
3
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States of America.
4
Applied Mathematics and Computer Science Department, Belarusian State University, Minsk, Belarus.
5
Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.
6
Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, United States of America.

Abstract

Case-control Genome-Wide Association Studies (GWAS) provide a rich resource for studying the genetic architecture of complex diseases. A key is to elucidate how the genetic effects vary by the environment, what is traditionally defined by Gene-Environment interactions (GxE). The overlooked complication is that multiple, distinct pathophysiologic mechanisms may lead to the same clinical diagnosis and often these mechanisms have distinct genetic bases. In this paper, we first show that using the clinically diagnosed status can lead to severely biased estimates of GxE interactions in situations when the frequency of the pathologic diagnosis of interest, as compared to other diagnoses, depends on the environment. We then propose a pseudo-likelihood solution to correct the bias. Finally, we demonstrate our method in extensive simulations and in a GWAS of Alzheimer's disease.

PMID:
30133451
PMCID:
PMC6104951
DOI:
10.1371/journal.pone.0201140
[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

The authors have declared that no competing interests exist.

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