Dataset Information

A Simple Approximation to Bias in Gene-Environment Interaction Estimates When a Case Might Not Be the Case.

ABSTRACT: Case-control genetic association studies are often used to examine the role of the genetic basis in complex diseases, such as cancer and neurodegenerative diseases. The role of the genetic basis might vary by nongenetic (environmental) measures, what is traditionally defined as gene-environment interactions (G×E). A commonly overlooked complication is that the set of clinically diagnosed cases might be contaminated by a subset with a nuisance pathologic state that presents with the same symptoms as the pathologic state of interest. The genetic basis of the pathologic state of interest might differ from that of the nuisance pathologic state. Often, frequencies of the pathologically defined states within the clinically diagnosed set of cases vary by the environment. We derive a simple and general approximation to bias in G×E parameter estimates when the presence of the nuisance pathologic state is ignored. We then perform extensive simulation studies to show that ignoring the presence of the nuisance pathologic state can result in substantial bias in G×E estimates and that the approximation we derived is reasonably accurate in finite samples. We demonstrate the applicability of the proposed approximation in a study of Alzheimer's disease.

SUBMITTER: Lobach I

PROVIDER: S-EPMC6812609 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Simple Approximation to Bias in Gene-Environment Interaction Estimates When a Case Might Not Be the Case.

Lobach Iryna I Kim Inyoung I Alekseyenko Alexander A Lobach Siarhei S Zhang Li L

Frontiers in genetics 20191009

Case-control genetic association studies are often used to examine the role of the genetic basis in complex diseases, such as cancer and neurodegenerative diseases. The role of the genetic basis might vary by nongenetic (environmental) measures, what is traditionally defined as gene-environment interactions (G×E). A commonly overlooked complication is that the set of clinically diagnosed cases might be contaminated by a subset with a <i>nuisance</i> pathologic state that presents with the same s ...[more]

PMID: 31681402

Similar Datasets

Project description:Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.

Dataset Information

A Simple Approximation to Bias in Gene-Environment Interaction Estimates When a Case Might Not Be the Case.

Publications

A Simple Approximation to Bias in Gene-Environment Interaction Estimates When a Case Might Not Be the Case.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets