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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Testing for gene-environment interaction under exposure misspecification.

Ryan Sun1, Raymond J Carroll2,3, David C Christiani4

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, U.S.A.

Biometrics
|November 10, 2017
PubMed
Summary
This summary is machine-generated.

Modeling gene-environment interactions is crucial for understanding disease etiology. This study reveals that misspecifying environmental factors can lead to biased results in logistic regression, impacting gene-environment (GxE) interaction analysis.

Keywords:
Asymptotic biasGenome-wide environmental interaction studies (GWEIS)HeteroscedasticityModel misspecificationResampling methodsSandwich variance

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Complex diseases arise from gene-environment (GxE) interactions.
  • Accurate modeling of GxE interactions is vital but challenging due to unknown environmental exposure functions.
  • Misspecification of environmental effects can compromise the reliability of GxE interaction studies.

Purpose of the Study:

  • To investigate the impact of misspecifying environmental exposure effects on the inference of gene-environment (GxE) interaction terms.
  • To analyze the asymptotic bias of GxE interaction coefficients in linear and logistic regression models under various misspecification scenarios.
  • To evaluate the performance of standard variance estimators and propose improved testing procedures for GxE interactions.

Main Methods:

  • Examined asymptotic bias of GxE interaction coefficients in linear and logistic regression models.
  • Considered arbitrary misspecification of exposure and confounder effects.
  • Assessed the performance of robust sandwich variance estimators and developed an alternative testing procedure.

Main Results:

  • In linear regression, GxE interaction coefficients can remain unbiased under specific conditions (gene-environment independence, certain confounder dependencies) even with environmental misspecification.
  • However, statistical inference for GxE interactions may still be compromised.
  • In logistic regression, GxE interaction coefficients are generally biased if genetic factors directly or indirectly influence the outcome.
  • The standard robust sandwich variance estimator showed poor performance in practical GxE studies.

Conclusions:

  • Misspecification of environmental effects poses significant challenges for accurate GxE interaction inference, particularly in logistic regression.
  • Alternative testing procedures are necessary for reliable GxE interaction analysis in the presence of model misspecification.
  • This research highlights the need for careful consideration of environmental exposure modeling in genetic association studies.