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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.

Gang Liu1, Bhramar Mukherjee1, Seunggeun Lee1

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.

American Journal of Epidemiology
|June 22, 2017
PubMed
Summary

This study introduces an empirical Bayes approach for additive gene-environment interaction, improving statistical power and controlling type I error in case-control studies, especially when gene-environment independence is violated.

Keywords:
bias-variance tradeoffeffect modificationempirical Bayes estimationgenetic risk scorerelative excess riskshrinkage

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Genetic Epidemiology

Background:

  • Additive interaction is proposed as a more relevant public health measure than multiplicative interaction.
  • Gene-environment independence assumption enhances statistical power for multiplicative interaction but can cause bias and inflated type I error if violated.

Purpose of the Study:

  • To extend the empirical Bayes (EB) approach to the additive scale for gene-environment interaction analysis.
  • To develop an EB estimator for relative excess risk due to interaction and a corresponding Wald test.
  • To evaluate the impact of gene-environment association on statistical tests in case-control studies.

Main Methods:

  • Extension of the empirical Bayes (EB) approach to the additive scale.
  • Derivation of an EB estimator for relative excess risk due to interaction.
  • Proposal of a Wald test within a general regression and retrospective likelihood framework.
  • Simulation studies to assess performance under varying gene-environment association.

Main Results:

  • The EB approach adaptively uses the gene-environment independence assumption.
  • The EB method offers increased power compared to standard logistic regression.
  • The EB approach provides better control of type I error than analyses assuming gene-environment independence.

Conclusions:

  • The proposed EB method offers a robust approach for analyzing additive gene-environment interaction in case-control studies.
  • This method balances bias and efficiency, particularly when gene-environment independence does not hold.
  • The EB approach enhances statistical power and error control for public health measures of interaction.