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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Genetic signal maximization using environmental regression.

Phillip E Melton1, Jack W Kent, Thomas D Dyer

  • 1Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX 78253, USA. pmelton@txbiomedgenetics.org.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for genetic epidemiology, optimizing genetic signals by accounting for environmental correlation. The approach enhances heritability for discrete traits by constraining regression coefficients.

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Area of Science:

  • Genetic Epidemiology
  • Statistical Genetics
  • Bioinformatics

Background:

  • Joint analyses in genetic epidemiology often overlook environmental correlations, focusing solely on genetic correlations.
  • Existing methods may not fully optimize the detection of genetic signals when environmental factors are involved.

Purpose of the Study:

  • To develop and evaluate a method that accounts for environmental correlation to enhance genetic signal detection.
  • To optimize genetic analyses by jointly analyzing discrete and quantitative traits, considering environmental noise.

Main Methods:

  • Utilized bivariate analyses to calculate heritability and environmental correlations using Genetic Analysis Workshop 17 (GAW17) family data.
  • Implemented a novel approach by constraining the beta coefficient in univariate models, incorporating the inverse of environmental correlations.
  • Conducted genetic association tests on 7,087 nonsynonymous SNPs for Affected status, comparing fixed vs. variable beta coefficients.

Main Results:

  • Calculated significant bivariate environmental correlations for quantitative traits Q1 (0.64 ± 0.09), Q2 (0.798 ± 0.076), and Q4 (-0.169 ± 0.18).
  • Observed improved heritability for Affected status in univariate models using the constrained beta coefficient, indicating enhanced genetic signal.
  • While no genome-wide significant associations were found, the constrained beta approach demonstrated a slight improvement in the genetic signal for Affected status.

Conclusions:

  • The proposed environmental regression approach effectively increases heritability for discrete traits by constraining the beta coefficient of correlated quantitative covariates.
  • Accounting for stochastic environmental effects through this method enhances the genetic signal, offering a more robust analysis in genetic epidemiology.