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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Related Experiment Video

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data.

Brad Verhulst1, Joshua N Pritikin2, James Clifford3

  • 1Department of Psychiatry and Behavioral Sciences, Texas A&M University, College Station, TX, USA. verhulst@tamu.edu.

Behavior Genetics
|April 26, 2021
PubMed
Summary
This summary is machine-generated.

Gene-environment interactions (GxE) influence complex traits. Our new framework analyzes GxE using genome-wide association studies (GWAS) data, revealing how genetic effects on alcohol use vary with age.

Keywords:
Alcohol use frequencyGene-environment interaction (GxE)Genetic marginal effectsGenome-wide association study (GWAS)

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Area of Science:

  • Genetics
  • Behavioral Science
  • Biostatistics

Background:

  • Gene-environment interactions (GxE) are crucial for understanding complex traits.
  • Genome-wide GxE studies in humans are underutilized due to methodological challenges.
  • Research in model organisms highlights the importance of environmental context in genetic associations.

Purpose of the Study:

  • To present a novel framework for analyzing GxE using raw genome-wide association studies (GWAS) data.
  • To apply this framework to investigate gene-by-age interactions for alcohol use frequency.
  • To clarify the interpretation of GxE associations by calculating genetic marginal effects.

Main Methods:

  • Utilized raw data from genome-wide association studies (GWAS).
  • Developed and applied a framework for gene-by-environment (GxE) interaction analysis.
  • Treated respondent age as a continuous environmental moderator for alcohol use frequency in the UK Biobank dataset.
  • Calculated genetic marginal effects to interpret GxE associations across the environmental gradient.

Main Results:

  • Genetic associations with alcohol use frequency significantly vary across different ages.
  • Marginal genetic effects provide a clearer interpretation of GxE than interaction coefficients alone.
  • The proposed GxE GWAS method requires larger sample sizes than standard GWAS but offers deeper insights.

Conclusions:

  • The presented framework effectively analyzes GxE using GWAS data, offering enhanced interpretability.
  • Genetic influences on alcohol use frequency are age-dependent, a finding clarified by marginal effects.
  • Future GxE GWAS can leverage large-scale population studies and consortia to achieve necessary statistical power.