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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...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Epistasis Analysis01:09

Epistasis Analysis

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...
Genetic Screens02:46

Genetic Screens

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.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Detecting gene-environment interactions using a combined case-only and case-control approach.

Dalin Li1, David V Conti

  • 1Department of Preventive Medicine and Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA

American Journal of Epidemiology
|December 17, 2008
PubMed
Summary

Bayes model averaging improves gene-environment interaction detection by combining case-control and case-only analyses. This method offers greater statistical power and reduces bias when genetic and environmental factors are not independent.

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Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Bioinformatics

Background:

  • Traditional case-control studies for gene-environment interactions have limited statistical power.
  • Case-only analyses are powerful but sensitive to violations of independence between genetic and environmental factors.

Purpose of the Study:

  • To introduce and evaluate Bayes model averaging as a method to integrate case-control and case-only analyses for detecting gene-environment interactions.
  • To assess the performance of Bayes model averaging in terms of statistical power and robustness to assumption violations.

Main Methods:

  • Gene-environment interaction analyses framed within a log-linear model framework.
  • Bayes model averaging used to combine case-control and case-only models.
  • Simulation studies to evaluate performance under various scenarios of factor independence.
  • Data analysis using an example dataset.

Main Results:

  • Bayes model averaging demonstrated higher statistical power than case-control analysis when no prior information on factor independence was available.
  • The approach significantly reduced bias and Type I error rates compared to case-only analysis when genetic and environmental factors were not independent.
  • Prior specification can further enhance power or robustness.

Conclusions:

  • Bayes model averaging provides a powerful and robust alternative for detecting gene-environment interactions.
  • This method effectively addresses limitations of conventional case-control and case-only designs, particularly when independence assumptions are violated.