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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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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 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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A kernel regression approach to gene-gene interaction detection for case-control studies.

Nicholas B Larson1, Daniel J Schaid

  • 1Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.

Genetic Epidemiology
|July 23, 2013
PubMed
Summary

This study introduces novel gene-level score tests to identify gene-gene interactions contributing to complex traits. The new methods offer higher power than existing approaches for genetic association analysis.

Keywords:
epistasisgene-gene interactionkernel methodsscore testsvariance component

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Complex traits are influenced by gene-gene interactions, necessitating advanced genetic models beyond single-locus analysis.
  • Current single-marker methods for interaction analysis face high dimensionality and do not leverage gene as a functional unit.
  • Understanding gene-gene interactions is crucial for dissecting the genetic architecture of complex diseases.

Purpose of the Study:

  • To develop and evaluate a comprehensive family of gene-level score tests for identifying pairwise gene-gene interactions.
  • To assess the performance of these novel methods using simulations based on coalescent genetic models.
  • To provide a framework for genome-wide gene-gene interaction analysis in case-control studies.

Main Methods:

  • Utilized kernel machine methods to devise score-based variance component tests.
  • Employed a generalized linear mixed model framework for statistical analysis.
  • Conducted extensive simulations using coalescent genetic models to assess power and performance.

Main Results:

  • The proposed gene-level score tests demonstrated generally higher power compared to alternative gene-level approaches.
  • Performance was competitive with exhaustive single-nucleotide polymorphism (SNP)-level analyses.
  • Simulated epistatic effects led to significant marginal testing results for involved genes, even without true main effects.

Conclusions:

  • The developed gene-level score tests provide a powerful and efficient approach for detecting gene-gene interactions.
  • These methods effectively utilize gene-level information, overcoming limitations of single-marker analyses.
  • The findings support the utility of these methods for genome-wide association studies of complex traits and diseases.