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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

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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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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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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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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

Updated: Apr 3, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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An Efficient Genome-Wide Multilocus Epistasis Search.

Hanni P Kärkkäinen1, Zitong Li2, Mikko J Sillanpää3

  • 1Department of Biology and Biocenter Oulu, University of Oulu, Oulu FIN-90014, Finland.

Genetics
|September 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel genome-wide association method for epistasis detection. It efficiently identifies pairwise interactions by combining sure independence screening with Bayesian LASSO, outperforming existing tools in simulations and real data.

Keywords:
epistasisextended Bayesian LASSOgenome-wide datamultilocus association modelsure independence screening

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWAS) increasingly analyze complex traits.
  • Identifying pairwise locus-by-locus (epistasis) interactions is crucial but computationally challenging.
  • Existing methods struggle with the high dimensionality of multilocus interaction analysis.

Purpose of the Study:

  • To develop a computationally efficient method for detecting epistasis in GWAS.
  • To reduce the dimensionality of genome-wide epistasis analysis.
  • To compare the proposed method with existing software like PLINK.

Main Methods:

  • Utilizes sure independence screening (SIS) to pre-select relevant interaction terms.
  • Employs an extended Bayesian least absolute shrinkage and selection operator (LASSO) model for multilocus association.
  • Incorporates fast generalized expectation-maximization estimation algorithms for efficient computation.

Main Results:

  • The proposed approach effectively reduces the dimensionality of epistasis analysis.
  • Demonstrated potential in simulated datasets from the Quantitative Trait Loci Mapping and Marker Assisted Selection (QTLMAS) Workshop 2008.
  • Showed comparable or superior performance to PLINK on real pig datasets.

Conclusions:

  • The developed method offers a powerful and efficient tool for genome-wide epistasis detection.
  • This approach facilitates the identification of complex genetic architectures underlying traits.
  • It provides a valuable alternative for researchers in statistical genomics and quantitative genetics.