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Related Concept Videos

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...
Epistasis01:39

Epistasis

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...
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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
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...

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Related Experiment Video

Updated: May 31, 2026

Quantifying Abdominal Pigmentation in Drosophila melanogaster
08:41

Quantifying Abdominal Pigmentation in Drosophila melanogaster

Published on: June 1, 2017

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

Tony Kam-Thong1, Benno Pütz, Nazanin Karbalai

  • 1Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

We developed a novel computational approach for detecting epistasis (gene-gene interactions) by exhaustively testing all single nucleotide polymorphism (SNP) pairs. This method leverages graphics processing units for efficient analysis, making complex genetic interaction studies feasible.

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Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) aim to identify genetic variants influencing human phenotypic variation.
  • Current analytical methods for single genetic loci are established.
  • Detecting epistasis (gene-gene interactions) remains computationally intensive due to the vast number of potential interactions.

Purpose of the Study:

  • To present a computationally feasible approach for epistasis detection.
  • To enable exhaustive testing of all single nucleotide polymorphism (SNP) pairs for interactions.
  • To identify complex genetic architectures underlying phenotypic differences.

Main Methods:

  • Exhaustive testing of all possible SNP pairs.
  • Utilizing the Hilbert-Schmidt Independence Criterion (HSIC) to assess statistical dependence between genetic markers and phenotypes.
  • Implementing the search strategy on highly parallelized graphics processing units (GPUs).

Main Results:

  • The developed approach enables the exhaustive testing of all SNP pairs for epistasis.
  • The HSIC-based strategy effectively delineates various forms of statistical dependence.
  • GPU acceleration makes the complete epistasis search feasible within a day.

Conclusions:

  • This GPU-accelerated method significantly advances the feasibility of large-scale epistasis detection.
  • The approach facilitates a more comprehensive understanding of genetic architectures.
  • It provides a powerful tool for identifying gene-gene interactions relevant to human traits.