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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...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: Jun 9, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

A general model for multilocus epistatic interactions in case-control studies.

Zhong Wang1, Tian Liu, Zhenwu Lin

  • 1Center for Statistical Genetics, Pennsylvania State University, Hershey, Pennsylvania, United States of America.

Plos One
|September 4, 2010
PubMed
Summary
This summary is machine-generated.

Epistasis, the interaction between multiple genes, is crucial for complex diseases. Our new model identifies high-order gene interactions, successfully detecting a three-locus epistasis in inflammatory bowel disease.

Related Experiment Videos

Last Updated: Jun 9, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Complex Diseases
  • Statistical Genetics

Background:

  • Epistasis, the interaction of alleles at different genetic loci, is hypothesized to be fundamental to the development and progression of complex diseases.
  • The intricate nature of complex diseases is likely influenced by a complex web of epistatic interactions involving numerous genes.

Purpose of the Study:

  • To develop a general statistical model for testing high-order epistatic interactions in complex diseases within a case-control study framework.
  • To integrate quantitative genetic theory of high-order epistasis into population-based case-control studies.
  • To enable the identification and testing of epistasis and its constituent genetic components.

Main Methods:

  • Developed a novel statistical model to assess high-order epistatic interactions.
  • Incorporated quantitative genetic theory for epistasis into a case-control study design.
  • Applied the model to analyze genetic data from a case-control study of inflammatory bowel disease.

Main Results:

  • Simulation studies demonstrated the model's power and controlled false positive rates across various sampling strategies.
  • The model successfully identified a significant three-locus epistasis in a case-control study.
  • Analysis involved genotyping five single nucleotide polymorphisms (SNPs) within a candidate gene.

Conclusions:

  • The developed model is effective for detecting high-order epistasis in complex diseases.
  • The findings highlight the importance of considering multi-locus interactions in genetic studies of complex diseases.
  • The study successfully identified a specific three-locus epistatic interaction relevant to inflammatory bowel disease.