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Epistasis Analysis

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...

Pleiotropy

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Epistasis

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...

Genome-wide Association Studies-GWAS

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 26, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Detecting pleiotropy and epistasis using variance components linkage analysis in jPAP.

Sandra J Hasstedt¹, Alun Thomas

¹Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA. sandy@genetics.utah.edu

|December 23, 2011

Summary

The Java Pedigree Analysis Package (jPAP) enables variance components linkage analysis for quantitative and discrete traits. It supports multivariate and multi-locus analyses to identify pleiotropy and epistasis, with a user-friendly interface.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

Genetics
Bioinformatics
Statistical Genetics

Background:

Quantitative and discrete traits are crucial in genetic studies.
Understanding trait relationships (pleiotropy) and gene interactions (epistasis) requires advanced analytical tools.
Existing software may lack comprehensive features or user-friendliness for complex genetic analyses.

Purpose of the Study:

To introduce the Java Pedigree Analysis Package (jPAP) for advanced genetic linkage analysis.
To provide a tool for analyzing both quantitative and discrete traits, including multivariate and multi-locus scenarios.
To offer a user-friendly interface for complex genetic analyses.

Main Methods:

Variance components linkage analysis for quantitative traits.
Linkage analysis for discrete traits.
Multivariate linkage analysis for combined trait types.
Multi-quantitative trait loci (QTL) analysis for epistasis.

Main Results:

jPAP successfully performs variance components linkage analysis for single and multiple traits (quantitative and/or discrete).
The software facilitates the inference of pleiotropy between traits through multivariate analysis.
Epistasis between multiple QTL can be investigated using jPAP.
A graphical user interface enhances the usability of the package.

Conclusions:

jPAP is a versatile tool for genetic linkage analysis, accommodating diverse trait types and genetic models.
The package supports the investigation of complex genetic architectures, including pleiotropy and epistasis.
The user-friendly interface of jPAP makes advanced genetic analysis more accessible.