Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

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...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Heritability

Heritability

Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...

Hardy-Weinberg Principle

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.

Punnett Squares

Punnett Squares

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Innovation in animal health under Regulation (EU) 2019/6: Review and recommendations.

Frontiers in veterinary science·2026

Same author

Discovery of gene-alcohol interaction loci influencing blood pressure in 1.1 million individuals from multiple populations.

Research square·2026

Same author

Cross-organ analysis reveals associations between vascular properties of the retina, the carotid and aortic arteries, and the brain.

Communications medicine·2026

Same author

Bidirectional genetic and phenotypic links between smoking and striatal iron content involving dopaminergic and inflammatory pathways.

Addiction (Abingdon, England)·2026

Same author

Large-Scale Gene-Smoking Interactions and Fine Mapping Study Identifies Multiple Novel Blood Pressure Loci in over 1 Million Individuals.

medRxiv : the preprint server for health sciences·2026

Same author

PLUME-OCT: A quality control tool to enhance statistical power in the analysis of biomarkers from 3D biomedical datasets of OCT images.

Computers in biology and medicine·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026

Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026

Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026

Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

FastEpistasis: a high performance computing solution for quantitative trait epistasis.

Thierry Schüpbach¹, Ioannis Xenarios, Sven Bergmann

¹Vital-IT Group, Molecular Modeling Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Bioinformatics (Oxford, England)

|April 9, 2010

Summary

This summary is machine-generated.

FastEpistasis significantly speeds up the analysis of genetic interactions for complex diseases by efficiently testing all single nucleotide polymorphism (SNP) pairs across multiple phenotypes. This computational tool reduces analysis time, enabling large-scale genetic studies.

More Related Videos

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

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

Published on: June 21, 2018

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Related Experiment Videos

Last Updated: Jan 9, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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

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

Published on: June 21, 2018

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Area of Science:

Genetics
Computational Biology
Bioinformatics

Background:

Genome-wide association studies (GWAS) are crucial for understanding complex diseases.
Analyzing genetic interactions (epistasis) is computationally intensive.
Gene expression quantitative trait mapping involves numerous simultaneous tests.

Purpose of the Study:

To develop an efficient parallel computational tool for epistasis analysis.
To address the computational challenges in large-scale genetic association studies.

Main Methods:

Developed FastEpistasis, a parallel software solution.
Extended the PLINK epistasis module for continuous phenotypes.
Implemented an algorithm that scales with processor count.

Main Results:

FastEpistasis efficiently tests epistasis effects for continuous phenotypes.
The tool demonstrates significant computation time reduction when analyzing multiple phenotypes.
Capable of analyzing 125 billion SNP pair tests in large populations within days.

Conclusions:

FastEpistasis offers a scalable and efficient solution for epistasis analysis.
The software accelerates genetic studies by reducing computational burden.
Enables comprehensive analysis of genetic interactions in large cohorts.