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...

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...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

You might also read

Related Articles

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

Sort by

Same author

Parametric hypothesis testing for pathway based hierarchical structural component models.

Genes & genomics·2026

Same author

Analysis of severity in COVID-19 patients by using longitudinal immune profiles.

iScience·2026

Same author

Enhancing polygenic risk prediction by modeling quantile-specific genetic effects.

Scientific reports·2026

Same author

Optoelectronic Synaptic Transistors Based on Colloidal CdSe Nanowires for Energy-Efficient Neuromorphic Computing.

ACS applied materials & interfaces·2026

Same author

NSD Family-Mediated H3K36 Methylation in Human Cancer: Mechanisms and Therapeutic Opportunities.

Biomedicines·2025

Same author

Impact of ACEI/ARB use on COVID-19 mortality in patients with ischaemic heart disease: insights from South Korean National health insurance service data.

BMC infectious diseases·2025

Search research articles

Related Experiment Video

Updated: Jul 5, 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

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm.

Junghyun Namkung¹, Jin-Wu Nam, Taesung Park

¹Bioinformatics Program at College of Natural Science, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul 151-747 Korea. jh.namkung@gmail.com

BMC Proceedings

|May 10, 2008

Summary

This summary is machine-generated.

This study introduces a genetic algorithm (GA) for efficient genome-wide gene x gene interaction analysis. The GA significantly reduces computational time for identifying interacting cancer-related genes and expression quantitative trait loci.

More Related Videos

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

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

Related Experiment Videos

Last Updated: Jul 5, 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

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

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
Computational Biology

Background:

Identifying interacting genes is crucial for understanding complex traits, but challenging due to the vast number of single nucleotide polymorphisms (SNPs).
Existing methods struggle with the computational demands of genome-wide searches for gene x gene interactions.

Purpose of the Study:

To develop and evaluate an efficient algorithm for detecting biologically interacting gene loci.
To apply a genetic algorithm (GA) for genome-wide gene x gene interaction analysis, focusing on cancer-related genes.

Main Methods:

Implemented a genetic algorithm (GA) incorporating heuristic methods (archive, elitism, local search).
Analyzed gene x gene interactions for expression quantitative trait loci (eQTLs) associated with three cancer-related genes.
Compared the computational time complexity of the GA with an exhaustive search algorithm.

Main Results:

The GA significantly reduced computational time compared to exhaustive search methods.
The developed GA provides a practical approach for detecting biologically interacting loci.
The GA successfully identified expression quantitative trait loci associated with cancer-related gene expression levels.

Conclusions:

Genome-wide interaction analysis using GA and a statistical model is a practical method for detecting interacting loci.
The heuristic-enhanced GA offers a substantial improvement in computational efficiency for large-scale genetic analyses.
This approach facilitates the discovery of gene x gene interactions relevant to cancer biology.