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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...

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Related Experiment Video

Updated: May 7, 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

A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple

Fangyu Li¹, Jinghua Zhao, Zhongshang Yuan

¹Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan 250012, China. xuefzh@sdu.edu.cn.

|September 25, 2013

Summary

We developed a novel mPLSPM statistic to analyze gene-gene interactions in complex diseases. This powerful method effectively detects genetic interactions across multiple quantitative traits, outperforming existing approaches.

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

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Published on: June 21, 2018

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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

Area of Science:

Genetics
Biostatistics
Complex Disease Research

Background:

Analyzing gene-gene interactions is crucial for understanding complex diseases.
Existing methods often focus on single traits or limited SNP interactions.
A more general approach is needed for dissecting genetic mechanisms underlying multiple quantitative traits.

Purpose of the Study:

To develop a novel statistical method for analyzing gene-gene interactions across multiple quantitative traits.
To introduce the mPLSPM statistic, a modification of Partial Least Squares Path Modeling (PLSPM).

Main Methods:

Developed the modified Partial Least Squares Path Modeling (mPLSPM) statistic.
Utilized simulation studies to evaluate the performance of mPLSPM.
Applied mPLSPM to real genetic data from the EPIC-Norfolk GWAS sub-cohort.

Main Results:

mPLSPM demonstrated superior power compared to principal component analysis (PCA) based linear regression.
Identified suggestive gene-gene interactions between TMEM18 and BDNF genes related to body shape scores and BMI.
Found that composite scores (latent traits) are more effective than single traits for capturing obesity-related genetic interactions.

Conclusions:

The novel mPLSPM statistic is a valid and powerful gene-based method.
mPLSPM successfully detects gene-gene interactions on multiple quantitative phenotypes.
This approach enhances the understanding of genetic underpinnings of complex diseases.