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

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Published on: November 12, 2012

Detecting gene-gene interactions from GWAS using diffusion kernel principal components.

Andrew Walakira¹, Junior Ocira², Diane Duroux²

¹Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia. adwalakira@gmail.com.

BMC Bioinformatics

|February 2, 2022

Summary

This summary is machine-generated.

This study introduces a new computational method for detecting gene interactions (epistasis) in complex diseases like inflammatory bowel disease (IBD). The approach efficiently identifies known and novel IBD-associated genes within complex genetic networks.

Keywords:

Bivariate synergy Diffusion kernel principal components Gene epistasis network Inflammatory bowel disease Spike and slab priors

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Last Updated: Oct 4, 2025

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

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Published on: November 12, 2012

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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Genes function in complex networks, and genetic background (epistasis) influences disease.
Identifying epistasis is challenging due to high dimensionality and data complexities.
Inflammatory Bowel Disease (IBD) is a complex gastrointestinal disease with unknown genetic underpinnings.

Purpose of the Study:

To develop a robust and computationally efficient method for epistasis detection.
To identify gene interactions relevant to complex diseases like IBD.
To construct a gene-based epistasis network for further analysis.

Main Methods:

Dimensionality reduction using diffusion kernel principal components (kpc) per gene.
Utilizing kpc gene summaries for downstream analysis.
Constructing a gene-based epistasis network.

Main Results:

The proposed approach is robust and computationally efficient for epistasis detection.
Successfully recovered known IBD-associated genes.
Identified additional genes of interest linked to IBD.

Conclusions:

The novel method provides an effective way to detect gene interactions in complex diseases.
This approach can aid in understanding the genetic architecture of IBD.
The identified gene networks offer new insights into gastrointestinal disease mechanisms.