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Genome-wide Association Studies-GWAS

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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.
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Related Experiment Video

Updated: May 26, 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

Finding genome-transcriptome-phenome association with structured association mapping and visualization in GenAMap.

Ross E Curtis¹, Junming Yin, Peter Kinnaird

¹Joint Carnegie Mellon-University of Pittsburgh PhD Program in Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. rcurtis@cs.cmu.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 17, 2011

Summary

This study introduces a new method to link genetic variants, gene expression, and disease traits. The approach enhances the discovery of disease-causing genetic variations and regulatory mechanisms.

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Last Updated: May 26, 2026

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Published on: May 22, 2018

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify disease variants but lack mechanistic insight.
Integrating gene expression (transcriptome) data with genetic (genome) and disease (phenome) data can elucidate disease mechanisms.
Current methods often overlook the complex interplay between genome, transcriptome, and phenome.

Purpose of the Study:

To develop a novel structured association mapping strategy for identifying genome-transcriptome-phenome associations.
To create new visualization tools for exploring complex three-way association results.
To integrate algorithmic approaches and visualizations into a cohesive system for biological discovery.

Main Methods:

A two-step procedure using GFlasso for genome-transcriptome associations and a novel gGFlasso method for transcriptome-phenome associations.
Leveraging inherent structures in genes and phenotypic traits within the gGFlasso method.
Developing and applying advanced visualization techniques within the GenAMap system for structured association mapping.

Main Results:

The GFlasso-gGFlasso approach, integrated with GenAMap, effectively identifies genome-transcriptome-phenome associations.
New visualizations aid in filtering large datasets to pinpoint significant SNPs, genes, and traits.
Simulated data analysis indicates improved sensitivity and specificity compared to existing methods.
Analysis of a mouse dataset revealed biologically relevant SNP-gene-trait associations.

Conclusions:

The proposed structured association mapping strategy significantly advances the understanding of genetic variant mechanisms in disease.
The integration of novel algorithms and visualization tools provides a powerful platform for biological discovery.
This approach holds promise for identifying potential therapeutic targets by uncovering regulatory pathways.