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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 Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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Published on: January 9, 2020

Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from

Hung Mai^1,2, Jingxuan Bao^1,3, Paul M Thompson⁴

¹Perelman School of Medicine, University of Pennsylvania, B306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, USA.

BMC Bioinformatics

|September 28, 2022

Summary

This study used S-PrediXcan to analyze genetic data and identify genes influencing total brain volume (TBV) and intracranial volume (ICV). The findings reveal novel gene associations and provide insights into the genetic mechanisms of brain structure.

Keywords:

Brain imaging Brain volume Gene expression Genetic variation Imaging genomic association

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Last Updated: Aug 27, 2025

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Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

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Published on: August 26, 2014

Area of Science:

Neuroimaging
Genetics
Transcriptomics

Background:

Brain volume is a heritable trait linked to development, aging, and neurological disorders.
Genome-wide association studies (GWAS) identify genetic variants associated with complex traits like brain volume.
Understanding how genetic variations affect regional gene expression is crucial for explaining phenotypic changes.

Purpose of the Study:

To identify tissue-specific transcriptomic effects on total brain volume (TBV) and intracranial volume (ICV) using S-PrediXcan.
To bridge the gap between genetic variants and phenotypic changes by analyzing gene expression levels.
To leverage GWAS summary data from UK Biobank and ENIGMA for comprehensive analysis.

Main Methods:

S-PrediXcan analysis applied to GWAS summary data.
Analysis of data from UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis (ENIGMA) initiatives.
Identification of tissue-specific transcriptomic associations with TBV and ICV.

Main Results:

Identified 10 genes highly associated with both TBV and ICV.
Confirmed association of 9 out of 10 genes with TBV through independent analysis.
Discovered correlations between identified genes and multiple cognitive/behavioral traits.
Revealed protein-protein interactions, molecular pathways, and biological functions of these genes.

Conclusions:

S-PrediXcan effectively identifies genes with tissue-specific transcriptomic effects on complex traits.
Novel genes related to brain volumetric traits were suggested.
Provided significant insights into the genetic mechanisms underlying human brain structure.