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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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Updated: Jul 4, 2025

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

Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.

Patrick Evans¹, Taylor Nagai¹, Anuar Konkashbaev¹

¹Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee.

Current Protocols

|February 5, 2024

Summary

This summary is machine-generated.

Transcriptome-wide association studies (TWAS) link gene expression to traits using genetic data. This method helps identify genes influencing diseases for further research.

Keywords:

PrediXcan complex traits electronic health records joint-tissue imputation (JTI)single-cell transcriptomics transcriptome-wide association studies (TWAS)

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Area of Science:

Genetics
Bioinformatics
Genomics

Background:

Transcriptome-wide association studies (TWAS) bridge the gap between genetic variants and phenotypic traits by examining gene expression.
Understanding gene-trait associations is crucial for dissecting complex diseases and guiding functional genomics research.

Purpose of the Study:

To provide a comprehensive overview of Transcriptome-wide association study (TWAS) methodologies.
To discuss the advantages, limitations, and applications of various TWAS approaches.
To highlight the utility of TWAS in post-Genome-wide association study (GWAS) analysis.

Main Methods:

TWAS utilizes in silico models to predict gene expression based on genetic variants.
These predictive models are then applied to Genome-wide association study (GWAS) data.
The approach facilitates the identification of gene-trait associations with high interpretability.

Main Results:

TWAS enables the identification of genes that mediate the relationship between genetic variation and observed phenotypes.
The methodology offers interpretable gene-trait associations, facilitating downstream functional studies.
TWAS supports the development of genetics-anchored resources for biological research.

Conclusions:

TWAS is a powerful post-GWAS analytical framework for identifying genetically influenced traits.
The approach enhances the interpretability of genetic association findings and supports functional genomics.
TWAS methodologies are valuable tools for advancing our understanding of genetic contributions to human health and disease.