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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: Jan 11, 2026

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

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TWAS atlas 2.0: an updated data resource for transcriptome-wide association studies.

Hao Gao^1,2,3, Congfan Bu^1,2, Si Zheng^4,5

¹National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.

Nucleic Acids Research

|November 18, 2025

Summary

This summary is machine-generated.

TWAS Atlas 2.0 enhances gene-trait association discovery by integrating more data and analysis tools. This resource aids researchers in understanding gene expression

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Last Updated: Jan 11, 2026

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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Published on: July 1, 2020

Area of Science:

Genetics
Bioinformatics
Systems Biology

Background:

Transcriptome-wide association studies (TWAS) integrate GWAS and eQTL data to identify genes linked to traits.
TWAS Atlas is a curated resource for analyzing gene-trait associations.
Previous versions facilitated gene-trait association analysis.

Purpose of the Study:

To update and expand the TWAS Atlas resource with the latest TWAS findings and analysis capabilities.
To enhance the integration and analysis of gene-trait associations for complex traits.
To provide researchers with advanced tools for exploring genetic regulation of traits.

Main Methods:

Curated and integrated the latest TWAS results from literature.
Incorporated GWAS datasets for expanded TWAS analysis across diverse phenotypes.
Enhanced the knowledge graph through literature information extraction.
Developed multidimensional interactive analysis modules (functional enrichment, Mendelian randomization, colocalization, fine-mapping).

Main Results:

TWAS Atlas 2.0 now includes 676,198 gene-trait associations, covering 590 traits and 24,870 genes.
Significantly enhanced knowledge graph with improved usability and analytical power.
New interactive modules enable comprehensive exploration of causal gene-trait relationships.
Expanded phenotype coverage and increased the number of gene-trait associations by over 60%.

Conclusions:

TWAS Atlas 2.0 is a significantly enhanced resource for exploring gene-trait associations.
The updated platform provides powerful tools for understanding the genetic basis of complex traits.
Facilitates deeper insights into gene expression and its regulatory roles in human diseases and traits.