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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...

General Transcription Factors

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...

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Updated: Nov 24, 2025

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

Published on: September 23, 2014

Multi-tissue transcriptome-wide association studies.

Nastasiya F Grinberg¹, Chris Wallace^1,2

¹Department of Medicine, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK.

Genetic Epidemiology

|December 28, 2020

Summary

This summary is machine-generated.

Multi-tissue transcriptome-wide association studies (TWAS) improve gene discovery by leveraging cross-tissue correlations. Random forests multi-task learning (RF-MTL) outperformed other methods in identifying disease-associated genes, particularly for type 1 diabetes.

Keywords:

complex traits gene expression multi-task learning transcriptome-wide association studies

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Transcriptome-wide association studies (TWAS) integrate gene expression data with genome-wide association studies (GWAS) to identify disease-associated genes.
Leveraging shared regulatory processes across tissues can enhance the accuracy of gene expression prediction in TWAS.

Purpose of the Study:

To evaluate multi-tissue extensions of regression and random forest (RF) models for improving TWAS accuracy.
To compare the performance of single-tissue versus multi-tissue approaches in identifying disease-associated genes.

Main Methods:

Investigated multi-tissue extensions of lasso regression and random forests (RF), specifically joint lasso and RF-MTL (multi-task learning RF).
Utilized an expression quantitative trait loci (eQTL) dataset for model training and validation.
Applied the developed methods to a type 1 diabetes GWAS dataset.

Main Results:

Multi-tissue methods, particularly RF-MTL, demonstrated superior prediction accuracy compared to single-tissue methods on the tested eQTL data.
Simulations indicated that multi-tissue approaches identified more associated genes, though joint lasso showed a tendency for false positives across tissues.
Application to type 1 diabetes GWAS revealed that multi-tissue methods identified more unique associated genes across various tissues.

Conclusions:

Multi-tissue TWAS methods are competitive and often superior to single-tissue approaches, especially for specific cell types.
RF-MTL shows significant promise for enhancing gene discovery in TWAS by effectively utilizing cross-tissue expression correlations.
These findings support the broader adoption of multi-tissue strategies in genetic association studies.