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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: Oct 11, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Disentangling genetic feature selection and aggregation in transcriptome-wide association studies.

Chen Cao¹, Pathum Kossinna¹, Devin Kwok²

¹Department of Biochemistry & Molecular Biology, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.

|December 1, 2021

Summary

Separating feature selection and aggregation in transcriptome-wide association studies (TWAS) improves predictive accuracy. Novel TWAS tools using disentangled protocols outperform standard genetically regulated expression (GReX) methods.

Keywords:

feature selection kernel machine statistical genetics statistical power transcriptome-wide association studies

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Last Updated: Oct 11, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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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

Area of Science:

Genetics
Bioinformatics
Statistical Genetics

Background:

Transcriptome-wide association studies (TWAS) are crucial for linking genetic variation to gene expression and phenotypes.
Improving the predictive accuracy of genetically regulated expression (GReX) is a key research area in TWAS.
Current TWAS methods often combine feature selection and aggregation into a single GReX component.

Purpose of the Study:

To investigate the limitations of the single GReX approach in TWAS.
To develop and evaluate disentangled protocols for feature selection and aggregation in TWAS.
To enhance the power and adaptability of TWAS methodology.

Main Methods:

Simulations were conducted to compare different TWAS protocols.
Real data analysis was performed to validate findings.
Standard TWAS protocols relying on GReX were compared against disentangled approaches using simple marker tests for feature selection and kernel machines for aggregation.

Main Results:

Disentangled protocols for feature selection and aggregation demonstrated superior performance compared to standard TWAS protocols.
The study identified that the single GReX approach limits the adaptability of TWAS methodology.
Novel TWAS tools based on disentangled methods were developed and shown to be more powerful.

Conclusions:

Separating feature selection and aggregation offers a more powerful and adaptable approach to TWAS.
Methodological research should focus on optimal combinations of feature selection and aggregation for increased TWAS power.
The findings provide new tools and insights for advancing TWAS research.