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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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Chen Cao¹, Bowei Ding², Qing Li¹

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

|February 26, 2021

Summary

This summary is machine-generated.

Transcriptome-wide association studies (TWAS) can outperform genome-wide association studies (GWAS) when gene expression is highly heritable. However, TWAS may be less powerful than GWAS if expression heritability is low, suggesting potential misapplication in some studies.

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Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify genetic variants associated with phenotypes.
Transcriptome-wide association studies (TWAS) integrate gene expression data to link variants to phenotypes through mediating gene expression.
The predictive accuracy of genotype-expression models in TWAS is often low, impacting study power.

Purpose of the Study:

To investigate the impact of expression model accuracy on TWAS power.
To compare the statistical power of GWAS, TWAS, and a hypothetical method using real expression data.
To determine the conditions under which TWAS is advantageous over GWAS.

Main Methods:

Derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to calculate statistical power.
Compared power across GWAS, TWAS (using predicted expression), and a hypothetical protocol (using real expression).
Examined scenarios of causality and pleiotropy, and varied expression heritability.

Main Results:

Under pleiotropy, TWAS with predicted expression outperformed TWAS with actual expression, validating TWAS utility even with imperfect models.
GWAS surpassed TWAS when expression heritability fell below 0.04 (causality) or 0.06 (pleiotropy).
Evidence suggests TWAS may have been inappropriately used in studies with low expression heritability.

Conclusions:

TWAS remains a valuable tool, particularly under pleiotropy, even with low predictive accuracy for gene expression.
The choice between GWAS and TWAS depends critically on expression heritability and genetic architecture (causality vs. pleiotropy).
Careful consideration of expression heritability is crucial to avoid misapplication of TWAS in genetic association studies.