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A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer

Xinyuan Dong^1,2, Yu-Ru Su¹, Richard Barfield¹

¹Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

|August 25, 2020

Summary

This summary is machine-generated.

A new method, summary statistics-based mixed effects score test (sMiST), improves the discovery of genetic loci by analyzing predicted gene expression and direct variant effects. This approach enhances genome-wide association studies (GWAS) by utilizing summary statistics for faster, broader genetic discovery.

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Area of Science:

Genetics
Bioinformatics
Statistical genomics

Background:

Genome-wide association studies (GWAS) identify genetic variants but explain limited heritability.
Functional information integration enhances the power to discover novel genetic loci.
Existing methods like S-PrediXcan and TWAS leverage predicted gene expression but may not capture total variant effects.

Purpose of the Study:

To propose a novel method, summary statistics-based mixed effects score test (sMiST), for testing the total effect of genetic variants.
To integrate genetically predicted gene expression and direct variant effects for improved locus discovery.
To enable conditional association analysis and accommodate multiple functional annotations and mediators.

Main Methods:

Developed sMiST, a summary statistics-based mixed effects score test.
Incorporated imputation of genetically predicted gene expression and direct variant effects.
Allowed for multiple functional annotations and genetically predicted mediators.
Enabled conditional association analysis adjusting for known variants.

Main Results:

sMiST demonstrates p-values consistent with individual-level data but with significantly improved computational speed.
The method requires only GWAS summary statistics, facilitating broad application.
Application to colorectal cancer GWAS identified several novel and secondary independent genetic loci.

Conclusions:

sMiST is an efficient and powerful tool for genetic discovery using summary statistics.
The method enhances the identification of novel genetic loci by considering comprehensive variant effects.
sMiST has broad applicability in large-scale genetic association studies.