Adaptive gene- and pathway-trait association testing with GWAS summary statistics

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-Wide Association Studies (GWAS) typically use single nucleotide polymorphism (SNP)-based analyses.
• Existing gene- and pathway-based tests often lack adaptiveness or require individual genetic data.
• Large-scale GWAS meta-analyses increasingly rely on summary statistics, necessitating new analytical approaches.

Purpose of the Study:

• To extend adaptive gene- and pathway-level association tests for use with GWAS summary statistics.
• To develop methods applicable without individual-level genotype and phenotype data.
• To provide highly adaptive tests for genetic association analysis.

Main Methods:

• Extension of two adaptive association tests to handle GWAS summary statistics.
• Utilizing the WTCCC GWAS dataset for method evaluation and comparison.
• Application to a meta-analyzed dataset for identifying blood pressure-associated genes and pathways.

Main Results:

• Proposed methods were evaluated and compared against existing approaches using WTCCC GWAS data.
• Demonstrated the utility of the extended methods in identifying genes and pathways linked to blood pressure.
• The methods are implemented in the publicly available R package aSPU.

Conclusions:

• The developed adaptive tests are effective for gene- and pathway-level association analysis using GWAS summary statistics.
• These methods offer a valuable alternative for large-scale genetic studies where individual data is unavailable.
• The R package aSPU facilitates the application of these advanced statistical techniques in genetic research.