Gene- and pathway-based association tests for multiple traits with GWAS summary statistics.

Area of Science:

• Genetics
• Statistical Genomics
• Bioinformatics

Background:

• Single nucleotide polymorphism (SNP)-trait association analysis is common but limited for complex traits.
• Exploring multiple correlated traits at gene or pathway levels can provide deeper biological insights.
• Existing methods may not fully leverage information from multiple traits or varying association patterns.

Purpose of the Study:

• To develop novel adaptive gene-based and pathway-based tests for association analysis of multiple traits using genome-wide association study (GWAS) summary statistics.
• To enhance the identification of genetic variants associated with complex traits and uncover underlying biological mechanisms.
• To provide flexible methods applicable to diverse trait types and GWAS data sources (single or meta-analyzed).

Main Methods:

• Development of adaptive gene-based and pathway-based tests that account for varying association patterns across SNPs and traits.
• The tests are designed to be adaptive at both SNP and trait levels, optimizing power across different scenarios.
• Methods can utilize Z-statistics or P-values from single or meta-analyzed GWAS, accommodating mixed trait types.

Main Results:

• Numerical studies with simulated and real data demonstrated the promising performance of the proposed adaptive methods.
• The adaptive nature of the tests allows for high power across a wide range of association signal sparsity levels.
• The methods are implemented in the R package 'aSPU' for public accessibility.

Conclusions:

• The proposed adaptive gene- and pathway-based tests offer a powerful and flexible approach for multi-trait association analysis in GWAS.
• These methods can improve the discovery of novel genetic variants and biological insights into complex traits.
• The 'aSPU' R package provides a readily available tool for researchers to apply these advanced statistical techniques.