Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data

Area of Science:

• Genetics and Genomics
• Statistical Bioinformatics
• Precision Medicine

Background:

• Genome-wide association studies (GWAS) have identified numerous disease-related genetic variants, but explain only a fraction of trait variation.
• Discovering variants with small effect sizes requires larger sample sizes and more powerful statistical methods.
• Pleiotropy, where variants affect multiple traits, is common and can enhance variant detection if analyzed across traits.

Purpose of the Study:

• To develop powerful statistical methods for genome-wide association tests that leverage pleiotropy across multiple traits.
• To create methods that utilize only publicly available GWAS summary statistics, overcoming data access limitations.
• To improve the detection of novel genetic variants associated with complex traits.

Main Methods:

• Developed a pleiotropy test for identifying variants affecting multiple traits.
• Created a pleiotropy-informed adaptive association test robust across various genetic models.
• Implemented efficient numerical algorithms for analytical P-value computation, avoiding resampling.
• Utilized publicly available GWAS summary statistics for analysis.

Main Results:

• The proposed adaptive test demonstrated robust and powerful performance.
• Application to glycemic traits identified novel genetic loci missed by single-trait GWAS meta-analyses.
• The methods are implemented in a publicly available R package.

Conclusions:

• Pleiotropy-informed adaptive association tests significantly enhance the power to detect novel genetic variants.
• These methods provide a valuable tool for precision medicine by improving the discovery of disease-associated loci.
• The developed statistical framework effectively utilizes existing GWAS summary data for multi-trait association analysis.