Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) identify disease-associated genetic variants.
• Increasing availability of GWAS summary statistics enables cross-trait association studies.
• Direct assessment of cross-trait associations is challenging due to complex genetic architectures.

Purpose of the Study:

• Develop a framework for systematic integration of cross-trait associations.
• Utilize GWAS summary statistics for pathway and biological mechanism inference.
• Enhance power for estimating cross-trait associations compared to gene-level analysis.

Main Methods:

• Developed an analytical framework integrating cross-trait association analysis.
• Incorporated two distinct approaches for detecting enriched pathways.
• Required only summary statistics for analysis.

Main Results:

• Framework applied to 25 traits across four phenotype groups.
• Identified an average of 54 significantly associated pathways per trait.
• Pathway-based analysis showed increased power for cross-trait association estimation.
• Detected 24 (53) trait-trait associations at adjusted p_FET < 1x10^-3 (p_FET < 0.01).
• Trait-trait association network revealed intra- and inter-group relationships.

Conclusions:

• Risk variants for 25 traits aggregate in shared biological pathways.
• Confirmed known biological mechanisms and suggested novel insights into multi-trait etiology.
• Demonstrated the utility of pathway-based analysis for understanding complex diseases.