Two novel pathway analysis methods based on a hierarchical model.

Area of Science:

• Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) generate vast amounts of data, necessitating advanced analytical methods.
• Pathway analysis enhances the interpretation of GWAS results by focusing on biological pathways.
• Hierarchical models offer a framework for integrating single nucleotide polymorphism (SNP) and pathway effects, but face computational challenges.

Purpose of the Study:

• To introduce novel, computationally tractable hierarchical models for pathway analysis in GWAS.
• To enable the application of hierarchical models to genome-wide data.
• To improve the identification of biologically relevant pathways associated with complex traits.

Main Methods:

• Developed a hierarchical model where SNP effects are analytically integrated, reducing computational complexity.
• Implemented two distinct methods: one using Bayes factors and another employing machine learning with adaptive lasso.
• These methods allow for computationally efficient model fitting on genome-wide datasets.

Main Results:

• The proposed methods demonstrated superior performance compared to Fisher's method and BGSA in simulation studies.
• Applied to real GWAS data for platelet function and body mass index, the methods replicated known findings.
• Novel pathways potentially involved in these phenotypes were identified.

Conclusions:

• The novel hierarchical models provide a computationally efficient and powerful approach for genome-wide pathway analysis.
• These methods enhance the discovery of genetic associations within biological pathways.
• The approach holds promise for advancing our understanding of the genetic architecture of complex diseases.