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A Decision Tree--Based Hierarchical Model to Integrate GWAS Summary Statistics and Functional Annotations (309597)

Keywords: GWAS, functional annotation, complex diseases

Genome wide association studies (GWAS) have been successful in finding thousands of disease-associated genetic variants. However, several challenges still remain. First, a complex disease is associated with many SNPs, each with small or moderate effect sizes that are harder to detect with limited sample size (a phenomenon called polygenicity). In addition, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with complex diseases. To address these challenges, we propose a novel hierarchical model that integrates GWAS summary statistics and functional annotation information within a unified framework. Our method improves statistical power to prioritize SNPs associated with disease risk while also facilitating understanding of potential mechanisms linking risk-associated SNPs with the complex disease by identifying key combinations of functional annotations. We evaluate the proposed approach with simulation studies and the application to the GWAS of systemic lupus erythematosus.