GPA-Tree: Statistical Approach for Functional-Annotation-Tree-Guided Prioritization of GWAS Results

TL;DR

GPA-Tree is a novel statistical method that combines hierarchical modeling and decision trees to improve GWAS analysis, identify key functional annotations, and elucidate genetic mechanisms of complex traits.

Contribution

It introduces GPA-Tree, a new approach that enhances SNP detection and functional annotation interpretation in GWAS through hierarchical modeling and decision trees.

Findings

GPA-Tree outperforms existing methods in simulation studies.

Applied to SLE GWAS, GPA-Tree identified immune cell dysregulation.

Demonstrated ability to uncover genetic architecture of complex traits.

Abstract

Motivation: In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework. Results: First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.