GPA-Tree: statistical approach for functional-annotation-tree-guided prioritization of GWAS results

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 functiona...

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Bibliographic Details
Published in:Bioinformatics Vol. 38; no. 4; pp. 1067 - 1074
Main Authors: Khatiwada, Aastha, Wolf, Bethany J, Yilmaz, Ayse Selen, Ramos, Paula S, Pietrzak, Maciej, Lawson, Andrew, Hunt, Kelly J, Kim, Hang J, Chung, Dongjun
Format: Journal Article
Language:English
Published: England Oxford University Press 27.01.2022
Subjects:
Algorithms
Computer Simulation
Genome-Wide Association Study - methods
Original Papers
Polymorphism, Single Nucleotide
Software
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
Online Access:Get full text
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Summary: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. Availability and implementation The GPATree software is available at https://dongjunchung.github.io/GPATree/. Supplementary information Supplementary data are available at Bioinformatics online.
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btab802