Abstract
While genome-wide association studies (GWASs) have been widely used to uncover associations between diseases and genetic variants, standard SNP-level GWASs often lack the power to identify SNPs that individually have a moderate effect size but jointly contribute to the disease. To overcome this problem, pathway-based GWASs methods have been developed as an alternative strategy that complements SNP-level approaches. We propose a Bayesian method that uses the generalized fused hierarchical structured variable selection prior to identify pathways associated with the disease using SNP-level summary statistics. Our prior has the flexibility to take in pathway structural information so that it can model the gene-level correlation based on prior biological knowledge, an important feature that makes it appealing compared to existing pathway-based methods. Using simulations, we show that our method outperforms competing methods in various scenarios, particularly when we have pathway structural information that involves complex gene-gene interactions. We apply our method to the Wellcome Trust Case Control Consortium Crohn's disease GWAS data, demonstrating its practical application to real data.
Original language | English (US) |
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Pages (from-to) | 724-739 |
Number of pages | 16 |
Journal | Statistics in Medicine |
Volume | 39 |
Issue number | 6 |
DOIs | |
State | Published - Mar 15 2020 |
Bibliographical note
Publisher Copyright:© 2019 John Wiley & Sons, Ltd.
Keywords
- generalized fused lasso
- group lasso
- hierarchical variable selection
- pathway-based GWAS
- summary statistics