1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea2Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea3Department of Psychology, McGill University, Montreal, QC H3A 1B1, Canada4Department of Statistics, Seoul National University, Seoul 151-747, Korea
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Motivation: To address ‘missing heritability’ issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates; hence our method considers the correlation of pathways without constraint by a multiple testing problem.Results: Through simulation studies, the proposed method was shown to have higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study.Availability and Implementation: An implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/.Contact:email@example.comSupplementary information: Supplementary data are available at Bioinformatics online.