title: Pathway-based approach using hierarchical components of collapsed rare variants.
abstract: 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.
  
  
  
  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.
  
  
  
  An implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/
  
  
  
  tspark@stats.snu.ac.kr
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.
authors: Sungyoung
authors: S
authors: Lee
authors: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.
authors: Sungkyoung
authors: S
authors: Choi
authors: Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea.
authors: Young Jin
authors: YJ
authors: Kim
authors: Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-Do 363-951, Korea.
authors: Bong-Jo
authors: BJ
authors: Kim
authors: Department of Psychology, McGill University, Montreal, QC H3A 1B1, Canada.
authors: Heungsun
authors: H
authors: Hwang
authors: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea Department of Statistics, Seoul National University, Seoul 151-747, Korea.
authors: Taesung
authors: T
authors: Park
mesh_terms: D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D016208:Databases, Factual; D059472:Exome; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008099:Liver / Q000201:enzymology; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype