Pathway-based approach using hierarchical components of collapsed rare variants
- Authors
- Lee, Sungyoung; Choi, Sungkyoung; Kim, Young Jin; Kim, Bong-Jo; Hwang, Heungsun; Park, Taesung
- Issue Date
- Sep-2016
- Publisher
- Oxford University Press
- Citation
- Bioinformatics, v.32, no.17, pp.586 - 594
- Indexed
- SCIE
SCOPUS
- Journal Title
- Bioinformatics
- Volume
- 32
- Number
- 17
- Start Page
- 586
- End Page
- 594
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/13055
- DOI
- 10.1093/bioinformatics/btw425
- ISSN
- 1367-4803
- Abstract
- 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.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY > ERICA 수리데이터사이언스학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/13055)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.