Gene- and pathway-based association tests for multiple traits with GWAS summary statisticsopen access
- Authors
- Kwak, I.-Y.; Pan, W.
- Issue Date
- 2017
- Publisher
- Oxford University Press
- Citation
- Bioinformatics, v.33, no.1, pp 64 - 71
- Pages
- 8
- Journal Title
- Bioinformatics
- Volume
- 33
- Number
- 1
- Start Page
- 64
- End Page
- 71
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/64066
- DOI
- 10.1093/bioinformatics/btw577
- ISSN
- 1367-4803
1367-4811
- Abstract
- To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. © The Author 2016. Published by Oxford University Press. All rights reserved.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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