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- Title
VIMCO: variational inference for multiple correlated outcomes in genome-wide association studies.
- Authors
Shi, Xingjie; Jiao, Yuling; Yang, Yi; Cheng, Ching-Yu; Yang, Can; Lin, Xinyi; Liu, Jin
- Abstract
Motivation In genome-wide association studies (GWASs) where multiple correlated traits have been measured on participants, a joint analysis strategy, whereby the traits are analyzed jointly, can improve statistical power over a single-trait analysis strategy. There are two questions of interest to be addressed when conducting a joint GWAS analysis with multiple traits. The first question examines whether a genetic loci is significantly associated with any of the traits being tested. The second question focuses on identifying the specific trait(s) that is associated with the genetic loci. Since existing methods primarily focus on the first question, this article seeks to provide a complementary method that addresses the second question. Results We propose a novel method, Variational Inference for Multiple Correlated Outcomes (VIMCO) that focuses on identifying the specific trait that is associated with the genetic loci, when performing a joint GWAS analysis of multiple traits, while accounting for correlation among the multiple traits. We performed extensive numerical studies and also applied VIMCO to analyze two datasets. The numerical studies and real data analysis demonstrate that VIMCO improves statistical power over single-trait analysis strategies when the multiple traits are correlated and has comparable performance when the traits are not correlated. Availability and implementation The VIMCO software can be downloaded from: https://github.com/XingjieShi/VIMCO. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
INTERNET servers; STATISTICAL power analysis; MULTITRAIT multimethod techniques; DATA analysis
- Publication
Bioinformatics, 2019, Vol 35, Issue 19, p3693
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btz167