Xingjie Shi1,2, Yuling Jiao3, Yi Yang4, Ching-Yu Cheng2, Can Yang5, Xinyi Lin2, Jin Liu2. 1. Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China. 2. Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore. 3. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China. 4. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China. 5. Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong.
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.
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.