LAUSR

Abstract Genome-wide association study (GWAS) has been a standard approach to discover the genetic determinants underlying complex traits. It is a major challenge in GWAS how to improve analysis power, uncover complex genetic correlation, and reveal gene-gene and gene-environment interactions through integrated analysis of multiple genetically related traits. To combat these challenges, we proposed a mixed linear model-based joint association analysis method for multiple traits, which include epistasis and gene-environment interaction in the mapping model and utilize within-trait variance and between-trait covariance simultaneously; A F-statistics based on Wilks statistics is used to test the significance of each SNP and paired interacted SNPs, each genetic effects of QTS are estimated and tested by the MCMC method based on a QTS full model. Simulations showed that the multi-trait GWAS method could provide increased power in detecting pleiotropic loci affecting more than one trait, and can unbiasedly estimate effects of QTS. To demonstrate the performance of the proposed method, we analyzed four blood lipid traits in Multi-Ethnic Study of Atherosclerosis (MESA) Cohort and two yield-related traits in a rice immortalized F2 dataset. A software package was developed for the proposed method.