PT  - JOURNAL ARTICLE
AU  - Zigui Wang
AU  - Deborah Chapman
AU  - Gota Morota
AU  - Hao Cheng
TI  - A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies
AID  - 10.1101/847285
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 847285
4099  - http://biorxiv.org/content/early/2019/11/20/847285.short
4100  - http://biorxiv.org/content/early/2019/11/20/847285.full
AB  - Bayesian regression methods that incorporate different mixture priors for marker effects are used in multi-trait genomic prediction. These methods can also be extended to genome-wide association studies (GWAS). In multiple-trait GWAS, incorporating the underlying causal structures among traits is essential for comprehensively understanding the relationship between genotypes and traits of interest. Therefore, we develop a GWAS methodology, SEM-BayesCΠ, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into a multi-trait Bayesian regression method using mixture priors. The performance of SEM-BayesCΠ was demonstrated by comparing its GWAS results with those from multi-trait BayesCΠ. Through the inductive causation (IC) algorithm, three potential causal structures were inferred of 0.9 highest posterior density (HPD) interval. SEM-BayesCΠ provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait BayesCΠ by performing GWAS based on indirect, direct and overall marker effects. The software tool JWAS offers open-source routines to perform these analyses.