Wang, ZiguiChapman, DeborahMorota, GotaCheng, Hao2021-08-312021-08-312020-12-012160-1836g3.120.401618 (PII)http://hdl.handle.net/10919/104880Bayesian 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-Bayesian alphabet, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into multi-trait Bayesian regression methods. SEM-Bayesian alphabet provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait GWAS by performing GWAS based on indirect, direct and overall marker effects. The superior performance of SEM-Bayesian alphabet was demonstrated by comparing its GWAS results with other similar multi-trait GWAS methods on real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.Pages 4439-444810 page(s)application/pdfenCreative Commons Attribution 4.0 InternationalLife Sciences & BiomedicineGenetics & HeredityStructural Equation ModelsBayesian RegressionVariable SelectionGWASGenomic PredictionGenPredShared data resourcesINFERENCEMODELS0604 GeneticsBayes TheoremGenomicsGenotypePhenotypePolymorphism, Single NucleotideModels, GeneticGenome-Wide Association StudyArticle - Refereed2021-08-31https://doi.org/10.1534/g3.120.4016181012Morota, Gota [0000-0002-3567-6911]330201912160-1836