A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

G3 (Bethesda). 2020 Dec 3;10(12):4439-4448. doi: 10.1534/g3.120.401618.


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-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.

Keywords: Bayesian Regression; GWAS; GenPred; Genomic Prediction; Shared data resources; Structural Equation Models; Variable Selection.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Bayes Theorem
  • Genome-Wide Association Study*
  • Genomics
  • Genotype
  • Models, Genetic*
  • Phenotype
  • Polymorphism, Single Nucleotide