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bioRxiv · 10.1101/847285

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

Wang, Z.·Chapman, D.·Morota, G.·Cheng, H.
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Abstract

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-BayesC{Pi}, 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-BayesC{Pi} was demonstrated by comparing its GWAS results with those from multi-trait BayesC{Pi}. Through the inductive causation (IC) algorithm, three potential causal structures were inferred of 0.9 highest posterior density (HPD) interval. SEM-BayesC{Pi} provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait BayesC{Pi} by performing GWAS based on indirect, direct and overall marker effects. The software tool JWAS offers open-source routines to perform these analyses.

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Wang, Z., Chapman, D., Morota, G., Cheng, H.. 2019-11-20. A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies. https://doi.org/10.1101/847285

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