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

Zigui Wang, Deborah Chapman, View ORCID ProfileGota Morota, View ORCID ProfileHao Cheng
doi: https://doi.org/10.1101/847285
Zigui Wang
*Department of Animal Science, University of California, Davis
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Deborah Chapman
*Department of Animal Science, University of California, Davis
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Gota Morota
†Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University
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Hao Cheng
*Department of Animal Science, University of California, Davis
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  • For correspondence: qtlcheng@ucdavis.edu
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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-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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 20, 2019.
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A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies
Zigui Wang, Deborah Chapman, Gota Morota, Hao Cheng
bioRxiv 847285; doi: https://doi.org/10.1101/847285
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A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies
Zigui Wang, Deborah Chapman, Gota Morota, Hao Cheng
bioRxiv 847285; doi: https://doi.org/10.1101/847285

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