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

Research output: Contribution to journalArticlepeer-review

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-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.

Original languageEnglish (US)
Pages (from-to)4439-4448
Number of pages10
JournalG3: Genes, Genomes, Genetics
Volume10
Issue number12
DOIs
StatePublished - Dec 2020

Keywords

  • Bayesian Regression
  • GWAS
  • GenPred
  • Genomic Prediction
  • Shared data resources
  • Structural Equation Models
  • Variable Selection

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

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