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A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies.
Content Provider | Europe PMC |
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Author | Wang, Zigui Chapman, Deborah Morota, Gota Cheng, Hao |
Copyright Year | 2020 |
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. |
Page Count | 10 |
Volume Number | 10 |
PubMed Central reference number | PMC7718731 |
Issue Number | 12 |
PubMed reference number | 33020191 |
Journal | G3 (Bethesda) |
e-ISSN | 21601836 |
DOI | 10.1534/g3.120.401618 |
Publisher | Genetics Society of America |
Publisher Date | 2020-12-03 |
Access Restriction | Open |
Rights License | This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2020 Wang et al. |
Subject Keyword | Structural Equation Models Bayesian Regression Variable Selection GWAS Genomic Prediction GenPred Shared data resources |
Content Type | Text |
Resource Type | Article |
Subject | Genetics Molecular Biology Genetics (clinical) |