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

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dc.contributor.authorWang, Ziguien
dc.contributor.authorChapman, Deborahen
dc.contributor.authorMorota, Gotaen
dc.contributor.authorCheng, Haoen
dc.contributor.departmentAnimal and Poultry Sciencesen
dc.date.accessioned2021-08-31T14:38:36Zen
dc.date.available2021-08-31T14:38:36Zen
dc.date.issued2020-12-01en
dc.date.updated2021-08-31T14:38:32Zen
dc.description.abstractBayesian 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.en
dc.description.versionPublished versionen
dc.format.extentPages 4439-4448en
dc.format.extent10 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1534/g3.120.401618en
dc.identifier.eissn2160-1836en
dc.identifier.issn2160-1836en
dc.identifier.issue12en
dc.identifier.orcidMorota, Gota [0000-0002-3567-6911]en
dc.identifier.otherg3.120.401618 (PII)en
dc.identifier.pmid33020191en
dc.identifier.urihttp://hdl.handle.net/10919/104880en
dc.identifier.volume10en
dc.language.isoenen
dc.publisherGenetics Society of Americaen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000599131000013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectLife Sciences & Biomedicineen
dc.subjectGenetics & Heredityen
dc.subjectStructural Equation Modelsen
dc.subjectBayesian Regressionen
dc.subjectVariable Selectionen
dc.subjectGWASen
dc.subjectGenomic Predictionen
dc.subjectGenPreden
dc.subjectShared data resourcesen
dc.subjectINFERENCEen
dc.subjectMODELSen
dc.subject0604 Geneticsen
dc.subject.meshBayes Theoremen
dc.subject.meshGenomicsen
dc.subject.meshGenotypeen
dc.subject.meshPhenotypeen
dc.subject.meshPolymorphism, Single Nucleotideen
dc.subject.meshModels, Geneticen
dc.subject.meshGenome-Wide Association Studyen
dc.titleA Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studiesen
dc.title.serialG3-Genes Genomes Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Animal and Poultry Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen

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