VTechWorks staff will be away for the Independence Day holiday from July 4-7. We will respond to email inquiries on Monday, July 8. Thank you for your patience.
 

Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes

Simple item page
dc.contributor.authorYu, Haipengen
dc.contributor.authorCampbell, Malachy T.en
dc.contributor.authorZhang, Qien
dc.contributor.authorWalia, Harkamalen
dc.contributor.authorMorota, Gotaen
dc.date.accessioned2021-10-14T18:40:53Zen
dc.date.available2021-10-14T18:40:53Zen
dc.date.issued2019-06-01en
dc.date.updated2021-10-14T18:40:50Zen
dc.description.abstractWith the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.en
dc.description.versionPublished versionen
dc.format.extentPages 1975-1986en
dc.format.extent12 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1534/g3.119.400154en
dc.identifier.eissn2160-1836en
dc.identifier.issn2160-1836en
dc.identifier.issue6en
dc.identifier.orcidMorota, Gota [0000-0002-3567-6911]en
dc.identifier.otherg3.119.400154 (PII)en
dc.identifier.pmid30992319en
dc.identifier.urihttp://hdl.handle.net/10919/105387en
dc.identifier.volume9en
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:000535489600014&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.subjectBayesian networken
dc.subjectfactor analysisen
dc.subjectmulti-phenotypesen
dc.subjectriceen
dc.subjectQUANTITATIVE TRAIT LOCUSen
dc.subjectLINKAGE DISEQUILIBRIUMen
dc.subjectCOMPLEX TRAITSen
dc.subjectR PACKAGEen
dc.subjectASSOCIATIONen
dc.subjectPANICLEen
dc.subjectMODELSen
dc.subjectGENESen
dc.subjectWHEATen
dc.subjectDISTRIBUTIONSen
dc.subject0604 Geneticsen
dc.subject.meshFactor Analysis, Statisticalen
dc.subject.meshBayes Theoremen
dc.subject.meshGenomicsen
dc.subject.meshPhenotypeen
dc.subject.meshGenome, Planten
dc.subject.meshAlgorithmsen
dc.subject.meshModels, Theoreticalen
dc.subject.meshGenetic Association Studiesen
dc.subject.meshOryzaen
dc.titleGenomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypesen
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

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes.pdf
Size:
1.22 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

All Faculty Deposits
Scholarly Works, School of Animal Sciences