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Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

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dc.contributor.authorBaba, Toshimien
dc.contributor.authorMomen, Mehdien
dc.contributor.authorCampbell, Malachy T.en
dc.contributor.authorWalia, Harkamalen
dc.contributor.authorMorota, Gotaen
dc.date.accessioned2021-10-05T16:44:04Zen
dc.date.available2021-10-05T16:44:04Zen
dc.date.issued2020-02-03en
dc.date.updated2021-10-05T16:44:01Zen
dc.description.abstractRandom regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Weak to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.870.38-0.80). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.en
dc.description.versionPublished versionen
dc.format.extent17 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN e0228118 (Article number)en
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0228118en
dc.identifier.eissn1932-6203en
dc.identifier.issn1932-6203en
dc.identifier.issue2en
dc.identifier.orcidMorota, Gota [0000-0002-3567-6911]en
dc.identifier.otherPONE-D-19-25821 (PII)en
dc.identifier.pmid32012182en
dc.identifier.urihttp://hdl.handle.net/10919/105166en
dc.identifier.volume15en
dc.language.isoenen
dc.publisherPLOSen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000534615800021&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.subjectgenetic architectureen
dc.subjectwater-useen
dc.subjectselectionen
dc.subjectassociationen
dc.subjectefficiencyen
dc.subjectphenomicsen
dc.subject.meshPlant Shootsen
dc.subject.meshWateren
dc.subject.meshRegression Analysisen
dc.subject.meshGenomicsen
dc.subject.meshBiomassen
dc.subject.meshGenotypeen
dc.subject.meshPhenotypeen
dc.subject.meshHigh-Throughput Nucleotide Sequencingen
dc.subject.meshOryzaen
dc.titleMulti-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotypingen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2020-01-07en
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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