Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

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dc.contributor.authorGuo, Jiaen
dc.contributor.authorKhan, Jahangiren
dc.contributor.authorPradhan, Sumiten
dc.contributor.authorShahi, Dipendraen
dc.contributor.authorKhan, Naeemen
dc.contributor.authorAvci, Muhsinen
dc.contributor.authorMcBreen, Jordanen
dc.contributor.authorHarrison, Stephenen
dc.contributor.authorBrown-Guedira, Gina L.en
dc.contributor.authorMurphy, Joseph Paulen
dc.contributor.authorJohnson, Jerry W.en
dc.contributor.authorMergoum, Mohameden
dc.contributor.authorMason, Richard Estenen
dc.contributor.authorIbrahim, Amir M. H.en
dc.contributor.authorSutton, Russell L.en
dc.contributor.authorGriffey, Carl A.en
dc.contributor.authorBabar, Md Alien
dc.contributor.departmentSchool of Plant and Environmental Sciencesen
dc.date.accessioned2020-11-12T17:21:39Zen
dc.date.available2020-11-12T17:21:39Zen
dc.date.issued2020-10-28en
dc.date.updated2020-11-12T14:13:44Zen
dc.description.abstractThe performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (<i>Triticum aestivum</i> L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGuo, J.; Khan, J.; Pradhan, S.; Shahi, D.; Khan, N.; Avci, M.; Mcbreen, J.; Harrison, S.; Brown-Guedira, G.; Murphy, J.P.; Johnson, J.; Mergoum, M.; Esten Mason, R.; Ibrahim, A.M.H.; Sutton, R.; Griffey, C.; Babar, M.A. Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes. Genes 2020, 11, 1270.en
dc.identifier.doihttps://doi.org/10.3390/genes11111270en
dc.identifier.urihttp://hdl.handle.net/10919/100841en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgenomic predictionen
dc.subjectmulti-trait modelen
dc.subjectmulti-environment genomic best linear unbiased predictoren
dc.subjectBayesian multi-trait multi-environment modelen
dc.subjectBayesian multi-output regressor stacking modelen
dc.subjectdeep learning multi-trait multi-environment modelen
dc.titleMulti-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimesen
dc.title.serialGenesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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