Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel

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dc.contributor.authorSarinelli, J. Martinen
dc.contributor.authorMurphy, Joseph Paulen
dc.contributor.authorTyagi, Priyankaen
dc.contributor.authorHolland, James B.en
dc.contributor.authorJohnson, Jerry W.en
dc.contributor.authorMergoum, Mohameden
dc.contributor.authorMason, Richard Estenen
dc.contributor.authorBabar, Alien
dc.contributor.authorHarrison, Stephenen
dc.contributor.authorSutton, Russell L.en
dc.contributor.authorGriffey, Carl A.en
dc.contributor.authorBrown-Guedira, Gina L.en
dc.contributor.departmentSchool of Plant and Environmental Sciencesen
dc.date.accessioned2019-08-29T17:12:32Zen
dc.date.available2019-08-29T17:12:32Zen
dc.date.issued2019-04en
dc.description.abstractKey message: The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract: Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.en
dc.description.notesThis project was supported by the Agriculture and Food Research Initiative Competitive Grants 2011-68002-30029 (TCAP) and 2017-67007-25939 (WheatCAP) from the USDA National Institute of Food and Agriculture and a Monsanto Graduate Fellowship supporting JMS.en
dc.description.sponsorshipAgriculture and Food Research Initiative Competitive Grants from the USDA National Institute of Food and Agriculture [2011-68002-30029, 2017-67007-25939]; Monsanto Graduate Fellowshipen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s00122-019-03276-6en
dc.identifier.eissn1432-2242en
dc.identifier.issn0040-5752en
dc.identifier.issue4en
dc.identifier.pmid30680419en
dc.identifier.urihttp://hdl.handle.net/10919/93305en
dc.identifier.volume132en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleTraining population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panelen
dc.title.serialTheoretical and Applied Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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