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dc.creatorĐorđević, Vuk
dc.creatorĆeran, Marina
dc.creatorMiladinović, Jegor
dc.creatorBalešević-Tubić, Svetlana
dc.creatorPetrović, Kristina
dc.creatorMiladinov, Zlatica
dc.creatorMarinković, Jelena
dc.date.accessioned2021-04-26T19:47:46Z
dc.date.available2021-04-26T19:47:46Z
dc.date.issued2019
dc.identifier.issn1380-3743
dc.identifier.urihttp://fiver.ifvcns.rs/handle/123456789/1916
dc.description.abstractGenomic selection is a valuable breeding tool that has a great potential for implementation in a real breeding program, as long as prediction model performance is carefully evaluated for each specific scenario. The performance of genomic prediction models has been commonly evaluated by standard cross-validation that can lead to an overestimation of the model performance, by using the same genetic material and their performances that were included in the model development. Besides cross-validation, this study explored the efficiency of yield prediction models for soybean (Glycine max (L.) Merr.) by using historical data for external model validation. Historical data represents a valuable source for evaluation of model performance, simulating the real breeding process. In general, results indicate a modest influence of statistical model and marker number on the prediction ability cross-validation and external validation. In both considerations, non-parametric random forest (RF) model showed an overestimation of genomic estimated breeding values (GEBVs). Overall, genomic prediction ability for soybean yield for historical data across years was relatively high (0.60), implicating that the model has the potential to predict broad adaptation of breeding lines. The model, however, had variable ability to predict phenotypic performance in separate years, with especially high prediction ability in years not impacted by yield-limiting factors, when the genetic potential was fully achieved. General improvement of model performance in both cross-validation and external validation was achieved by increasing the phenotyping intensity that must reflect the target environment variability in terms of different climatological conditions.en
dc.publisherSpringer, Dordrecht
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/31022/RS//
dc.relationDanube region (Deutsche Gesellschaft fur Internationale Zusammenarbeit GmbH)
dc.relationAPV 114-451-2739/2016-01: Održiva proizvodnja biljnih proteina: soja, mikroorganizmi, odgovor na klimatske promene, financed by the Provincial Secretariat for Higher Education and Scientific Research, AP Vojvodina
dc.rightsrestrictedAccess
dc.sourceMolecular Breeding
dc.subjectSoybeanen
dc.subjectGenomic selectionen
dc.subjectPrediction modelsen
dc.subjectHistorical dataen
dc.subjectExternal validationen
dc.titleExploring the performance of genomic prediction models for soybean yield using different validation approachesen
dc.typearticle
dc.rights.licenseARR
dc.citation.issue5
dc.citation.other39(5)
dc.citation.rankM21
dc.citation.volume39
dc.identifier.doi10.1007/s11032-019-0983-6
dc.identifier.scopus2-s2.0-85065741333
dc.identifier.wos000467653900002
dc.type.versionpublishedVersion


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