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Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame

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dc.contributor.authorSabag, Idanen
dc.contributor.authorBi, Yeen
dc.contributor.authorPeleg, Zvien
dc.contributor.authorMorota, Gotaen
dc.date.accessioned2023-10-11T13:20:26Zen
dc.date.available2023-10-11T13:20:26Zen
dc.date.issued2023-03en
dc.description.abstractIntroduction: Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted. Methods: In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses. Results: In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%-58% compared to the single-environment model, particularly when borrowing information from other environments was made possible. Discussion: Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.en
dc.description.notesThis research was supported by a Research Grant from BARD, the United States-Israel Binational Agricultural Research and Development Fund (No. IS-5400-21), the Hebrew University of Jerusalem, and Virginia Polytechnic Institute and State University. IS is indebted to the Samuel and Lottie Rudin scholarship foundation.en
dc.description.sponsorshipBARD; United States-Israel Binational Agricultural Research and Development Fund [IS-5400-21]; Hebrew University of Jerusalem; Virginia Polytechnic Institute and State University; Samuel and Lottie Rudin scholarship foundationen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fgene.2023.1108416en
dc.identifier.eissn1664-8021en
dc.identifier.other1108416en
dc.identifier.pmid36992702en
dc.identifier.urihttp://hdl.handle.net/10919/116447en
dc.identifier.volume14en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgenomic predictionen
dc.subjectMediterranean climateen
dc.subjectmulti-environmenten
dc.subjectoilseed cropen
dc.subjectsesameen
dc.titleMulti-environment analysis enhances genomic prediction accuracy of agronomic traits in sesameen
dc.title.serialFrontiers in Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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