Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets

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dc.contributor.authorSabadin, Felipeen
dc.contributor.authorDoVale, Julio Cesaren
dc.contributor.authorPlatten, John Damienen
dc.contributor.authorFritsche-Neto, Robertoen
dc.date.accessioned2023-05-03T16:52:29Zen
dc.date.available2023-05-03T16:52:29Zen
dc.date.issued2022-10en
dc.description.abstractLong-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.en
dc.description.notesFunding AGGRi Alliance (Accelerated Genetic Gain in Rice in South Asia and Africa - OPP1194889) - Bill and Melinda Gates Foundation (BMGF).en
dc.description.sponsorshipBill and Melinda Gates Foundation (BMGF); [OPP1194889]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fpls.2022.935885en
dc.identifier.other935885en
dc.identifier.pmid36275547en
dc.identifier.urihttp://hdl.handle.net/10919/114902en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectrecurrent genomic selectionen
dc.subjecttraining set designen
dc.subjectstochastic simulationen
dc.subjectself-pollinated cropsen
dc.subjectGS-based methodsen
dc.titleOptimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training setsen
dc.title.serialFrontiers in Plant Scienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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