Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/341757
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampSouza, Livia Moura de-
dc.contributor.authorunicampSouza, Anete Pereira de-
dc.typeArtigopt_BR
dc.titleGenomic selection in rubber tree breeding: a comparison of models and methods for managing GxE interactionspt_BR
dc.contributor.authorSouza, Livia M.-
dc.contributor.authorFrancisco, Felipe R.-
dc.contributor.authorGoncalves, Paulo S.-
dc.contributor.authorScaloppi Junior, Erivaldo J.-
dc.contributor.authorLe Guen, Vincent-
dc.contributor.authorFritsche-Neto, Roberto-
dc.contributor.authorSouza, Anete P.-
dc.subjectSeringueirapt_BR
dc.subjectGenotipagempt_BR
dc.subject.otherlanguageRubber plantspt_BR
dc.subject.otherlanguageGenotypingpt_BR
dc.description.abstractSeveral genomic prediction models combining genotype x environment (GxE) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. GxE interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment GxE genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance GxE deviation model (MDs); and 4) a multienvironment, environment-specific variance GxE deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H-2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programspt_BR
dc.relation.ispartofFrontiers in plant sciencept_BR
dc.publisher.cityLausannept_BR
dc.publisher.countrySuiçapt_BR
dc.publisherFrontiers Research Foundationpt_BR
dc.date.issued2019-
dc.date.monthofcirculationOct.pt_BR
dc.language.isoengpt_BR
dc.description.volume10pt_BR
dc.rightsAbertopt_BR
dc.sourceWOSpt_BR
dc.identifier.eissn1664-462Xpt_BR
dc.identifier.doi10.3389/fpls.2019.01353pt_BR
dc.identifier.urlhttps://www.frontiersin.org/articles/10.3389/fpls.2019.01353/fullpt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumber168028/2017-4pt_BR
dc.description.sponsordocumentnumber88887.334728/2019-00pt_BR
dc.description.sponsordocumentnumber18/18985-7pt_BR
dc.date.available2020-05-20T12:03:16Z-
dc.date.accessioned2020-05-20T12:03:16Z-
dc.description.provenanceSubmitted by Cintia Oliveira de Moura (cintiaom@unicamp.br) on 2020-05-20T12:03:16Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:17:21Z : No. of bitstreams: 1 000496429700001.pdf: 1390598 bytes, checksum: df8a0dabf012ccec00a4d44fce92eefc (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-20T12:03:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/341757-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Biologia Vegetalpt_BR
dc.contributor.unidadeInstituto de Biologiapt_BR
dc.contributor.unidadeInstituto de Biologiapt_BR
dc.subject.keywordMultienvironmentpt_BR
dc.subject.keywordSingle nucleotidept_BR
dc.identifier.source000496429700001pt_BR
dc.creator.orcid0000-0002-0202-9531pt_BR
dc.creator.orcid0000-0003-3831-9829pt_BR
dc.type.formArtigo de pesquisapt_BR
dc.identifier.articleid1353pt_BR
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