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dc.creatorSantos, Jhonathan Pedroso Rigal dos-
dc.creatorPires, Luiz Paulo Miranda-
dc.creatorVasconcellos, Renato Coelho de Castro-
dc.creatorPereira, Gabriela Santos-
dc.creatorVon Pinho, Renzo Garcia-
dc.creatorBalestre, Marcio-
dc.date.accessioned2019-02-05T11:27:32Z-
dc.date.available2019-02-05T11:27:32Z-
dc.date.issued2016-
dc.identifier.citationSANTOS, J. P. R. dos et al. Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers. BMC Genetics, [S.l.], v. 17, 2016.pt_BR
dc.identifier.urihttps://link.springer.com/article/10.1186/s12863-016-0392-3pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/32730-
dc.description.abstractBackground: The identification of lines resistant to ear diseases is of great importance in maize breeding because such diseases directly interfere with kernel quality and yield. Among these diseases, ear rot disease is widely relevant due to significant decrease in grain yield. Ear rot may be caused by the fungus Stenocarpella maydi; however, little information about genetic resistance to this pathogen is available in maize, mainly related to candidate genes in genome. In order to exploit this genome information we used 23.154 Dart-seq markers in 238 lines and apply genome-wide selection to select resistance genotypes. We divide the lines into clusters to identify groups related to resistance to Stenocarpella maydi and use Bayesian stochastic search variable approach and rr-BLUP methods to comparate their selection results. Results: Through a principal component analysis (PCA) and hierarchical clustering, it was observed that the three main genetic groups (Stiff Stalk Synthetic, Non-Stiff Stalk Synthetic and Tropical) were clustered in a consistent manner, and information on the resistance sources could be obtained according to the line of origin where populations derived from genetic subgroup Suwan presenting higher levels of resistance. The ridge regression best linear unbiased prediction (rr-BLUP) and Bayesian stochastic search variable (BSSV) models presented equivalent abilities regarding predictive processes. Conclusion: Our work showed that is possible to select maize lines presenting a high resistance to Stenocarpella maydis. This claim is based on the acceptable level of predictive accuracy obtained by Genome-wide Selection (GWS) using different models. Furthermore, the lines related to background Suwan present a higher level of resistance than lines related to other groups.pt_BR
dc.languageen_USpt_BR
dc.publisherSpringerpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceBMC Geneticspt_BR
dc.subjectEar rotpt_BR
dc.subjectGenetic groupspt_BR
dc.subjectRidge regression best linear unbiased predictionpt_BR
dc.subjectBayesian stochastic search variablept_BR
dc.titleGenomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markerspt_BR
dc.typeArtigopt_BR
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