Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study.

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dc.contributorGABRIELA FRANÇA OLIVEIRA, UFV; ANA CAROLINA CAMPANA NASCIMENTO, UFV; MOYSÉS NASCIMENTO, UFV; ISABELA DE CASTRO SANT'ANNA, IAC; JUAN VICENTE ROMERO, AGROSAVIA; CAMILA FERREIRA AZEVEDO, UFV; LEONARDO LOPES BHERING, UFV; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa.
dc.creatorOLIVEIRA, G. F.
dc.creatorNASCIMENTO, A. C. C.
dc.creatorNASCIMENTO, M.
dc.creatorSANT'ANNA, I. de C.
dc.creatorROMERO, J. V.
dc.creatorAZEVEDO, C. F.
dc.creatorBHERING, L. L.
dc.creatorCAIXETA, E. T.
dc.date2022-01-26T15:00:24Z
dc.date2022-01-26T15:00:24Z
dc.date2022-01-26
dc.date2021
dc.date.accessioned2026-07-01T00:27:04Z
dc.descriptionThis study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.
dc.identifierPlos One, v. 16, n. 1, e0243666, 2021.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1139325
dc.identifierhttps://doi.org/10.1371/journal.pone.0243666
dc.identifier.urihttp://hdl.handle.net/123456789/404352
dc.languageeng
dc.rightsopenAccess
dc.subjectRegressão Linear
dc.subjectSeleção Genótipa
dc.subjectGenomics
dc.subjectPlant selection guides
dc.subjectPlant breeding
dc.titleQuantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study.
dc.typeArtigo de periódico

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