Functional models in genome-wide selection

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorMoura, Ernandes Guedes-
Autor(es): dc.creatorPamplona, Andrezza Kellen Alves-
Autor(es): dc.creatorBalestre, Marcio-
Data de aceite: dc.date.accessioned2026-02-09T11:35:57Z-
Data de disponibilização: dc.date.available2026-02-09T11:35:57Z-
Data de envio: dc.date.issued2020-05-15-
Data de envio: dc.date.issued2020-05-15-
Data de envio: dc.date.issued2019-10-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/40940-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1143218-
Descrição: dc.descriptionThe development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, three scenarios were analyzed: F2 population with 300 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 12,150 SNP markers that were distributed through ten linkage groups; F∞ populations with 320 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 10,020 SNP markers that were distributed through ten linkage groups; and data related to Eucalyptus spp. to measure the model performance in a real LD setting, with 611 individuals whose phenotypes were simulated from QTLs distributed through a panel of 36,812 SNPs with known positions. The performance of the proposed method was compared with those of other genome selection models, namely, RR-BLUP, Bayes B and Bayesian Lasso. The Bayesian functional model presented higher or similar predictive ability when compared with those classical regressions methods in simulated and real scenarios on different LD structures. In general, the Bayesian functional model also achieved higher computational efficiency, using 12 SNPs per MCMC round. The model was efficient in the identification of causal regions and showed high flexibility of analysis, as it is easily adaptable to any genomic selection model.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherNational Center for Biotechnology Information-
Direitos: dc.rightsacesso aberto-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
???dc.source???: dc.sourcePlos One-
Palavras-chave: dc.subjectSequencing technologies-
Palavras-chave: dc.subjectPrediction of genomic values-
Palavras-chave: dc.subjectGenomic analysis-
Palavras-chave: dc.subjectGenomic selection-
Palavras-chave: dc.subjectGenetic markers-
Palavras-chave: dc.subjectTecnologias de sequenciamento-
Palavras-chave: dc.subjectPrevisão Bayesiana de valores genéticos-
Palavras-chave: dc.subjectAnálise genômica-
Palavras-chave: dc.subjectSeleção genômica-
Palavras-chave: dc.subjectMarcadores genéticos-
Título: dc.titleFunctional models in genome-wide selection-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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