Use este identificador para citar ou linkar para este item:
http://repositorio.ufla.br/jspui/handle/1/40940
Título: | Functional models in genome-wide selection |
Palavras-chave: | Sequencing technologies Prediction of genomic values Genomic analysis Genomic selection Genetic markers Tecnologias de sequenciamento Previsão Bayesiana de valores genéticos Análise genômica Seleção genômica Marcadores genéticos |
Data do documento: | Out-2019 |
Editor: | National Center for Biotechnology Information |
Citação: | MOURA, E. G.; PAMPLONA, A. K. A.; BALESTRE, M. Functional models in genome-wide selection. Plos One, San Francisco, v. 14, n. 10, Oct. 2019. Paginação irregular. |
Resumo: | The 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. |
URI: | http://repositorio.ufla.br/jspui/handle/1/40940 |
Aparece nas coleções: | DEX - Artigos publicados em periódicos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
---|---|---|---|---|
ARTIGO_Functional models in genome-wide selection.pdf | 3,52 MB | Adobe PDF | Visualizar/Abrir |
Este item está licenciada sob uma Licença Creative Commons