Data/hora: 02/06/2024 02:23:47

Provedor de dados: | 65
País: | Brazil
Título: | Combined index of genomic prediction methods applied to productivity
Autores: | Suela,Matheus Massariol; Lima,Leísa Pires; Azevedo,Camila Ferreira; Resende,Marcos Deon Vilela de; Nascimento,Moysés; Silva,Fabyano Fonseca e.
Data: | 2019-01-01
Ano: | 2019
Palavras-chave: | Genomic prediction; Selection index; Genetic gain.
Resumo: | ABSTRACT:  Rice cultivation has great national and global importance, being one of the most produced and consumed cereals in the world and the primary food for more than half of the world’s population. Because of its importance as food, developing efficient methods to select and predict genetically superior individuals in reference to plant traits is of extreme importance for breeding programs. The objective of this research was to evaluate and compare the efficiency of the Delta-p, G-BLUP (Genomic Best Linear Unbiased Predictor), BayesCpi, BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator), Delta-p/G-BLUP index, Delta-p/BayesCpi index, and Delta-p/BLASSO index in the estimation of genomic values and the effects of single nucleotide polymorphisms on phenotypic data associated with rice traits. Use of molecular markers allowed high selective efficiency and increased genetic gain per unit time. The Delta-p method uses the concept of change in allelic frequency caused by selection and the theoretical concept of genetic gain. The Index is based on the principle of combined selection, using the information regarding the additive genomic values predicted via G-BLUP, BayesCpi, BLASSO, or Delta-p. These methods were applied and compared for genomic prediction using nine rice traits: flag leaf length, flag leaf width, panicles number per plant, primary panicle branch number, seed length, seed width, amylose content, protein content, and blast resistance. Delta-p/G-BLUP index had higher predictive abilities for the traits studied, except for amylose content trait in which the method with the highest predictive ability was BayesCpi, being approximately 3% greater than that of the Delta-p/G-BLUP index.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000600404
Editor: | Universidade Federal de Santa Maria
Relação: | 10.1590/0103-8478cr20181008
Formato: | text/html
Fonte: | Ciência Rural  v.49 n.6 2019
Fonte: | info:eu-repo/semantics/openAccess