Title:	Genome prediction accuracy of common bean via Bayesian models.
Authors:	BARILI, L. D. VALE, N. M. do SILVA, F. R. e CARNEIRO, J. E. de S. OLIVEIRA, H. R. de VIANELLO, R. P. VALDISSER, P. A. M. R. NASCIMENTO, M.
Affiliation:	LEIRI DAIANE BARILI, UFV; NAINE MARTINS DO VALE, COODETEC; FABYANO FONSECA E SILVA, UFV; JOSÉ EUSTAQUIO DE SOUZA CARNEIRO, UFV; HINAYAH ROJAS DE OLIVEIRA, UFV; ROSANA PEREIRA VIANELLO, CNPAF; PAULA ARIELLE M RIBEIRO VALDISSER, CNPAF; MOYSES NASCIMENTO, UFV.
Date Issued:	2018
Citation:	Ciência Rural, v. 48, n. 8, e20170497, 2018.
Description:	We aimed to apply genomic information based on SNP (single nucleotide polymorphism) markers for the genetic evaluation of the traits ?stay-green? (SG), plant architecture (PA), grain aspect (GA) and grain yield (GY) in common bean through Bayesian models. These models were compared in terms of prediction accuracy and ability for heritability estimation for each one of the mentioned traits. A total of 80 cultivars were genotyped for 377 SNP markers, whose effects were estimated by five different Bayesian models: Bayes A (BA), B (BB), C (BC), LASSO (BL) e Ridge regression (BRR). Although, prediction accuracies calculated by means of cross-validation have been similar within each trait, the BB model stood out for the trait SG, whereas the BRR was indicated for the remaining traits. The heritability estimates for the traits SG, PA, GA and GY were 0.61, 0.28, 0.32 and 0.29, respectively. In summary, the Bayesian methods applied here were effective and ease to be implemented. The used SNP markers can help in the early selection of promising genotypes, since incorporating genomic information increase the prediction accuracy of the estimated genetic merit.
Thesagro:	Feijão Phaseolus Vulgaris Marcador Molecular
NAL Thesaurus:	Beans Genetic markers Marker-assisted selection
Keywords:	Validação cruzada Cross-validation
ISSN:	1678-4596
DOI:	10.1590/0103-8478cr20170497
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPAF)