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|Research center of Embrapa/Collection:||Embrapa Florestas - Artigo em periódico indexado (ALICE)|
|Type of Material:||Artigo em periódico indexado (ALICE)|
|Authors:||GOIS, I. B.|
RESENDE, M. D. V. de
AZEVEDO, C. F.
NOVELLI, V. M.
MACHADO, M. A.
|Additional Information:||I. B. Gois, Departamento de Fitotecnia, Universidade Federal de Viçosa; A. Borém, Departamento de Fitotecnia, Universidade Federal de Viçosa; M. Cristofani-Yaly, Instituto Agronômico de Campinas, Centro APTA Citros Sylvio Moreira; MARCOS DEON VILELA DE RESENDE, CNPF; C. F. Azevedo, Departamento de Estatística, Universidade Federal de Viçosa; M. Bastianel, Instituto Agronômico de Campinas, Centro APTA Citros Sylvio Moreira; V. M. Novelli, Instituto Agronômico de Campinas, Centro APTA Citros Sylvio Moreira; M. A. Machado, Instituto Agronômico de Campinas, Centro APTA Citros Sylvio Moreira.|
|Title:||Genome wide selection in citrus breeding.|
|Publisher:||Genetics and Molecular Research, v. 15, n. 4, gmr15048863, Oct. 2016.|
|Description:||Genome wide selection (GWS) is essential for the genetic improvement of perennial species such as Citrus because of its ability to increase gain per unit time and to enable the efficient selection of characteristics with low heritability. This study assessed GWS efficiency in a population of Citrus and compared it with selection based on phenotypic data. A total of 180 individual trees from a cross between Pera sweet orange (Citrus sinensis Osbeck) and Murcott tangor (Citrus sinensis Osbeck x Citrus reticulata Blanco) were evaluated for 10 characteristics related to fruit quality. The hybrids were genotyped using 5287 DArT_seqTM (diversity arrays technology) molecular markers and their effects on phenotypes were predicted using the random regression - best linear unbiased predictor (rr-BLUP) method. The predictive ability, prediction bias, and accuracy of GWS were estimated to verify its effectiveness for phenotype prediction. The proportion of genetic variance explained by the markers was also computed. The heritability of the traits, as determined by markers, was 16-28%. The predictive ability of these markers ranged from 0.53 to 0.64, and the regression coefficients between predicted and observed phenotypes were close to unity. Over 35% of the genetic variance was accounted for by the markers. Accuracy estimates with GWS were lower than those obtained by phenotypic analysis; however, GWS was superior in terms of genetic gain per unit time. Thus, GWS may be useful for Citrus breeding as it can predict phenotypes early and accurately, and reduce the length of the selection cycle. This study demonstrates the feasibility of genomic selection in Citrus.|
|Appears in Collections:||Artigo em periódico indexado (CNPF)|