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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Prediction of genomic breeding values for reproductive traits in Nellore heifers

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Author(s):
Costa, Raphael Bermal [1, 2] ; Irano, Natalia [1] ; Solar Diaz, Lara Del Pilar [1] ; Takada, Luciana [1] ; Hermisdorff, Isis da Costa [2] ; Carvalheiro, Roberto [1] ; Baldi, Fernando [1] ; de Oliveira, Henrique Nunes [1] ; Tonhati, Humberto [1] ; de Albuquerque, Lucia Galva [1]
Total Authors: 10
Affiliation:
[1] Sao Paulo State Univ, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal - Brazil
[2] Univ Fed Bahia, Ave Adhemar de Barros 500, BR-40170110 Salvador, BA - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Theriogenology; v. 125, p. 12-17, FEB 2019.
Web of Science Citations: 0
Abstract

The objective of this study was to assess the accuracy of genomic predictions for female reproductive traits in Nellore cattle. A total of 1853 genotyped cows and 305,348 SNPs were used for genomic selection analyses. GBLUP, BAYESC pi, and IBLASSO were applied to estimate SNP effects. The pseudo-phenotypes used as dependent variables were: observed phenotype (PHEN), adjusted phenotype (CPHEN), estimated breeding value (EBV), and deregressed estimated breeding value (DEBV). Predictive abilities were assessed by the average correlation between CPHEN and genomic estimated breeding value (GEBV) and by the average correlation between DEBV and GEBV in the validation population. Regression coefficients of pseudo phenotypes on GEBV in the validation population were indicators of prediction bias in GEBV. BAYESC pi showed higher predictive ability to estimate SNP effects and GEBV for all traits. (C) 2018 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle
Grantee:Lucia Galvão de Albuquerque
Support type: Research Projects - Thematic Grants