Prediction of genomic breeding values for reproductive traits in Nellore heifers

• Autor(a) principal: Costa, Raphael Bermal [UNESP]
• Data de Publicação: 2019
• Outros Autores: Irano, Natalia [UNESP], Solar Diaz, Lara Del Pilar [UNESP], Takada, Luciana [UNESP], Hermisdorff, Isis da Costa, Carvalheiro, Roberto [UNESP], Baldi, Fernando [UNESP], Oliveira, Henrique Nunes de [UNESP], Tonhati, Humberto [UNESP], Albuquerque, Lucia Galva de [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.theriogenology.2018.10.014; http://hdl.handle.net/11449/186587
• topic: Genomic selection; Predicative ability; Reproductive efficiency; SNP

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.