Prediction of genomic breeding values for reproductive traits in Nellore heifers

被引：11

作者：

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
]}

机构：

[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

来源：

THERIOGENOLOGY | 2019年 / 125卷

基金：

巴西圣保罗研究基金会;

关键词：

Genomic selection; Predicative ability; Reproductive efficiency; SNP; SELECTION; ASSOCIATION; HOLSTEIN; LASSO; FERTILITY; SHRINKAGE; PUBERTY; GROWTH;

D O I：

10.1016/j.theriogenology.2018.10.014

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：12 / 17

页数：6

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