Prediction of genomic breeding values of milk traits in Brazilian Saanen goats

Abstract

Summary The study’s objective was to compare the genomic prediction ability methods for the traits milk yield, milk composition and somatic cell count of Saanen Brazilian goats. Nine hundred forty goats, genotyped with an Axiom_OviCap (Caprine) panel, Affimetrix customized array with 62,557 single nucleotide polymorphisms (SNPs), were used for the genomic selection analyses. The genomic methods studied to estimate the effects of SNPs and direct genomic values (DGV) were as follows: (a) genomic BLUP (GBLUP), (b) Bayes Cπ and (c) Bayesian Lasso (BLASSO). Estimated breeding values (EBV) and deregressed estimated breeding values (dEBV) were used as response variables for the genomic predictions. The prediction ability was assessed by Pearson's correlation between DGV and response variables (EBV and dEBV). Regression coefficients of the response variables on the DGV were obtained to verify if the genomic predictions were biased. In addition, the mean square error of prediction (MSE) was used as a measure of verification of model fit to the data. The means of prediction accuracy, when EBV was used as a response variable, were 0.68, 0.68 and 0.67 for GBLUP, Bayes Cπ and BLASSO, respectively. With dEBV, the mean prediction accuracy was 0.50 for all models. The averages of the EBV regression coefficients on DGV were 1.08 for all models (GBLUP, Bayes Cπ and BLASSO), higher than those obtained for the regression coefficient of dEBV on DGV, which presented values of 1.05, 1.05 and 1.08 for GBLUP, Bayes Cπ and BLASSO, respectively. None of the methods stood out in terms of prediction ability; however, the GBLUP method was the most appropriate for estimating the DGV, in a slightly more reliable and less biased way, besides presenting the lowest computational cost. In the context of the present study, EBV was the preferred response variables considering the genomic prediction accuracy despite dEBV also presented lower bias.

Analysis of the genomic landscape of inbreeding in two divergent groups of Spanish Florida goats

“…The lack of options in SNP genotyping and genome‐wide characterization of genomic variability among breeds until very recent times could be among the reasons underlying this situation. This tendency has reversed in the last few years with the report of several genomic studies in goats, including the characterization of population variability (Kim et al, 2019; Oget et al, 2019; Visser et al, 2016), the description of new candidate genes (Guo et al, 2021; Martin et al, 2018; Moaeen‐ud‐Din et al, 2022; Mucha et al, 2018) and quantitative trait loci (QTL) (Wang et al, 2020; Zhu et al, 2019) associated with phenotypic traits, and the integration of the species into breeding programmes (de Sousa et al, 2021).…”