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.

中文翻译：

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巴西萨能山羊产奶性状基因组育种价值的预测

该研究的目的是比较 Saanen 巴西山羊产奶量、牛奶成分和体细胞计数的基因组预测能力方法。940 只山羊，使用 Axiom_OviCap（山羊）面板进行基因分型，Affimetrix 定制阵列具有 62,557 个单核苷酸多态性 (SNP)，用于基因组选择分析。为估计 SNP 和直接基因组值 (DGV) 的影响而研究的基因组方法如下：(a) 基因组 BLUP (GBLUP)，(b) 贝叶斯 Cπ 和 (c) 贝叶斯套索 (BLASSO)。估计育种值 (EBV) 和回归估计育种值 (dEBV) 用作基因组预测的响应变量。通过 DGV 和响应变量（EBV 和 dEBV）之间的 Pearson 相关性评估预测能力。获得 DGV 上响应变量的回归系数以验证基因组预测是否有偏差。此外，预测均方误差 (MSE) 被用作验证模型与数据拟合的度量。当 EBV 用作响应变量时，GBLUP、Bayes Cπ 和 BLASSO 的预测精度平均值分别为 0.68、0.68 和 0.67。使用 dEBV，所有模型的平均预测准确度为 0.50。对于所有模型（GBLUP、Bayes Cπ 和 BLASSO），DGV 上的 EBV 回归系数的平均值为 1.08，高于 dEBV 在 DGV 上的回归系数的平均值，后者对于 GBLUP、贝叶斯 Cπ 的值分别为 1.05、1.05 和 1.08和 BLASSO，分别。没有一种方法在预测能力方面脱颖而出；然而，GBLUP 方法最适合估计 DGV，除了呈现最低的计算成本之外，还以更可靠和更少偏差的方式。在本研究的背景下，考虑到基因组预测的准确性，EBV 是首选的响应变量，尽管 dEBV 也呈现出较低的偏差。