This study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregnancy (EP) and stayability (STAY). The numbers of genotyped animals and SNP markers available were 2342 and 321 419 (AFC), 4671 and 309 486 (SC), 2681 and 319 619 (STAY) and 3356 and 319 108 (EP). Predictive ability of support vector regression (SVR), Bayesian regularized artificial neural network (BRANN) and random forest (RF) were compared with results obtained using parametric models (genomic best linear unbiased predictor, GBLUP, and Bayesian least absolute shrinkage and selection operator, BLASSO). A 5‐fold cross‐validation strategy was performed and the average prediction accuracy (ACC) and mean squared errors (MSE) were computed. The ACC was defined as the linear correlation between predicted and observed breeding values for categorical traits (EP and STAY) and as the correlation between predicted and observed adjusted phenotypes divided by the square root of the estimated heritability for continuous traits (AFC and SC). The average ACC varied from low to moderate depending on the trait and model under consideration, ranging between 0.56 and 0.63 (AFC), 0.27 and 0.36 (SC), 0.57 and 0.67 (EP), and 0.52 and 0.62 (STAY). SVR provided slightly better accuracies than the parametric models for all traits, increasing the prediction accuracy for AFC to around 6.3 and 4.8% compared with GBLUP and BLASSO respectively. Likewise, there was an increase of 8.3% for SC, 4.5% for EP and 4.8% for STAY, comparing SVR with both GBLUP and BLASSO. In contrast, the RF and BRANN did not present competitive predictive ability compared with the parametric models. The results indicate that SVR is a suitable method for genome‐enabled prediction of reproductive traits in Nellore cattle. Further, the optimal kernel bandwidth parameter in the SVR model was trait‐dependent, thus, a fine‐tuning for this hyper‐parameter in the training phase is crucial.

中文翻译：

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使用参数模型和机器学习方法，通过基因组预测内洛牛的生殖特性

这项研究旨在评估Nellore牛生殖特征的基因组预测的不同机器学习（ML）方法的预测能力。研究的特征是第一次产犊的年龄（AFC），阴囊周长（SC），早孕（EP）和可住宿性（STAY）。可利用的基因型动物和SNP标记的数量为2342和321419（AFC），4671和309486（SC），2681和319619（STAY）以及3356和319108（EP）。将支持向量回归（SVR），贝叶斯正则化人工神经网络（BRANN）和随机森林（RF）的预测能力与使用参数模型（基因组最佳线性无偏预测因子，GBLUP和贝叶斯最小绝对收缩和选择算子， BLASSO）。进行了5倍交叉验证策略，并计算了平均预测准确性（ACC）和均方误差（MSE）。ACC被定义为分类性状（EP和STAY）的预测和观察到的育种值之间的线性相关性，以及预测和观察到的调整表型之间的相关性除以连续性状（AFC和SC）的估计遗传力的平方根。根据所考虑的性状和模型，平均ACC从低到中变化，介于0.56和0.63（AFC），0.27和0.36（SC），0.57和0.67（EP），0.52和0.62（STAY）之间。对于所有性状，SVR提供的精度都比参数模型好一些，与GBLUP和BLASSO相比，AFC的预测准确性分别提高到6.3和4.8％左右。同样 与SVR与GBLUP和BLASSO相比，SC的增长率分别为8.3％，EP的4.5％和STAY的4.8％。相反，与参数模型相比，RF和BRANN没有呈现出竞争性的预测能力。结果表明，SVR是用于基因组预测内洛尔牛繁殖性状的合适方法。此外，SVR模型中的最佳内核带宽参数取决于特征，因此，在训练阶段对该超参数进行微调至关重要。