Access to dense panels of molecular markers has facilitated genomic selection in animal breeding. The purpose of this study was to compare the nonparametric (random forest and support vector machine), semiparametric reproducing kernel Hilbert spaces (RKHS), and parametric methods (ridge regression and Bayes A) in prediction of genomic breeding values for traits with different genetic architecture. The predictive performance of different methods was compared in different combinations of distribution of QTL effects (normal and uniform), two levels of QTL numbers (50 and 200), three levels of heritability (0.1, 0.3 and 0.5), and two levels of training set individuals (1000 and 2000). To do this, a genome containing four chromosomes each 100-cM long was simulated on which 500, 1000 and 2000 evenly spaced single-nucleotide markers were distributed. With an increase in heritability and the number of markers, all the methods showed an increase in prediction accuracy (P<0.05). By increasing the number of QTLs from 50 to 200, we found a significant decrease in the prediction accuracy of breeding value in all methods (P<0.05). Also, with the increase in the number of training set individuals, the prediction accuracy increased significantly in all statistical methods (P<0.05). In all the various simulation scenarios, parametric methods showed higher prediction accuracy than semiparametric and nonparametric methods. This superior mean value of prediction accuracy for parametric methods was not statistically significant compared to the semiparametric method, but it was statistically significant compared to the nonparametric method. Bayes A had the highest accuracy of prediction among all the tested methods and, is therefore, recommended for genomic evaluation.

中文翻译：

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基因组评估中参数，半参数和非参数方法的比较。

获得密集的分子标记物组有助于在动物育种中进行基因组选择。这项研究的目的是比较非参数（随机森林和支持向量机），半参数再现核希尔伯特空间（RKHS）和参数方法（岭回归和贝叶斯A）在预测具有不同遗传结构的性状的基因组育种值时的作用。 。在QTL效果分布的不同组合（正常和均匀），两个级别的QTL数量（50和200），三个级别的遗传力（0.1、0.3和0.5）和两个级别的训练中比较了不同方法的预测性能。设置个人（1000和2000）。为此，模拟了一个包含四个每个100-cM长的染色体的基因组，在该染色体上分布了500、1000和2000个均匀间隔的单核苷酸标记。随着遗传力和标记数的增加，所有方法均显示出预测准确性的提高（P <0.05）。通过将QTL的数量从50个增加到200个，我们发现在所有方法中育种值的预测准确性均显着降低（P <0.05）。而且，随着训练集个体数量的增加，在所有统计方法中，预测准确性均显着提高（P <0.05）。在所有各种模拟方案中，参数方法都比半参数和非参数方法显示出更高的预测精度。与半参数方法相比，该参数方法的预测准确性的优越均值在统计学上不显着，但与非参数方法相比，它在统计学上具有显着意义。