BACKGROUND Estimating the genetic component of a complex phenotype is a complicated problem, mainly because there are many allele effects to estimate from a limited number of phenotypes. In spite of this difficulty, linear methods with variable selection have been able to give good predictions of additive effects of individuals. However, prediction of non-additive genetic effects is challenging with the usual prediction methods. In machine learning, non-additive relations between inputs can be modeled with neural networks. We developed a novel method (NetSparse) that uses Bayesian neural networks with variable selection for the prediction of genotypic values of individuals, including non-additive genetic effects. RESULTS We simulated several populations with different phenotypic models and compared NetSparse to genomic best linear unbiased prediction (GBLUP), BayesB, their dominance variants, and an additive by additive method. We found that when the number of QTL was relatively small (10 or 100), NetSparse had 2 to 28 percentage points higher accuracy than the reference methods. For scenarios that included dominance or epistatic effects, NetSparse had 0.0 to 3.9 percentage points higher accuracy for predicting phenotypes than the reference methods, except in scenarios with extreme overdominance, for which reference methods that explicitly model dominance had 6 percentage points higher accuracy than NetSparse. CONCLUSIONS Bayesian neural networks with variable selection are promising for prediction of the genetic component of complex traits in animal breeding, and their performance is robust across different genetic models. However, their large computational costs can hinder their use in practice.

中文翻译：

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具有用于预测基因型值的变量选择的贝叶斯神经网络。


背景技术估计复杂表型的遗传成分是一个复杂的问题，主要是因为需要从有限数量的表型中估计许多等位基因效应。尽管存在这一困难，具有变量选择的线性方法已经能够对个体的累加效应给出良好的预测。然而，使用通常的预测方法来预测非加性遗传效应具有挑战性。在机器学习中，输入之间的非加性关系可以使用神经网络进行建模。我们开发了一种新颖的方法（NetSparse），该方法使用带有变量选择的贝叶斯神经网络来预测个体的基因型值，包括非加性遗传效应。结果我们模拟了具有不同表型模型的多个群体，并将 NetSparse 与基因组最佳线性无偏预测 (GBLUP)、BayesB、它们的显性变体以及加法相加进行了比较。我们发现，当 QTL 数量相对较少（10 或 100）时，NetSparse 的准确度比参考方法高 2 至 28 个百分点。对于包含显性或上位效应的场景，NetSparse 的表型预测准确率比参考方法高 0.0 到 3.9 个百分点，但在极端过度显性的场景中除外，在这种情况下，显式模拟显性的参考方法的准确度比 NetSparse 高 6 个百分点。结论 具有变量选择的贝叶斯神经网络有望用于预测动物育种中复杂性状的遗传成分，并且其在不同遗传模型中的表现都很稳健。然而，它们巨大的计算成本可能会阻碍它们在实践中的使用。