Using results from genome-wide association studies for understanding complex traits is a current challenge. Here we review how genotype data can be used with different machine learning (ML) methods to predict phenotype occurrence and severity from genotype data. We discuss common feature encoding schemes and how studies handle the often small number of samples compared to the huge number of variants. We compare which ML methods are being applied, including recent results using deep neural networks. Further, we review the application of methods for feature explanation and interpretation.

中文翻译：

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基于基因型数据的基于机器学习的疾病预测


利用全基因组关联研究的结果来理解复杂的性状是当前的挑战。在这里，我们回顾如何将基因型数据与不同的机器学习 (ML) 方法结合使用，以根据基因型数据预测表型的发生和严重程度。我们讨论常见的特征编码方案以及研究如何处理与大量变体相比通常较少的样本。我们比较了正在应用的机器学习方法，包括使用深度神经网络的最新结果。此外，我们回顾了特征解释和解释方法的应用。