The success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. To limit circularity and bias, VARITY excludes features informed by variant annotation and protein identity. To provide a rationale for each prediction, we quantified the contribution of features and feature combinations to the pathogenicity inference of each variant. VARITY outperformed all previous computational methods evaluated, identifying at least 10% more pathogenic variants at thresholds achieving high (90% precision) stringency.

个性化基因组医学的成功取决于我们评估罕见人类变异的致病性的能力，包括重要的错义变异类别。在训练准确的计算系统方面存在许多挑战，例如，在用作训练示例的变体集中找到数量、质量和偏差之间的平衡，以及避免可能突出偏差影响的预测特征。在这里，我们描述了 VARITY，它明智地利用了更大的训练示例库，但具有不确定的准确性和代表性。为了限制循环性和偏差，VARITY 排除了由变异注释和蛋白质身份告知的特征。为了为每个预测提供一个基本原理，我们量化了特征和特征组合对每个变体的致病性推断的贡献。