Gene panel and exome sequencing have revealed a high rate of molecular diagnoses among diseases where the genetic architecture has proven suitable for sequencing approaches, with a large number of distinct and highly penetrant causal variants identified among a growing list of disease genes. The challenge is, given the DNA sequence of a new patient, to distinguish disease-causing from benign variants. Large samples of human standing variation data highlight regional variation in the tolerance to missense variation within the protein-coding sequence of genes. This information is not well captured by existing bioinformatic tools, but is effective in improving variant interpretation. To address this limitation in existing tools, we introduce the missense tolerance ratio (MTR), which summarizes available human standing variation data within genes to encapsulate population level genetic variation. We find that patient-ascertained pathogenic variants preferentially cluster in low MTR regions (P < 0.005) of well-informed genes. By evaluating 20 publicly available predictive tools across genes linked to epilepsy, we also highlight the importance of understanding the empirical null distribution of existing prediction tools, as these vary across genes. Subsequently integrating the MTR with the empirically selected bioinformatic tools in a gene-specific approach demonstrates a clear improvement in the ability to predict pathogenic missense variants from background missense variation in disease genes. Among an independent test sample of case and control missense variants, case variants (0.83 median score) consistently achieve higher pathogenicity prediction probabilities than control variants (0.02 median score; Mann-Whitney U test, P < 1 × 10⁻¹⁶). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy.

基因组和外显子组测序显示出疾病之间的分子诊断率很高，在这些疾病中，遗传结构已被证明适用于测序方法，并且在越来越多的疾病基因列表中发现了大量独特且高渗透性的因果变体。面临的挑战是，鉴于新患者的DNA序列，如何将引起疾病的原因与良性变异区分开。人类站立变异数据的大量样本突显了基因的蛋白质编码序列中对错义变异的耐受性的区域变异。现有的生物信息学工具无法很好地捕获此信息，但可以有效地改善变体解释。为了解决现有工具中的这一限制，我们引入了错义容忍率（MTR），其中总结了基因中可用的人类站立变异数据，以封装种群水平的遗传变异。我们发现，由患者确定的病原体变异优先聚集在MTR较低的区域（P <0.005）的消息灵通的基因。通过评估与癫痫相关的基因的20种公开可用的预测工具，我们还强调了理解现有预测工具的经验空分布的重要性，因为这些预测工具在各个基因之间会有所不同。随后将MTR与经验选择的生物信息学工具整合到基因特异性方法中，表明从疾病基因的背景错义变异预测致病性错义变异的能力有了明显的提高。在病例和对照错义变体的独立测试样本中，病例变体（中位数为0.83）始终比对照变体（中位数为0.02； Mann-Whitney U检验，P <1×10 ^-16）具有更高的致病性预测概率。）。我们专注于癫痫基因的应用。但是，该框架适用于癫痫以外的疾病基因。