Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation
Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation
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DOI：
10.1101/gr.226589.117
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发表时间：
2017-10-01
期刊：
影响因子：
7
通讯作者：
Petrovski, Slave
中科院分区：
文献类型：
--
作者：
Traynelis, Joshua;Silk, Michael;Petrovski, Slave
基因组和外显子组测序已经揭示了在遗传结构已经被证明适合于测序方法的疾病中的高分子诊断率，其中在越来越多的疾病基因列表中鉴定出大量不同的和高度外显的致病变体。挑战在于，根据新患者的DNA序列，区分致病变异和良性变异。大样本的人类站立变异数据突出显示了基因蛋白质编码序列内错义变异耐受性的区域差异。现有的生物信息学工具不能很好地捕获这些信息，但在改进变异解释方面是有效的。为了解决现有工具的这一局限性，我们引入了错义容忍比（MTR），它总结了基因内可用的人类常设变异数据，以封装群体水平的遗传变异。我们发现，患者确定的致病性变异优先聚集在低MTR区域（P < 0.005）的知情基因。通过评估与癫痫相关的基因中的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零分布的重要性，因为这些预测工具在基因之间存在差异。随后将MTR与经验选择的生物信息学工具以基因特异性方法整合，表明从疾病基因中的背景错义变异预测致病性错义变异的能力明显提高。在病例和对照错义变体的独立测试样本中，病例变体（0.83中值得分）始终比对照变体（0.02中值得分; Mann-Whitney U检验，P < 1 x 10（-16））实现更高的致病性预测概率。我们专注于癫痫基因的应用，然而，该框架适用于癫痫以外的疾病基因。
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 x 10(-16)). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy.