Max Planck Institute of Molecular Cell Biology and Genetics, 01307, Dresden, Germany.
Center for Systems Biology Dresden, 01307, Dresden, Germany.
Nat Commun. 2023 Apr 19;14(1):2230. doi: 10.1038/s41467-023-37661-z.
Despite the increasing use of genomic sequencing in clinical practice, the interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) provide valuable evidence in variant assessment, but they are prone to misclassifying benign variants, contributing to false positives. Here, we develop Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for missense variants trained using extensive diagnostic data available in 59 actionable disease genes (American College of Medical Genetics and Genomics Secondary Findings v2.0, ACMG SF v2.0). DeMAG improves performance over existing VEPs by reaching balanced specificity (82%) and sensitivity (94%) on clinical data, and includes a novel epistatic feature, the 'partners score', which leverages evolutionary and structural partnerships of residues. The 'partners score' provides a general framework for modeling epistatic interactions, integrating both clinical and functional information. We provide our tool and predictions for all missense variants in 316 clinically actionable disease genes (demag.org) to facilitate the interpretation of variants and improve clinical decision-making.
尽管基因组测序在临床实践中的应用越来越广泛，但即使在研究充分的疾病基因中，稀有遗传变异的解读仍然具有挑战性，导致许多患者的变异具有不确定的意义（VUS）。计算变异效应预测器（VEP）在变异评估中提供了有价值的证据，但它们容易将良性变异错误分类，导致假阳性。在这里，我们开发了用于行动基因中的突变解析（DeMAG），这是一种基于监督学习的错义变异分类器，使用了 59 个可操作疾病基因中的广泛诊断数据进行训练（美国医学遗传学和基因组学学院的次要发现 v2.0，ACMG SF v2.0）。DeMAG 通过在临床数据上达到平衡的特异性（82%）和敏感性（94%），优于现有的 VEP，并且包括一个新的上位特征，即“伙伴得分”，该特征利用了残基的进化和结构伙伴关系。“伙伴得分”为建模上位相互作用提供了一个通用框架，整合了临床和功能信息。我们为 316 个具有临床可操作性的疾病基因中的所有错义变异提供了我们的工具和预测结果（demag.org），以促进变异的解释和改善临床决策。
