PDF(Pubmed)
Abstract：
Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.
摘要：
解释致病遗传变异仍然是人类遗传学和罕见疾病领域的挑战。进行深度突变扫描以绘制变体效应的当前成本和复杂性阻碍了所有疾病相关基因中变体的全基因组分辨率的众包方法。我们的框架,饱和诱变增强功能测定（SMuRF），通过模块化DMS组件来解决这些问题，提供简单且具有成本效益的饱和诱变，以及简化功能测定以增强对未解决变体的解释。将SMuRF应用于神经肌肉疾病基因FKRP和LARGE1，我们已经为超过99.8％的所有可能的编码单核苷酸变体（SNV）产生了功能评分，为营养不良症的临床变异解释提供了额外的证据。从SMuRF生成的数据可实现严重性预测，解析易受错义破坏的关键蛋白质结构区域，并为开发计算预测因子提供训练数据集。总之,我们的方法提供了一个框架,可以通过跨标准研究实验室进行众包实施的方式,实现对疾病基因的变异-功能洞察.
