Ma Kaiyue, Huang Shushu, Ng Kenneth K, Lake Nicole J, Joseph Soumya, Xu Jenny, Lek Angela, Ge Lin, Woodman Keryn G, Koczwara Katherine E, Cohen Justin, Ho Vincent, O'Connor Christine L, Brindley Melinda A, Campbell Kevin P, Lek Monkol
Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
Equal second authors.
bioRxiv. 2024 Jun 25:2023.07.12.548370. doi: 10.1101/2023.07.12.548370.
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 and , 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.
在人类遗传学中,对致病基因变异的解读仍然是一项挑战。深度突变扫描方法目前的成本和复杂性阻碍了通过众包方式对疾病相关基因中的变异进行全基因组分辨率分析。我们的框架,即饱和诱变强化功能测定法(SMuRF),通过提供简单且经济高效的饱和诱变方法以及简化功能测定流程来增强对未解析变异的解读,从而解决了这些问题。将SMuRF应用于神经肌肉疾病基因和,我们为所有可能的编码单核苷酸变异生成了功能评分,这有助于解析临床上报告的意义未明的变异。SMuRF在预测疾病严重程度、解析关键结构区域以及为计算预测器的开发提供训练数据集方面也显示出实用性。我们的方法为以一种对跨标准研究实验室的众包实施广泛有用的方式实现疾病基因从变异到功能的洞察开辟了新方向。