Department of Genetics, Yale School of Medicine, New Haven, CT, USA; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.
Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
Cell. 2024 Nov 14;187(23):6707-6724.e22. doi: 10.1016/j.cell.2024.08.047. Epub 2024 Sep 25.
Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods are obstacles for achieving genome-wide resolution of variants in disease-related genes. Our framework, saturation mutagenesis-reinforced functional assays (SMuRF), offers simple and cost-effective saturation mutagenesis paired with streamlined 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. Overall, our approach enables variant-to-function insights for disease genes in a cost-effective manner that can be broadly implemented by standard research laboratories.
致病基因突变的解读仍然是人类遗传学中的一个挑战。目前,深度突变扫描方法的成本和复杂性是实现疾病相关基因中变体的全基因组分辨率的障碍。我们的框架,饱和诱变增强功能分析(SMuRF),提供了简单且具有成本效益的饱和诱变,结合精简的功能分析,以增强对未解决变体的解读。将 SMuRF 应用于神经肌肉疾病基因 FKRP 和 LARGE1,我们为所有可能的编码单核苷酸变体生成了功能评分,这有助于解决临床上报告的意义不明的变体。SMuRF 还可用于预测疾病严重程度、确定关键结构区域,并为开发计算预测器提供训练数据集。总的来说,我们的方法以具有成本效益的方式为疾病基因提供了从变体到功能的见解,这种方法可以被标准研究实验室广泛实施。