生物
饱和突变
基因
饱和(图论)
突变
遗传学
计算生物学
突变
突变体
数学
组合数学
作者
Kaiyue Ma,Shushu Huang,Kenneth Ng,Nicole J. Lake,Soumya Joseph,J. Xu,Angela Lek,Lin Ge,Keryn G. Woodman,Katherine Koczwara,Justin Cohen,Vincent Ho,Christine O’Connor,Melinda A. Brindley,Kevin P. Campbell,Monkol Lek
出处
期刊:Cell
[Elsevier]
日期:2024-09-01
标识
DOI:10.1016/j.cell.2024.08.047
摘要
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.
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