Loss-of-function of DDX3X is a leading cause of neurodevelopmental disorders (NDD) in females. DDX3X is also a somatically mutated cancer driver gene proposed to have tumour promoting and suppressing effects. We perform saturation genome editing of DDX3X, testing in vitro the functional impact of 12,776 nucleotide variants. We identify 3432 functionally abnormal variants, in three distinct classes. We train a machine learning classifier to identify functionally abnormal variants of NDD-relevance. This classifier has at least 97% sensitivity and 99% specificity to detect variants pathogenic for NDD, substantially out-performing in silico predictors, and resolving up to 93% of variants of uncertain significance. Moreover, functionally-abnormal variants can account for almost all of the excess nonsynonymous DDX3X somatic mutations seen in DDX3X-driven cancers. Systematic maps of variant effects generated in experimentally tractable cell types have the potential to transform clinical interpretation of both germline and somatic disease-associated variation.

DDX3X功能丧失是女性神经发育障碍 (NDD) 的主要原因。 DDX3X也是一种体细胞突变的癌症驱动基因，被认为具有促进和抑制肿瘤的作用。我们对DDX3X进行饱和基因组编辑，在体外测试 12,776 个核苷酸变体的功能影响。我们鉴定出 3432 个功能异常变异，分为三个不同的类别。我们训练机器学习分类器来识别 NDD 相关性的功能异常变体。该分类器具有至少 97% 的灵敏度和 99% 的特异性来检测 NDD 致病性变异，在计算机预测中表现明显优于，并解决了高达 93% 的不确定意义的变异。此外，功能异常变异几乎可以解释DDX3X驱动的癌症中出现的所有过量非同义DDX3X体细胞突变。在实验上易处理的细胞类型中产生的变异效应的系统图谱有可能改变生殖系和躯体疾病相关变异的临床解释。