UC Davis Genome Center, University of California, Davis, CA, 95616, USA.
MIND Institute, University of California, Davis, 95817, USA.
J Autism Dev Disord. 2023 Mar;53(3):963-976. doi: 10.1007/s10803-022-05586-z. Epub 2022 May 20.
The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study.
神经发育障碍（NDD）的早期检测可以显著改善患者的预后。在 NDD 病例和对照中非同义新生突变的差异负担表明，新生编码变异可用于鉴定可能表现出 NDD 表型的样本子集。因此，我们开发了一种使用新生编码变异来准确预测 NDD 且具有非常低的假阳性率（FPR）的方法，用于一小部分病例。我们使用浅层神经网络，该神经网络集成了新生的可能导致基因失活和错义变异、基因约束的度量以及保守信息，以非常低的 FPR 预测一小部分 NDD 病例，并为未来的临床研究确定 NDD 风险基因。
