Details of the results for Developmental Delay.

The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene drives disease, known as “gene prioritization,” is not entirely understood. Genome-wide searches for gene-disease associations are still underdeveloped due to reliance on previous discoveries and evidence sources with false positive or negative relations. This paper introduces DeepGenePrior, a model based on deep neural networks that prioritizes candidate genes in genetic diseases. Using the well-studied Variational AutoEncoder (VAE), we developed a score to measure the impact of genes on target diseases. Unlike other methods that use prior data to select candidate genes, based on the "guilt by association" principle and auxiliary data sources like protein networks, our study exclusively employs copy number variants (CNVs) for gene prioritization. By analyzing CNVs from 74,811 individuals with autism, schizophrenia, and developmental delay, we identified genes that best distinguish cases from controls. Our findings indicate a 12% increase in fold enrichment in brain-expressed genes compared to previous studies and a 15% increase in genes associated with mouse nervous system phenotypes. Furthermore, we identified common deletions in ZDHHC8, DGCR5, and CATG00000022283 among the top genes related to all three disorders, suggesting a common etiology among these clinically distinct conditions. DeepGenePrior is publicly available online at http://git.dml.ir/z_rahaie/DGP to address obstacles in existing gene prioritization studies identifying candidate genes.脑部疾病的遗传病因学具有高度异质性，其特征为中枢神经系统发育异常，进而导致躯体或智力功能受损。确定驱动疾病的基因的过程，即所谓的"基因优先级排序（gene prioritization）"，目前尚未完全明晰。由于依赖既往研究发现以及存在假阳性/假阴性关联的证据来源，全基因组范围内的基因-疾病关联搜索仍不够完善。本研究提出了基于深度学习神经网络的基因优先级排序模型DeepGenePrior，用于遗传疾病的候选基因优先级排序。借助研究成熟的变分自编码器（Variational AutoEncoder, VAE），我们构建了用于衡量基因对靶疾病影响程度的评分体系。与其他基于"关联即有罪（guilt by association）"原则、借助蛋白质组网络等辅助数据源并利用先验数据筛选候选基因的方法不同，本研究仅采用拷贝数变异（copy number variants, CNVs）开展基因优先级排序。通过分析74811名自闭症、精神分裂症及发育迟缓患者的拷贝数变异数据，我们筛选出了能够最优区分病例组与对照组的基因。研究结果显示，相较于既往研究，本方法在脑部表达基因的富集倍数提升了12%，在与小鼠神经系统表型相关的基因中富集倍数提升了15%。此外，我们在与三种疾病均相关的顶级候选基因中，发现了ZDHHC8、DGCR5及CATG00000022283的共通缺失区域，这表明这些临床表型各异的疾病存在共同的遗传病因。为解决现有候选基因优先级排序研究中的瓶颈问题，DeepGenePrior已在以下网址公开上线：http://git.dml.ir/z_rahaie/DGP。