Large-scale discovery of novel neurodevelopmental disorder-related genes through a unified analysis of single-nucleotide and copy number variants.
Large-scale discovery of novel neurodevelopmental disorder-related genes through a unified analysis of single-nucleotide and copy number variants.
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DOI：
10.1186/s13073-022-01042-w
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发表时间：
2022-04-26
期刊：
影响因子：
12.3
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中科院分区：
文献类型：
--
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先前对从头变异的大规模研究发现了许多与神经发育障碍（NDD）相关的基因；然而，人们还预测许多 NDD 相关基因有待发现。此类基因可以通过整合拷贝数变异（CNV）和增加样本量来发现，而这些变异在之前的研究中尚未得到充分考虑。我们首先构建了一个模型，根据基因长度和外显子数量等几个因素来估计每个基因的从头 CNV 率。其次，我们通过汇总我们自己的和公开的数据集（包括 denovo-db 和破译发育障碍研究数据），编制了 41,165 名 NDD 个体的从头单核苷酸变异 (SNV) 和 3675 名 NDD 个体的从头 CNV 的综合列表。第三，总结我们估计的从头 CNV 率和之前确定的 SNV 率，在 41,165 个病例中评估了从头有害 SNV 和 CNV 的基因富集。使用考虑功能特征（例如基因本体和表达模式）的深度学习模型，根据显着富集的基因与已知 NDD 基因的相似性，进一步对它们进行优先级排序。我们总共鉴定了 380 个具有统计显着性的基因（5% 错误发现率），其中包括 31 个受 de novo CNV 影响的基因。在这 380 个基因中，有 52 个以前没有被报道为 NDD 基因，从头 CNV 的数据促成了三个基因（GLTSCR1、MARK2 和 UBR3）的重要性。在 52 个基因中，考虑到有害变异的限制，我们合理地排除了 18 个基因（这个数字几乎与理论上预期的假阳性数（即 380 × 0.05 = 19）相同），并提取了 34 个“合理的”候选基因。它们作为 NDD 基因的有效性得到了它们与已知 NDD 基因的功能和基因表达模式的相似性的一致支持。使用深度学习量化整体相似性，我们确定了 11 个高置信度（> 90% 真阳性概率）候选基因：HDAC2、SUPT16H、HECTD4、CHD5、XPO1、GSK3B、NLGN2、ADGRB1、CTR9、BRD3 和 MARK2。我们鉴定了数十个 NDD 基因的新候选基因。这里开发的方法和资源都将有助于进一步鉴定新的 NDD 相关基因。在线版本包含可在 10.1186/s13073-022-01042-w 获取的补充材料。
Previous large-scale studies of de novo variants identified a number of genes associated with neurodevelopmental disorders (NDDs); however, it was also predicted that many NDD-associated genes await discovery. Such genes can be discovered by integrating copy number variants (CNVs), which have not been fully considered in previous studies, and increasing the sample size. We first constructed a model estimating the rates of de novo CNVs per gene from several factors such as gene length and number of exons. Second, we compiled a comprehensive list of de novo single-nucleotide variants (SNVs) in 41,165 individuals and de novo CNVs in 3675 individuals with NDDs by aggregating our own and publicly available datasets, including denovo-db and the Deciphering Developmental Disorders study data. Third, summing up the de novo CNV rates that we estimated and SNV rates previously established, gene-based enrichment of de novo deleterious SNVs and CNVs were assessed in the 41,165 cases. Significantly enriched genes were further prioritized according to their similarity to known NDD genes using a deep learning model that considers functional characteristics (e.g., gene ontology and expression patterns). We identified a total of 380 genes achieving statistical significance (5% false discovery rate), including 31 genes affected by de novo CNVs. Of the 380 genes, 52 have not previously been reported as NDD genes, and the data of de novo CNVs contributed to the significance of three genes (GLTSCR1, MARK2, and UBR3). Among the 52 genes, we reasonably excluded 18 genes [a number almost identical to the theoretically expected false positives (i.e., 380 × 0.05 = 19)] given their constraints against deleterious variants and extracted 34 “plausible” candidate genes. Their validity as NDD genes was consistently supported by their similarity in function and gene expression patterns to known NDD genes. Quantifying the overall similarity using deep learning, we identified 11 high-confidence (> 90% true-positive probabilities) candidate genes: HDAC2, SUPT16H, HECTD4, CHD5, XPO1, GSK3B, NLGN2, ADGRB1, CTR9, BRD3, and MARK2. We identified dozens of new candidates for NDD genes. Both the methods and the resources developed here will contribute to the further identification of novel NDD-associated genes. The online version contains supplementary material available at 10.1186/s13073-022-01042-w.
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