BACKGROUND Genomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients carrying de novo SVs are frequently unknown. METHODS We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing. RESULTS In 7 of these cases (18%), whole-genome sequencing analysis revealed disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict the effects on genes directly affected by SVs and on genes indirectly affected likely due to the changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In 8 cases, evidence was found for the involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to two cohorts containing a total of 379 patients with previously detected and classified de novo SVs and identified candidate driver genes in 189 cases (50%), including 40 cases whose SVs were previously not classified as pathogenic. Pathogenic position effects were predicted in 28% of all studied cases with balanced SVs and in 11% of the cases with copy number variants. CONCLUSIONS These results demonstrate an integrated computational and experimental approach to predict driver genes based on analyses of WGS data with phenotype association and chromatin organization datasets. These analyses nominate new pathogenic loci and have strong potential to improve the molecular diagnosis of patients with de novo SVs.

中文翻译：

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优先驱动具有从头基因组结构变异的患者的先天表型的基因。

背景技术基因组结构变体（SV）可以影响许多基因和调控元件。因此，驱动携带新SV的患者表型的分子机制通常是未知的。方法我们将系统的实验方法和生物信息学方法相结合，以改善39例具有先天性SV的多发性先天性异常和/或智力残疾的患者的分子诊断，其中大部分患者在常规基因检测后诊断不确定。结果在这些病例中的7例（占18％）中，全基因组测序分析显示，在常规基于微阵列的分析中遗漏了SV的与疾病相关的复杂性。我们开发了一种计算工具来预测对直接受SV影响的基因以及对可能由于染色质组织的变化和对调控机制的影响而间接影响的基因的影响。通过将这些功能预测与广泛的表型信息相结合，可以在16/39（41％）患者中鉴定出候选驱动基因。在8个案例中，发现了多个候选驱动程序参与表型不同部分的证据。随后，我们将这种计算方法应用于两个队列，总共包含379例先前检测到并重新分类过的SV的患者，并在189例病例（50％）中确定了候选驱动基因，其中包括40例先前未归为致病性SV的病例。在所有研究的病例中，有SV平衡的病例中有28％预测了病原体的位置效应，而在拷贝数变异的病例中，有11％的病例预测了病原体的位置效应。结论这些结果证明了基于WGS数据与表型关联和染色质组织数据集的分析基础上的预测驱动基因的综合计算和实验方法。这些分析可提名新的致病基因座，并具有改善从头SV患者分子诊断的强大潜力。