Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis
Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis
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DOI：
10.1038/s41467-019-10649-4
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发表时间：
2019-06-28
影响因子：
16.6
通讯作者：
Franke, Lude
中科院分区：
文献类型：
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作者：
Deelen, Patrick;van Dam, Sipko;Franke, Lude
外显子组和基因组测序的诊断率仍然很低（8-70%），这是由于对导致疾病的基因的不完全了解。为了改善这一点，我们使用来自31，499个样本的RNA-seq数据来预测哪些基因会导致特定的疾病表型，并开发了基因网络辅助诊断优化（GADO）。我们表明，这种无偏的方法，它不依赖于特定的知识，对个别基因，是有效的，在确定以前未知的疾病基因协会，并标记基因，以前被错误地牵连在疾病。GADO可以在www.genenetwork.nl上运行，提供HPO术语和包含候选变体的基因列表。最后，将GADO应用于61名患者的队列，其中外显子组测序分析没有导致遗传诊断，产生了10个病例的可能致病基因。
The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.