Rackham Owen J L, Shihab Hashem A, Johnson Michael R, Petretto Enrico
Medical Research Council (MRC) Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
The Medical Research Council Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.
Nucleic Acids Res. 2015 Mar 11;43(5):e33. doi: 10.1093/nar/gku1322. Epub 2014 Dec 29.
Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene's intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol's ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.
解读个人基因组序列的方法需求日益增加。在此，我们报告了一种新颖的框架（EvoTol），用于利用蛋白质编码区域内的患者序列数据来识别致病基因。EvoTol利用蛋白质序列的进化保守性来量化基因对突变的不耐受性，并且可以纳入组织特异性基因表达数据。我们将此框架应用于癫痫和先天性心脏病的全外显子组序列数据分析，并证明EvoTol识别已知致病基因的能力是其他竞争方法所无法比拟的。将EvoTol应用于人类相互作用组揭示了富含对蛋白质序列变异不耐受的基因的网络，为人类疾病的新型多基因贡献提供了信息。
