Increasing the Yield and Utility of Pediatric Genomic Medicine with Exomiser

项目总结 多达10%的人患有一种罕见的疾病；其中80%的疾病是由 基因突变和高达75%的基因突变在出生时或从儿童开始就存在。遗传性疾病的诊断通常是 问题：大约25%的RD患者必须等待5到30年才能确诊，大约一半的患者 最初的诊断是错误的。对于许多受影响的儿童来说，只有在旷日持久的 以及令人沮丧的长途跋涉去看不同的专家。新兴的基因测序技术为 缩短这条漫长而昂贵的诊断之路的可能性。基因突变的检测方法 所有基因的序列(外显子组测序)或所有遗传物质的序列(基因组测序) 被称为下一代测序(NGS)，它首先被用来确定一种 2010年的疾病，现在正成为临床上的常规。使用NGS进行诊断的能力更强 自2010年以来，疑似遗传病儿童的发病率增加了一倍以上。NGS数据的诊断分析 涉及对DNA中数万(外显子组)甚至数百万(基因组)变化的评估 (变种)，这需要复杂的计算机算法来筛选这些/此数据以找到 原因。我们小组开发了人类表型本体论(HPO)，这是一种在 人类遗传学和儿科临床数据计算分析的世界，允许算法 将患者的症状与超过7000种遗传病的数据库记录进行匹配。 我们的Exomiser软件比较了患有已知人类疾病的患者和 转基因动物模型，并将其与DNA致病潜力的分析相结合 变种，大大减少了识别因果变种的搜索空间。Exomiser高效地处理这两个 外显子组和基因组数据。在这个提案中，我们计划扩展Exomiser以利用新的基因组数据类型 包括长时间基因组测序和基于NGS的RNA数据分析，这将改善 结构变异体(SVS)和影响基因表达或剪接的变异体的致病性预测。我们 还将通过表征临床表型和网络预测新疾病基因 受影响基因的分子功能(途径)。我们计划使用这些算法来评估收藏 (队列)100,000基因组计划等项目中未解决的案件。我们的算法方法将 应用于定期智能地重新分析未解决的病例，因为新的信息添加到医疗 文学。最后，我们将开发工具来将Exomiser集成到广泛的设置中，方法是添加 支持全球基因组学与健康联盟(GA4GH)制定的标准。建议数 先进的Exomiser将使Exomiser更高效、更准确，非专科儿科医生更容易使用， 将基因组诊断技术引入常规的儿科临床护理。

PROJECT SUMMARY As much as 10% of the population suffers from a rare disease (RD); 80% of these diseases are caused by gene mutations and up to 75% are present at birth or begin in childhood. Diagnosis of genetic diseases is often problematic: roughly 25% of RD patients must wait between 5 and 30 years for a diagnosis, and about half of the initial diagnoses are wrong. For many affected children, definitive diagnosis comes only after a protracted and frustrating odyssey of visits to different specialists. Emerging genetic sequencing techniques offer the possibility of shortening this long and costly path to diagnosis. Methods for determining the changes in gene sequences across all genes (exome sequencing) or all genetic material (genome sequencing), collectively referred to as Next-Generation Sequencing (NGS), and which were first used to identify the genetic cause of a disease in 2010, are now becoming routine in the clinic. The ability to make a diagnosis with NGS has more than doubled since 2010 for children with suspected genetic diseases. The diagnostic analysis of NGS data involves the assessment of tens of thousands (exome) or even millions (genome) of changes in the DNA (variants), which requires sophisticated computer algorithms that can sift through these/this data to find the cause. Our group has developed the Human Phenotype Ontology (HPO), a resource widely used around the world for the computational analysis of clinical data in human genetics and pediatrics, allowing algorithms to match the symptoms of a patient with database records of over 7,000 genetic diseases. Our Exomiser software compares the clinical phenotypes of patients with known human diseases and genetically modified animal models, and couples this with an analysis of the disease-causing potential of DNA variants, greatly reducing the search space to identify the causal variant. Exomiser efficiently processes both exome and genome data. In this proposal, we plan to extend Exomiser to utilize new genomic data types including long-read genome sequencing and NGS-based analysis of RNA data, which will improve pathogenicity prediction for structural variants (SVs) and for variants affecting gene expression or splicing. We will also predict novel disease genes through characterization of networks of clinical phenotypes and the molecular functions (pathways) of affected genes. We plan to use these algorithms to assess collections (cohorts) of unsolved cases in projects such as the 100,000 Genomes Project. Our algorithmic approach will be applied to intelligently reanalyze unsolved cases periodically as new information is added to the medical literature. And finally, we will develop tools to integrate Exomiser into a large range of settings by adding support for standards generated by the Global Alliance for Genomics and Health (GA4GH). The proposed advances will make Exomiser more efficient, more accurate, and easier for non-specialist pediatricians to use, bringing genomic diagnostics to routine pediatric clinical care.