Increasing the Yield and Utility of Pediatric Genomic Medicine with Exomiser

项目摘要 多达10%的人口患有罕见疾病（RD）;这些疾病中的80%是由以下原因引起的： 基因突变和高达75%的存在于出生或开始在童年。遗传病的诊断往往 问题：大约25%的RD患者必须等待5到30年才能得到诊断，大约一半的RD患者需要等待5到30年才能得到诊断。 最初的诊断是错误的对于许多受影响的儿童来说，只有在长期的 和拜访不同专家的艰难旅程。新兴的基因测序技术提供了 缩短这条漫长而昂贵的诊断之路的可能性。测定基因改变的方法 所有基因（外显子组测序）或所有遗传物质（基因组测序）的序列，统称为 被称为下一代测序（NGS），并且其首先用于鉴定遗传原因。 在2010年的疾病，现在已经成为常规的临床。使用NGS进行诊断的能力 自2010年以来，患有疑似遗传疾病的儿童人数增加了一倍多。NGS数据的诊断分析 包括对DNA中数万（外显子组）甚至数百万（基因组）的变化进行评估 （变种），这需要复杂的计算机算法，可以筛选这些/这些数据，以找到 事业我们的小组已经开发了人类表型本体论（HPO），这是一种广泛使用的资源， 人类遗传学和儿科临床数据的计算分析世界，允许算法 将患者的症状与超过7,000种遗传疾病的数据库记录相匹配。 我们的Exomiser软件比较了已知人类疾病患者的临床表型， 转基因动物模型，并将其与DNA致病潜力的分析相结合 变异，大大减少了搜索空间，以确定因果变异。Exomiser有效地处理了 外显子组和基因组数据。在本提案中，我们计划扩展Exomiser以利用新的基因组数据类型 包括长读基因组测序和基于NGS的RNA数据分析，这将提高 结构变异体（SV）和影响基因表达或剪接的变异体的致病性预测。我们 还将通过临床表型网络的特征来预测新的疾病基因， 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品 10万个基因组计划等项目中未解决的病例（队列）。我们的算法方法将 应用于智能重新分析未解决的案件定期新的信息被添加到医疗 文学最后，我们将开发工具，通过添加 支持全球基因组学与健康联盟（GA 4GH）制定的标准。拟议 进步将使Exomiser更有效，更准确，更容易为非专业儿科医生使用， 将基因诊断引入常规儿科临床护理。

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.