Increasing the Yield and Utility of Pediatric Genomic Medicine with Exomiser

利用 Exomiser 提高儿科基因组医学的产量和实用性

• 批准号: 10611970
• 负责人: CHRISTOPHER J MUNGALL
• 金额: $ 70.29万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-10 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611970
• 关键词: Address; Affect; Algorithms; Animal Model; Area; Birth; Case Study; Child; Child Care; Childhood; Clinic; Clinical; Clinical Data; Clinical Management; Clinical Research; Clinical assessments; Cohort Analysis; Collection; Complex; Computational algorithm; Computer Analysis; Computer software; Couples; DNA; DNA sequencing; Data; Databases; Diagnosis; Diagnostic; Disease; England; Evaluation; Finding by Cause; Frustration; Gene Expression; Gene Mutation; Genes; Genetic; Genetic Diseases; Genetic Materials; Genetically Modified Animals; Genome; Genomic medicine; Genomics; Health; Human; Human Genetics; Individual; Intellectual functioning disability; Intelligence; Knowledge; Laboratories; Literature; Medical; Medicine; Mendelian disorder; Methods; Modeling; Molecular; Morbidity - disease rate; Ontology; Pathogenesis; Pathogenicity; Pathway interactions; Patients; Pediatrics; Performance; Phenotype; Population; Predictive Value; Process; Publishing; RNA; RNA Splicing; RNA analysis; Rare Diseases; Records; Research; Resources; Role; Running; Scanning; Series; Specialist; Spliced Genes; Symptoms; Techniques; Technology; Testing; Time; Tissue-Specific Gene Expression; United States National Institutes of Health; Variant; Visit; causal variant; clinical care; clinical decision-making; clinical diagnostics; clinical phenotype; cohort; cost; diagnostic tool; diagnostic value; disease diagnostic; exome; exome sequencing; gene discovery; genetic disorder diagnosis; genome sequencing; genomic data; human disease; improved; meter; new technology; next generation sequencing; novel; novel strategies; pediatrician; personalized management; phenomics; rare genetic disorder; support tools; tool; transcriptome sequencing; uptake; whole genome

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.

项目摘要 多达10％的人口患有罕见疾病（RD）；这些疾病中有80％是由 基因突变和高达75％的出生时出现或从童年开始。遗传疾病的诊断通常是 有问题：大约25％的RD患者必须等待5至30年的诊断，约有一半 最初的诊断是错误的。对于许多受影响的儿童，只有在持久之后才进行确定的诊断 以及令人沮丧的访问不同专家的奥德赛。新兴的遗传测序技术提供 缩短这一漫长而昂贵的诊断途径的可能性。确定基因变化的方法 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 称为下一代测序（NGS），最初被用来识别A的遗传原因 2010年的疾病现在正成为诊所的常规。 NGS诊断的能力具有更多 自2010年以来，对于怀疑遗传疾病的儿童而言，它比2010年增加了一倍。 NGS数据的诊断分析 涉及对DNA变化的数万（外显型组）甚至数百万（基因组）的评估 （变体），它需要复杂的计算机算法，这些算法可以筛选这些/此数据以找到 原因。我们的小组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的世界，允许算法 将患者的症状与7,000多个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的患者的临床表型和 基因修饰的动物模型，并将其与DNA的引起疾病潜力的分析结束 变体大大减少了搜索空间以识别因果变体。 exomiser有效地处理 外显子和基因组数据。在此提案中，我们计划扩展exomiser以利用新的基因组数据类型 包括长阅读基因组测序和基于NGS的RNA数据分析，这将改善 结构变体（SV）的致病性预测以及影响基因表达或剪接的变体。我们 还将通过表征临床表型网络和 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集 在100,000个基因组项目等项目中未解决的案例的（同类）。我们的算法方法将 随着新信息添加到医疗 文学。最后，我们将开发工具，通过添加 全球基因组和健康联盟（GA4GH）产生的标准的支持。提议 进步将使exomiser效率更高，更准确和更容易使用，而非专业的儿科医生可以使用， 将基因组诊断物带到常规的小儿临床护理中。

项目成果

Phenotype-aware prioritisation of rare Mendelian disease variants.

• DOI: 10.1016/j.tig.2022.07.002
• 发表时间: 2022-12
• 期刊: TRENDS IN GENETICS
• 影响因子: 11.4
• 作者: Kelly, Catherine;Szabo, Anita;Pontikos, Nikolas;Arno, Gavin;Robinson, Peter N.;Jacobsen, Jules O. B.;Smedley, Damian;Cipriani, Valentina
• 通讯作者: Cipriani, Valentina

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.

• DOI: 10.1093/bib/bbac188
• 发表时间: 2022-09-20
• 期刊: Briefings in bioinformatics
• 影响因子: 9.5

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

• DOI: 10.1002/humu.24353
• 发表时间: 2022-06
• 期刊: HUMAN MUTATION
• 影响因子: 3.9
• 作者: Laurie, Steven;Piscia, Davide;Matalonga, Leslie;Corvo, Alberto;Fernandez-Callejo, Marcos;Garcia-Linares, Carles;Hernandez-Ferrer, Carles;Luengo, Cristina;Martinez, Ines;Papakonstantinou, Anastasios;Pico-Amador, Daniel;Protasio, Joan;Thompson, Rachel;Tonda, Raul;Bayes, Monica;Bullich, Gemma;Camps-Puchadas, Jordi;Paramonov, Ida;Trotta, Jean-Remi;Alonso, Angel;Attimonelli, Marcella;Beroud, Christophe;Bros-Facer, Virginie;Buske, Orion J.;Canada-Pallares, Andres;Fernandez, Jose M.;Hansson, Mats G.;Horvath, Rita;Jacobsen, Julius O. B.;Kaliyaperumal, Rajaram;Lair-Preterre, Severine;Licata, Luana;Lopes, Pedro;Lopez-Martin, Estrella;Mascalzoni, Deborah;Monaco, Lucia;Perez-Jurado, Luis A.;Posada de la Paz, Manuel;Rambla, Jordi;Rath, Ana;Riess, Olaf;Robinson, Peter N.;Salgado, David;Smedley, Damian;Spalding, Dylan;'t Hoen, Peter A. C.;Topf, Ana;Zaharieva, Irina;Graessner, Holm;Gut, Ivo G.;Lochmuller, Hanns;Beltran, Sergi
• 通讯作者: Beltran, Sergi

The GA4GH Phenopacket schema defines a computable representation of clinical data.

• DOI: 10.1038/s41587-022-01357-4
• 发表时间: 2022-06
• 期刊: NATURE BIOTECHNOLOGY
• 影响因子: 46.9
• 作者: Jacobsen, Julius O. B.;Baudis, Michael;Baynam, Gareth S.;Beckmann, Jacques S.;Beltran, Sergi;Buske, Orion J.;Callahan, Tiffany J.;Chute, Christopher G.;Courtot, Melanie;Danis, Daniel;Elemento, Olivier;Essenwanger, Andrea;Freimuth, Robert R.;Gargano, Michael A.;Groza, Tudor;Hamosh, Ada;Harris, Nomi L.;Kaliyaperumal, Rajaram;Lloyd, Kevin C. Kent;Khalifa, Aly;Krawitz, Peter M.;Koeler, Sebastian;Laraway, Brian J.;Lehvaslaiho, Heikki;Matalonga, Leslie;McMurry, Julie A.;Metke-Jimenez, Alejandro;Mungall, Christopher J.;Munoz-Torres, Monica C.;Ogishima, Soichi;Papakonstantinou, Anastasios;Piscia, Davide;Pontikos, Nikolas;Queralt-Rosinach, Nuria;Roos, Marco;Sass, Julian;Schofield, Paul N.;Seelow, Dominik;Siapos, Anastasios;Smedley, Damian;Smith, Lindsay D.;Steinhaus, Robin;Sundaramurthi, Jagadish Chandrabose;Swietlik, Emilia M.;Thun, Sylvia;Vasilevsky, Nicole A.;Wagner, Alex H.;Warner, Jeremy L.;Weiland, Claus;Haendel, Melissa A.;Robinson, Peter N.
• 通讯作者: Robinson, Peter N.

The Clinical Variant Analysis Tool: Analyzing the evidence supporting reported genomic variation in clinical practice.

• DOI: 10.1016/j.gim.2022.03.013
• 发表时间: 2022-07
• 期刊: GENETICS IN MEDICINE
• 影响因子: 8.8
• 作者: Chin, Hui-Lin;Gazzaz, Nour;Huynh, Stephanie;Handra, Iulia;Warnock, Lynn;Moller-Hansen, Ashley;Boerkoel, Pierre;Jacobsen, Julius O. B.;du Souich, Christele;Zhang, Nan;Shefchek, Kent;Prentice, Leah M.;Washington, Nicole;Haendel, Melissa;Armstrong, Linlea;Clarke, Lorne;Li, Wenhui Laura;Smedley, Damian;Robinson, Peter N.;Boerkoel, Cornelius F.
• 通讯作者: Boerkoel, Cornelius F.