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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要多达 10% 的人口患有罕见病 (RD)；这些疾病80%是由高达 75% 的基因突变在出生时就存在或从儿童时期开始。遗传病的诊断常常是问题在于：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，只有经过长期的治疗后才能得到明确的诊断以及拜访不同专家的令人沮丧的冒险之旅。新兴的基因测序技术提供了缩短这一漫长且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）的序列被称为下一代测序（NGS），它首先被用来识别疾病的遗传原因 2010年出现的疾病，现在已成为临床常规。通过 NGS 进行诊断的能力有更多自 2010 年以来，患有疑似遗传病的儿童人数增加了一倍多。 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（变体），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计算分析的世界，允许算法将患者的症状与 7000 多种遗传疾病的数据库记录进行匹配。我们的 Exomiser 软件会比较患有已知人类疾病的患者的临床表型，并转基因动物模型，并将其与 DNA 致病潜力的分析结合起来变体，大大减少了识别因果变体的搜索空间。 Exomiser 可有效处理这两种情况外显子组和基因组数据。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读基因组测序和基于 NGS 的 RNA 数据分析，这将改善结构变异（SV）和影响基因表达或剪接的变异的致病性预测。我们还将通过临床表型和疾病网络的表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馆藏十万基因组计划等项目中未解决的案例（队列）。我们的算法方法将随着新信息添加到医疗中，可用于定期智能地重新分析未解决的案例文学。最后，我们将开发工具，通过添加以下内容将 Exomiser 集成到各种设置中：支持全球基因组学与健康联盟 (GA4GH) 制定的标准。拟议的进步将使 Exomiser 更高效、更准确，并且更容易让非专业儿科医生使用，将基因组诊断引入常规儿科临床护理中。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Interpretable prioritization of splice variants in diagnostic next-generation sequencing.

诊断性下一代测序中剪接变异的可解释优先顺序。

DOI：
发表时间：
2021-11-04
期刊：
影响因子：
9.8
作者：
Danis, Daniel;Jacobsen, Julius O B;Carmody, Leigh C;Gargano, Michael A;McMurry, Julie A;Hegde, Ayushi;Haendel, Melissa A;Valentini, Giorgio;Smedley, Damian;Robinson, Peter N
通讯作者：
Robinson, Peter N

Phenopacket-tools: Building and validating GA4GH Phenopackets.

Phenopacket-tools：构建和验证 GA4GH Phenopackets。

DOI：
发表时间：
2023
期刊：
影响因子：
3.7
作者：
Danis, Daniel;Jacobsen, Julius O B;Wagner, Alex H;Groza, Tudor;Beckwith, Martha A;Rekerle, Lauren;Carmody, Leigh C;Reese, Justin;Hegde, Harshad;Ladewig, Markus S;Seitz, Berthold;Munoz;Harris, Nomi L;Rambla, Jordi;Baudis, Micha
通讯作者：
Baudis, Micha

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

临床变异分析工具：分析支持临床实践中报告的基因组变异的证据。

DOI：
发表时间：
2022-07
期刊：
影响因子：
0
作者：
Chin, Hui;Gazzaz, Nour;Huynh, Stephanie;Handra, Iulia;Warnock, Lynn;Moller;Boerkoel, Pierre;Jacobsen, Julius O B;du Souich, Christèle;Zhang, Nan;Shefchek, Kent;Prentice, Leah M;Washington, Nicole;Haendel, Melissa;Armstrong
通讯作者：
Armstrong

FABIAN-variant: predicting the effects of DNA variants on transcription factor binding.

FABIAN 变体：预测 DNA 变体对转录因子结合的影响。

DOI：
发表时间：
2022-07-05
期刊：
影响因子：
14.9
作者：
Steinhaus, Robin;Robinson, Peter N;Seelow, Dominik
通讯作者：
Seelow, Dominik

De novo TRPM3 missense variant associated with neurodevelopmental delay and manifestations of cerebral palsy.

从头 TRPM3 错义变异与神经发育迟缓和脑瘫表现相关。

DOI：
发表时间：
2023-12
期刊：
影响因子：
1.8
作者：
Sundaramurthi, Jagadish Chandrabose;Bagley, Anita M;Blau, Hannah;Carmody, Leigh;Crandall, Amy;Danis, Daniel;Gargano, Michael A;Gustafson, Anxhela Gjyshi;Raney, Ellen M;Shingle, Mallory;Davids, Jon R;Robinson, Peter N
通讯作者：
Robinson, Peter N