Methods for improving clinical diagnostic by detection, prediction, interpretation and prioritization of aberrant transcriptome variations

通过异常转录组变异的检测、预测、解释和优先排序来改进临床诊断的方法

• 批准号: 10033447
• 负责人: Yoseph Barash
• 金额: $ 34.77万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-03 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10033447
• 关键词: Address; Adopted; Age; Algorithms; Alternative Splicing; Benchmarking; Cells; Child; Clinical; Code; Complex; Computers; DNA; Data Analyses; Data Set; Defect; Detection; Development; Diagnosis; Diagnostic; Disease; Event; Excision; Exhibits; Exons; Gender; Genes; Genetic; Genetic Diseases; Genetic Variation; Genomics; Genotype-Tissue Expression Project; Goals; Human; International; Letters; Libraries; Manuals; Medical; Mendelian disorder; Messenger RNA; Methods; Modeling; Molecular Diagnosis; Mutation; Normal tissue morphology; Outcome; Pathogenicity; Patients; Pediatric Hospitals; Performance; Phenotype; Philadelphia; Positioning Attribute; Program Research Project Grants; Protein Isoforms; Proteins; RNA; RNA Processing; RNA Splicing; Regulation; Reverse Transcriptase Polymerase Chain Reaction; Sampling; Site; Technology; Testing; Tissues; Training; United States National Institutes of Health; Untranslated RNA; Variant; Work; causal variant; clinical Diagnosis; clinical application; clinical diagnostics; cohort; computational pipelines; congenital anomaly; deep learning; exome; exome sequencing; follow-up; genetic testing; genetic variant; heterogenous data; improved; interest; novel; prediction algorithm; predictive modeling; programs; standard of care; tool; transcriptome; transcriptome sequencing

The goal of this research program project is to develop methods to improve clinical diagnosis of children with rare Mendelian disorders. Even with the most advanced standard-of-care genetic test of exome sequencing (ES) diagnostic rate is still below 50%. One reason for this rate is that mutations in non-protein coding regions or those that are synonymous (code for the same protein) are generally discarded even though these could be deleterious due to their effect on the processing of RNA transcribed from the underlying gene. We propose 2 complementary methods to help improve clinical diagnosis: The first is “RNA-first”, where our algorithms suggest which clinically accessible tissue (CAT) to use for RNA sequencing, then compare the results to a larger pool of donors to detect which RNA processing variations may be deleterious. The second is a “DNA-first” approach where we develop “RNA splicing code” models that predict the effect of genetic variations on RNA processing in a given tissue of interest. The two approaches, “RNA-first” and “DNA-first”, will be combined into a clinical diagnostic pipeline at the Children Hospital of Philadelphia (CHOP) and applied to solve undiagnosed cases at CHOP and other centers, including the NIH’s Undiagnosed Disease Network (UDN).

本研究项目的目标是开发方法，以改善儿童的临床诊断， 罕见的孟德尔遗传病即使使用最先进的标准护理基因检测外显子组测序（ES） 诊断率仍低于50%。造成这一比率的一个原因是非蛋白质编码区或 那些同义的（编码相同的蛋白质）通常被丢弃， 由于它们对从基础基因转录的RNA的加工的影响，它们是有害的。我们建议2 补充方法，以帮助改善临床诊断：第一个是“RNA优先”，我们的算法建议 用于RNA测序的临床可及组织（CAT），然后将结果与更大的 供者检测哪些RNA加工变异可能是有害的。第二种是“DNA优先”的方法 在那里，我们开发了“RNA剪接密码”模型，预测遗传变异对RNA加工的影响， 给定的感兴趣组织。这两种方法，“RNA优先”和“DNA优先”，将结合成一个临床 诊断管道在费城儿童医院（CHOP），并应用于解决未确诊的病例， CHOP和其他中心，包括NIH的未诊断疾病网络（UDN）。