Pathogenic Variant Discovery Across a Broad Spectrum of Human Diseases

跨多种人类疾病的致病变异发现

• 批准号: 9376872
• 负责人: FENG CHEN
• 金额: $ 55.48万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-04 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9376872
• 关键词: Adopted; Advanced Development; Affect; American; Benign; Bioinformatics; Biological Assay; Charge; Clinical; Clinical Data; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; DNA; Data; Data Set; Databases; Detection; Diagnosis; Disease; Ethnic Origin; Family Study; Gene Frequency; General Population; Genetic Transcription; Genome; Genomics; Goals; Guidelines; Health; Human; Imagery; In Vitro; Individual; Knowledge; Laboratories; Medical Genetics; Methodology; Molecular Medicine; Mutagenesis; Mutation; Names; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Nature; Ontology; Pathogenicity; Patients; Penetrance; Phenotype; Phosphorylation; Phosphotransferases; Probability; Process; Proteins; RNA; Sequence Analysis; Structure; System; Testing; Trans-Omics for Precision Medicine; Variant; Weight; base; clinical sequencing; clinically relevant; cohort; cost; data sharing; density; experimental study; falls; genetic variant; genomic data; human disease; improved; medical schools; novel; protein function; protein structure; rare variant; software systems; tool; transcription factor; transcriptome sequencing; treatment strategy; variant of unknown significance

Project Summary Falling costs of generating genomic data and computational advances in discerning health-affecting variants therein are bringing personalized molecular medicine closer to reality. Progress has also been made on establishing guidelines (e.g., by the American College of Medical Genetics and Genomics) for the interpretation of sequence variants. However, the crucial step of systematically and accurately interpreting their clinical implications remains an unsolved problem. Specifically, clinical interpretation is technically challenging for several reasons, including: 1) the enormous number of variants in individual genomes, making it difficult to pinpoint causal variants, 2) limited functional/clinical data at the gene and variant levels, 3) discovery of novel clinical variants is a tedious low-throughput process using traditional laboratory and clinical approaches, and 4) conventional bioinformatics tools tend to have insufficient precision based on limitations imposed by linear sequence analysis alone. As a result, clinical genomics is still far too costly for routine clinical use. To meet the urgent need of high precision clinical variant interpretation, our proposal aims to 1) build upon existing clinical knowledge (ClinVar) from ClinGen efforts, 2) utilize rich human variation data in public databases (e.g., ExAC and dbSNP), and 3) leverage existing and upcoming sequencing data from large disease cohorts and small family studies; all to support developing/employing a cross-cutting computational/experimental strategy for clinical variant discovery at a massive scale across a broad spectrum of human diseases. We hypothesize that variants clustering in 3D spatial proximity to known pathogenic variants have high probabilities of affecting protein function. We hypothesize further that many pathogenic variants in databases such as ExAC remain undetected/hidden due to their recessive nature or their rarity that limits statistical power for detection in association analyses. To test these hypotheses and to establish a database for functionally important variants associated with human diseases, we propose to develop a software system called ClinPath3D to detect and characterize clinically relevant pathogenic variants. Essentially, it will utilize protein structures and variant pathogenicity potential to identify 3D spatial pathogenic variant clusters (PVCs) (Aim 1). We will then apply ClinPath3D to interpret rare variants of unknown significance (VUS) from the ExAC, dbSNP, and other variant databases using pathogenic variants obtained from ClinVar as nucleation points for clustering, all with a view toward discerning disease variants in the general population (Aim 2). Finally, we will use large sequencing data sets (CCDG, TopMed, UK100K) to statistically assess variant enrichment in specific disease cohorts and will further improve positive results by experimentally characterizing 50-100 high-priority variants in kinases and 50-100 in transcription factors (Aim 3). Results from these studies will contribute to clinical advancement in two key ways: (1) methodological improvement of identifying pathogenic/functional variants in patient genomes and (2) the building of a comprehensive database of clinically relevant variants across a broad spectrum of disease types.

项目摘要 产生基因组数据和计算进步的成本下降，以辨别影响健康的变体 其中正在使个性化的分子医学更接近现实。进度也已取得 为美国医学遗传与基因组学院建立准则（例如， 序列变体的解释。但是，系统，准确地解释他们的关键步骤 临床意义仍然是一个未解决的问题。具体而言，临床解释在技术上具有挑战性 由于几个原因，包括：1）单个基因组中的大量变体数量，因此很难 精确因果变体，2）在基因和变体水平上有限的功能/临床数据，3）发现新颖 临床变体是使用传统实验室和临床方法的繁琐的低通量过程，4） 基于线性施加的限制，传统的生物信息学工具往往具有不足的精度 仅序列分析。结果，对于常规临床使用而言，临床基因组学仍然太昂贵了。见面 迫切需要高精度的临床变体解释，我们的建议旨在1）建立在现有的临床基础上 Clingen努力的知识（Clinvar），2）在公共数据库中利用丰富的人类变异数据（例如，EXAC 和DBSNP），以及3）利用大型疾病队列和小的现有和即将到来的测序数据 家庭研究；所有这些都支持开发/采用交叉切割计算/实验策略 临床变异发现在广泛的人类疾病中大规模发现。我们假设 这种变体在3D空间邻近与已知致病变异的近端有很高的概率影响 蛋白质功能。我们进一步假设，数据库中的许多致病变异仍然存在 由于其隐性性或稀有性，未被发现/隐藏，限制了检测的统计能力 协会分析。测试这些假设并为功能上重要的变体建立数据库 与人类疾病相关，我们建议开发一个称为Clinpath3D的软件系统以检测和 表征临床上相关的致病变异。本质上，它将利用蛋白质结构和变体 鉴定3D空间致病变体簇（PVC）的致病潜力（AIM 1）。然后我们将申请 Clinpath3d解释来自EXAC，DBSNP和其他变体的稀有意义（VU）的稀有变体 使用从Clinvar获得的病原变体作为聚类的成核点的数据库，所有这些都有视图 朝着一般人群中的辨别疾病变异（AIM 2）。最后，我们将使用大型测序数据 集合（CCDG，Topmed，UK100K）在统计上评估特定疾病同龄人的变异富集，并将 通过实验表征激酶和 转录因子的50-100（目标3）。这些研究的结果将有助于两者的临床进步 关键方法：（1）方法论改善患者基因组中的致病性/功能变异 （2）在广泛的疾病中建立临床相关变体的综合数据库 类型。