Clinically Relevant Genome Variation Database

我们建议创建世界上最重要的与临床护理相关的遗传变异数据库（临床上 相关遗传变异资源或CRVR）。我们将提供透明的数据合成和共识 关于给定遗传变体在一系列遗传病变中的临床效用的意见，包括单个 核苷酸变化、小插入缺失和结构变体。我们将与ClinVar、PharmGKB和 OMIM和借鉴NHGRI的倡议，包括基因组测序和分析和孟德尔 疾病测序中心和临床测序探索性研究中心。我们将 与其他CRVR研究中心和NHGRI资助的计划密切合作，以改善临床数据的存储 laboratories.我们的数据库将通过三个目标建立。目标1将吸引和激励临床 基因组学社区围绕CRVR的努力。我们将与其他CRVR和U41研究人员合作， 这项活动，因为他们将重点关注专业协会，临床检测实验室和 更广泛的临床基因组学社区，以确保创建符合预期社区的CRVR资源 需要，包括疾病特异性和突变类型工作组（DSWG和MTWG）的组装 由临床遗传学家和分子诊断专家组成， 变体分类和专业组织指南的整合。目标2将涉及 通过对现有文献、基因座数据库 和NHGRI倡议。我们将传播关于临床相关遗传变异的共识结果， 这些变体的临床意义，支持证据和共识过程的文件。 信息将使用标准本体和先进的处理方法进行汇总 核心数据库（Core Database，CoreDB）。专家评审的共识是 通过方便用户的门户网站（经遗传咨询工作组审查）传播， 挖掘，并向适当的临床和研究社区提供共识临床指南。结果 将按基因、变异、疾病、途径和文献组织。支持证据也将被策划 随着新信息的积累，资源将不断更新。目标3将 包括部署机器学习算法，用于半自动识别假定 临床相关变异（CRV）。我们将进行临床和流行病学的数据挖掘， 遗传学文献和现有的数据库，以确定推定的临床上重要的变异。这将涉及 从ClinVar、OMIM、CSER和Aim 2中汇总的孟德尔中心挖掘数据。各工作组 在目标1中形成的将建立标准并监督策展人审查变体。我们将开发和优化 疾病和基因特异性机器学习算法，以促进快速分类的变异的基础上， 基因检测服务通过ClinVar提供的数据。我们将整合从第一次世界大战中推断出的种群遗传数据， 至少有25个参考群体来自1000个基因组计划和其他大型努力到我们的机器 学习方法，以推断这里发现的CRV的全球相关性。

We propose to create the world's premier database of genetic variants relevant to clinical care (Clinically Relevant Genetic Variants Resource or CRVR). We will provide transparent data synthesis and consensus opinion on the clinical utility of a given genetic variant across a spectrum of genetic lesions including single nucleotide changes, small indels and structural variants. We will integrate with ClinVar, PharmGKB, and OMIM and draw upon NHGRI initiatives including the Genome Sequencing and Analysis and Mendelian Disorders Sequencing Centers, and the Clinical Sequencing Exploratory Research Centers. We will work closely with other CRVR sites and NHGRI funded initiatives to improve deposition of data from clinical laboratories. Our database will be built through three Aims. Aim 1 will engage and energize the clinical genomics community around CRVR efforts. We will partner with the other CRVR and U41 investigators in this activity as they will focus on engagement of professional societies, clinical testing laboratories, and the broader clinical genomics community to ensure creation of a CRVR resource that meets anticipated community needs including assembly of Disease-Specific and Mutation Type Working Groups (DSWGs and MTWGs) comprised of expert clinical geneticists and molecular diagnosticians to establish metrics for the initial classification of variants and integration of guidelines from professional organizations. Aim 2 will involve creation of a CRVR CoreDB resource through expert review of the existing literature, locus databases, and NHGRI initiatives. We will disseminate consensus findings on clinically relevant genetic variants and the clinical implications of these variants, with supporting evidence and documentation of the consensus process. Information will be aggregated using standard ontologies and advanced methodologies for handling heterogeneous data to create a Core Database (CoreDB). The consensus of expert review will be disseminated through a user-friendly web Portal (vetted by Genetic Counseling WG), web services for data mining, and consensus clinical guidelines to the appropriate clinical and research communities. The results will be organized by gene, variant, disease, pathway, and literature. Supporting evidence will also be curated and disseminated, and the resource will be updated continuously as new information accumulates. Aim 3 will involve deployment of machine-learning algorithms for semi- automatic identification of putative Clinically Relevant Variants (CRVs). We will undertake data mining of the clinical and epidemiological genetics literature and existing databases to identify putative clinically important variants. This will involve mining data from ClinVar, OMIM, CSER, and the Mendelian centers aggregated in Aim 2. The Working Groups formed in Aim 1 will establish criteria and oversee curators vetting variants. We will develop and optimize disease- and gene-specific machine learning algorithms to facilitate rapid classification of variants based on data provided by genetic testing services via ClinVar. We will integrate population-genetic data inferred from at least 25 reference populations from the 1000 Genomes Project and other large endeavors into our machine learning approaches so as to infer the global relevance of CRVs discovered here.