Clinically Relevant Genome Variation Database

我们建议创建世界领先的与临床护理(临床)相关的基因变异数据库 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 关于一种特定基因变异在包括单一遗传损害在内的各种遗传损害中的临床实用性的意见 核苷酸变化，小的indels和结构变异。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。我们会工作的 与其他CRVR站点和NHGRI密切合作，以改进临床数据的存储 实验室。我们的数据库将通过三个目标来建立。目标1将参与并激励临床 围绕CRVR努力的基因组学社区。我们将与CRVR和U41的其他调查人员合作 这项活动将侧重于专业协会、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，包括组建特定疾病工作组和突变类型工作组(DSWG和MTWG) 由专业的临床遗传学家和分子诊断学家组成，为最初的 对变体进行分类，并整合专业组织的指南。目标2将涉及 通过对现有文献的专家审查，创建CRVR核心数据库资源， 和NHGRI倡议。我们将传播关于临床相关基因变异的共识结果，以及 这些变异的临床意义，以及共识过程的支持性证据和文件。 信息将使用标准本体和高级处理方法进行汇总 用于创建核心数据库(CoreDB)的异类数据。专家评审的共识将是 通过用户友好的网络门户网站(由遗传咨询工作组审查)传播数据的网络服务 挖掘，并达成共识的临床指南，以适当的临床和研究社区。结果是 将按基因、变种、疾病、途径和文献进行组织。支持证据也将被整理 并加以传播，随着新信息的积累，资源将不断更新。目标3将 涉及部署机器学习算法以半自动识别推定的 临床相关变异(CRV)。我们将进行临床和流行病学的数据挖掘 遗传学文献和现有数据库，以确定推定的临床重要变异。这将涉及到 来自ClinVar、OMIM、CSER和孟德尔中心的挖掘数据聚集在AIM 2中。 在AIM 1中成立的组织将制定标准，并监督馆长对变种进行审查。我们将发展和优化 疾病和基因特定的机器学习算法，以促进基于以下条件的变体快速分类 基因检测服务通过ClinVar提供的数据。我们将整合从At推断的种群遗传数据 1000基因组计划和其他大型项目中的至少25个参考种群进入我们的机器 学习方法，以推断在这里发现的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.