VGER, the Vanderbilt Genome-Electronic Records Project

 描述(由申请人提供)：随着电子病历(EMR)在过去20年中投入实践，越来越多的人认识到，电子病历不仅可以改善对个人的护理服务，还可以了解疾病表现和结果或质量保证等领域的可变性。将密集的基因组信息与Emerge中的EMR相结合，为基因组医学中的发现和初步实施提供了工具，同时也为在医疗保健中使用基因组数据带来了新的挑战和机遇。这些措施包括开发和挖掘必要的大型数据集，以识别具有极端表型或罕见基因类型的患者群体；识别常见疾病的临床相关子集；以及识别可操作的基因组变异，并确定如何最好地将这些变异部署在学习医疗系统中。根据我们在Emerge-I和Emerge-II方面的经验和贡献，我们在此提出应对这些挑战的三个具体目标。在具体目标1中，我们将通过创建越来越细粒度的表型定义来扩大网络的表型库，这些表型定义识别具有可预测的临床过程或治疗反应的特定疾病亚组。基因-表型之间的关系将通过GWA型和我们开发的先进的PheWA型方法进行研究。在具体目标2中，作为Emerge-III 25,000名患者队列的一部分，我们将通过对我们中心2,500名受试者的100个基因进行重新测序，来识别与人类特征有强烈关联的罕见变异。我们建议研究具有已知影响人类健康和药物反应的变异的基因，以及我们初步的Phewas分析暗示的作为重要人类表型的稳健标记的变异。在具体目标3中，我们将扩大我们的先发制人的药物基因组实施计划Prepect，以开发一条管道，向患者和提供者提供可操作的变体，并评估他们的反应。我们将跨Emerge协作开发、实施和评估工具，以提供新的信息，衡量影响，确保患者获得最佳利益。通过执行这些发现和实施目标，我们的网站和Emerge网络将为推动基因组医学成为现代医疗保健贡献者的愿景做出重要贡献。

 DESCRIPTION (provided by applicant): With their introduction into practice over the last two decades, electronic medical records (EMRs) have become increasingly recognized as platforms to not only improve delivery of care to the individual but also to understand variability in domain such as disease presentation and outcomes or quality assurance. Coupling dense genomic information to EMRs in eMERGE has provided tools for both discovery and initial implementation in genomic medicine, while raising new challenges and opportunities for using genomic data in healthcare. These include developing and mining the large datasets necessary to identify groups of patients with extreme phenotypes or rare genotypes; identifying clinically-relevant subsets of common diseases; and identifying actionable genomic variants and determining how best to deploy these in a learning healthcare system. Building on our experience and contributions to eMERGE-I and eMERGE-II, we propose here three specific aims to address these challenges. In Specific Aim 1, we will expand the network's phenotyping library by creating increasingly granular phenotype definitions that identify specific subsets of disease with predictable clinical courses or response to therapies. Genotype-phenotype relations will be studied by GWAS and advanced PheWAS methodology we have developed. In Specific Aim 2, we will identify rare variants with strong associations with human traits by resequencing 100 genes in 2,500 subjects at our center as part of the eMERGE-III 25,000 patient cohort. We propose studying genes with variants known to affect human health and drug responses, and variants that our preliminary PheWAS analysis implicates as robust markers of important human phenotypes. In Specific Aim 3, we will expand PREDICT, our pre-emptive pharmacogenomic implementation program, to develop a pipeline that will deliver actionable variants to patients and providers and to assess their response. We will collaborate across eMERGE to develop, implement, and assess tools to deliver new information, measuring impact to ensure optimal benefit to patients. By executing these discovery and implementation aims, our site and the eMERGE network will contribute importantly to advancing the vision of Genomic Medicine as a contributor to modern healthcare.