Columbia GENIE (GENomic Integration with Ehr)

描述（由申请人提供）：哥伦比亚大学是emerging - ii联盟的当前参与者，在纽约市为不同种族和民族的患者提供服务，并具有很强的社区参与传统。我们为eMERGE-II的目标做出了重大贡献，包括开发和评估基于电子健康记录的表型算法；了解电子病历数据中的数据偏差、数据缺失和其他数据质量问题及其对表型的影响；确定下一代电子病历表型的研究议程；探索使用患者自我报告的健康状况数据来补充电子病历数据进行表型分析；开发新的遗传估计方法；设计信息学干预措施，整合患者护理和临床研究工作流程，并将电子病历和序列数据与基因组知识联系起来，为决策提供支持；向患者传达遗传风险；解决患者对返回偶然发现的偏好；调查返回结果对患者和临床医生的影响。哥伦比亚大学还将“精准医学”作为一项全校范围内的重大倡议。迄今为止，我们的生物库已经积累了26,310个多种族队列，他们的样本与我们的电子病历数据相关联，其中我们目前拥有3,059名患者的外显子组序列数据，并同意广泛的遗传发现和广泛的数据共享，无需重新同意7,648名患者。这包括近4000名患者，他们有丰富的自我报告的健康状况信息，他们是曼哈顿北部社区的代表，并且不是基于任何特定疾病或诊断预先选择的。我们对eMERGE-III的建议建立在我们之前在基因组医学方面的工作和专业知识的基础上。我们的四个具体目标将通过广泛传播数据和表现型算法，与eMERGE和其他研究联盟（例如，CSER， LEGACY， DHEAMS， OHDSI， CTSA， PCORI等）的密切合作，以及使用基于标准的形式化方法来实现。目标1：通过设计，验证和共享高通量，数据质量意识，基于标准的表型方法来推进下一代表型。目标2：通过与eMERGE网络和其他表型研究社区的广泛合作，对具有不同临床表型的罕见变异进行遗传关联研究。目标3：开发实用的、可扩展的学习机制，通过利用基因组患者门户和遗传提供者来动态地引出和整合患者对基因组结果返回、返回结果和研究患者对返回结果的理解的偏好。目标4：通过增强和验证我们的基因组决策支持临床和信息学基础设施，以及定制共享决策的学习机制，提供基因组决策支持。

DESCRIPTION (provided by applicant): A current participant in the eMERGE-II consortium, Columbia serves a racially and ethnically diverse patient population in New York City, and has a strong tradition of community engagement. We have made significant contributions to the goals of eMERGE-II, including developing and evaluating electronic health records-based phenotyping algorithms; understanding data biases, data missingness, and other data quality issues in EHR data and their impact on phenotyping; defining a research agenda for next-generation EHR phenotyping; exploring the use of patient self-reported health status data to complement EHR data for phenotyping; developing novel methods for hereditability estimation; designing informatics interventions to integrate patient care and clinical research workflows and to link EHR and sequence data with genomic knowledge for decision support; communicating genetic risk to patients; addressing patients' preferences for returning incidental findings; and investigating the impact of returning results on patients and clinicians. Columbia has also established Precision Medicine as a major university-wide initiative. To date, our biobank has accumulated a multiethnic cohort of 26,310 individuals with their samples linked to our EHR data, among which we currently have exome sequence data on 3,059 patients and consent for broad genetic discoveries and wide data sharing without re-consent from 7,648 patients. This includes nearly 4,000 patients with rich self- reported health status information, who are representative of the Northern Manhattan community, and were not pre-selected based on any specific disease or diagnosis. Our proposal for eMERGE-III builds on our prior work and expertise in genomic medicine. Our four specific aims will be accomplished by wide dissemination of data and phenotyping algorithms, close collaboration with eMERGE and other research consortia (e.g., CSER, LEGACY, DHEAMS, OHDSI, CTSA, PCORI, and so on), and by using standards-based formal methods. Aim 1: Advance next-generation phenotyping by designing, validating, and sharing high-throughput, data quality-aware, standards-based phenotyping methods. Aim 2: Perform genetic association studies of rare variants with diverse clinical phenotypes through broad collaboration with the eMERGE network and other phenotyping research communities. Aim 3: Develop practical, scalable learning mechanisms for returning results by leveraging a genomic patient portal and genetic providers to dynamically elicit and incorporate patient preferences for return of genomic results, returning results, and studying patient understanding of returned results. Aim 4: Provide genomic decision support by enhancing and validating our clinical and informatics infrastructure for genomic decision support with learning mechanisms for tailored shared decision-making.