Columbia GENIE (GENomic Integration with Ehr)

Columbia GENIE（基因组与 Ehr 集成）

• 批准号: 8968053
• 负责人: ALI G GHARAVI
• 金额: $ 85.95万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8968053
• 关键词: Address; Algorithms; Basic Science; Clinical; Clinical Data; Clinical Informatics; Clinical Research; Collaborations; Communities; Complement; Consent; Country; Data; Data Quality; Diagnosis; Discipline; Disease; Electronic Health Record; Ethics; Funding; Genetic; Genetic Risk; Genomics; Goals; Health; Health Personnel; Health Status; Height; Hereditary Disease; Hospitals; Incidental Findings; Individual; Informatics; Institutes; Intervention; Knowledge; Learning; Link; Literature; Medical Genetics; Medical center; Medicine; Methods; Modeling; New York City; Participant; Patient Care; Patient Preferences; Patient Self-Report; Patients; Pharmacology; Phenotype; Precision Medicine Initiative; Provider; Research; Research Infrastructure; Sampling; Science; Solutions; Stratification; Systems Biology; Technology; Universities; Washington; Work; base; biobank; biomedical informatics; clinical data warehouse; clinical phenotype; cohort; data sharing; design; disorder prevention; disorder risk; exome sequencing; genetic association; health record; improved; legal implication; medically underserved; next generation; novel; patient population; point of care; precision medicine; public health ethics; public health relevance; rare variant; shared decision making; social implication; socioeconomics

 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 clinicians27-32. 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.

 描述（由适用提供）：哥伦比亚当前参加了现成的II联盟，在纽约市为大致和种族多样化的患者人群提供服务，并具有牢固的社区参与传统。我们为出现的II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EHR数据中的数据偏见，数据丢失和其他数据质量问题及其对表型的影响；定义下一代EHR表型的研究议程；探索使用患者自我报告的健康状况数据以补充EHR数据进行表型的使用；开发用于遗传性估计的新方法；设计信息的干预措施，以整合患者护理和临床研究工作流程，并将EHR和序列数据与基因组知识联系起来以进行决策支持；向患者传达遗传风险；解决患者对返回偶然发现的偏好；并研究返回结果对患者和临床医生的影响27-32。哥伦比亚还建立了精密医学作为一项主要的大学范围倡议。迄今为止，我们的生物库积累了与我们的EHR数据相关的26,310名个人组成的多民族队列，其中我们目前拥有3,059名患者的外显子序列数据，并同意广泛的遗传发现和广泛的数据共享，而无需重新介绍7,648例患者。这包括近4,000名自我报告的健康状况信息的患者，这些患者代表曼哈顿北部社区，并且不是根据任何特定疾病或诊断预先选择的。我们对III的提议建立在我们先前的工作和基因组医学专业知识的基础上。我们的四个具体目标将通过广泛传播数据和表型算法，与Emerge和其他研究概念（例如CSER，Legacy，Dheams，ohdsi，ohdsi，ohdsi，ctsa，pcori等）进行密切合作来实现。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提高下一代表型。 AIM 2：通过与Emerge Network和其他表型研究社区进行广泛的合作，对具有不同临床表型的稀有变体进行遗传关联研究。 AIM 3：通过利用基因组患者门户和遗传提供商动态引起的实用，可扩展的学习机制来返回结果，并纳入患者的偏好，以返回基因组结果，返回结果以及研究患者对返回结果的理解。 AIM 4：通过增强和验证我们的临床和信息基础设施来提供基因组决策支持，以通过学习机制来量身定制决策。