EMR Phenotype and Community Engaged Genomic Associations

EMR 表型和社区参与的基因组关联

• 批准号: 8520368
• 负责人: CHRISTOPHER G CHUTE
• 金额: $ 95.17万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8520368
• 关键词: Address; Algorithms; Area; Atrial Fibrillation; Bioethics; Cardiology; Clinic; Clinical; Code; Collaborations; Colonic Polyps; Communication; Communication Tools; Communities; Complex; Comprehension; Computerized Medical Record; Consent; Coronary heart disease; Data; Diagnosis; Disease; Disease susceptibility; Dose; Drops; Drug usage; Early Diagnosis; Effectiveness; Electronic library; Empirical Research; Epidemiologist; Ethical Issues; Ethics; Future; Genetic; Genetic Predisposition to Disease; Genetic Risk; Genetic screening method; Genome; Genomics; Genotype; Individual; Informatics; Instruction; Interview; Knowledge; Laboratories; Libraries; Life Style; Link; Medicine; Methods; Myopathy; Natural Language Processing; Patient Care; Patients; Perception; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Predisposition; Preventive; Principal Investigator; Procedures; Professional counselor; Provider; Public Health; Publishing; Randomized Clinical Trials; Research; Research Infrastructure; Risk; Risk Factors; Scientist; Site; Staging; Surveys; Testing; Text; Thromboembolism; Translating; Variant; Venous; Work; adverse outcome; base; clinical practice; clinical risk; cohort; community consultation; cost; disorder risk; exome sequencing; experience; fitness; genetic risk assessment; genetic variant; genome wide association study; heart disease risk; interest; open source; point of care; repository; response; screening; tool

DESCRIPTION (provided by applicant): The electronic medical record (EMR) can be leveraged for high throughput phenotyping of large numbers of patients for genomics research. As part of eMERGE-l, we used EMR-based algorithms to enable genome- wide association studies (GWAS) of several primary and network-wide phenotypes. The present application will leverage the research infrastructure established in eMERGE-l to identify common genetic variants that influence medically important phenotypes. The Mayo eMERGE-ll cohort (n=6916) includes the 3769 eMERGE-l patients and an additional 3147 individuals, the majority (90%) genotyped on the same lllumina 660W platform. We will work with other eMERGE-ll sites to expand and validate the library of electronic phenotyping algorithms to enable GWAS of multiple phenotypes of interest. A major focus of our application is to translate recent GWAS findings to clinical practice. Our specific aims are: Specific aim 1. Conduct EMR-based GWAS to identify common genetic variants that influence a) inter-individual variation in cardiorespiratory fitness and response to statin medications and b) susceptibility to venous thromboembolism and colon polyps. Specific aim 2. Quantify genetic risk of a common 'complex' disease - coronary heart disease (CHD) - and an adverse drug response - statin myopathy. We will develop risk communication tools that convey the clinical and genetic components of risk to both patients and care providers. Specific aim 3. Develop informatics approaches to incorporate genomic data into the EMR, including links to clinical decision support. Specific aim 4. Conduct a randomized-clinical trial to investigate how patients respond to genetically informed CHD-risk. We will re-consent 150 eMERGE-l patients without CHD, communicate the results via a genetic counselor, and discuss in detail the implications of the testing relevant to disease risk. The effectiveness of the communication and the patients' comprehension of risk, their hopes and concerns, and planned changes in lifestyle will be assessed by surveys and interviews after the patient-counselor encounter. As part of our ongoing efforts in community consultation, we will establish a community advisory group specific to this project. RELEVANCE (See instructions): The proposed application will leverage the research infrastructure established in eMERGE-l to identify common genetic variants that influence medically important phenotypes. We will develop tools to incorporate genomic information in the EMR. In addition, we will investigate clinical, translational, and ethical aspects of genetic testing for complex diseases and assess the response of patients to genetic testing.

描述（由申请人提供）：电子病历（EMR）可用于基因组学研究的大量患者的高通量表型分析。作为eMERGE-I的一部分，我们使用基于EMR的算法来实现几种主要和网络范围表型的全基因组关联研究（GWAS）。本申请将利用在eMERGE-I中建立的研究基础设施来鉴定影响医学上重要的表型的常见遗传变异。马约eMERGE-II队列（n=6916）包括3769例eMERGE-I患者和另外3147例个体，大多数（90%）在相同的Illumina 660 W平台上进行基因分型。我们将与其他eMERGE-II研究中心合作，扩展和验证电子表型分析算法库，以实现多种感兴趣表型的GWAS。我们应用的一个主要重点是将最近的GWAS发现转化为临床实践。我们的具体目标是：具体目标1。进行基于EMR的GWAS，以确定影响a）心肺适应性和对他汀类药物反应的个体间差异和B）静脉血栓栓塞和结肠息肉易感性的常见遗传变异。具体目标2。量化一种常见的“复杂”疾病-冠心病（CHD）-和药物不良反应-他汀类肌病的遗传风险。我们将开发风险沟通工具，向患者和护理提供者传达风险的临床和遗传成分。具体目标3。开发信息学方法，将基因组数据纳入EMR，包括与临床决策支持的链接。具体目标4。进行一项随机临床试验，以调查患者如何应对遗传学告知的CHD风险。我们将重新同意150名没有CHD的eMERGE-1患者，通过遗传顾问传达结果，并详细讨论与疾病风险相关的测试的影响。沟通的有效性和患者对风险的理解，他们的希望和担忧，以及生活方式的计划改变将在患者-顾问会面后通过调查和访谈进行评估。作为我们持续进行的社区咨询工作的一部分，我们将成立一个专门针对该项目的社区咨询小组。 相关性（参见说明）：拟议的应用将利用eMERGE-l中建立的研究基础设施来识别影响医学重要表型的常见遗传变异。我们将开发将基因组信息纳入EMR的工具。此外，我们将调查复杂疾病基因检测的临床，翻译和伦理方面，并评估患者对基因检测的反应。