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.
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.
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