National Human Genome Research Institute's vision for genomic medicine includes elements of both discovery and implementation. This vision seeks to improve the practice of medicine through the appropriate integration of genomic information into clinical care while continuing to better understand fundamentals of genomic variation, genome structure and the role of genetic variation in disease and therapeutic outcomes. The eMERGE network has made critical contributions to discovery by developing methods for high throughput electronic phenotyping using information captured in electronic health records (EHRs) in the course of clinical care and identifying new associations between diseases and quantitative traits with common genetic variants through genome wide associations studies. eMERGE has also provided leadership in implementing genomic medicine by developing interfaces with EHR, clinical decision support, strategies for holding genomic variant information and transferring clinically relevant variants into the EHR, and returning genomic variant information to physicians and patients, both for disease susceptibility and for improving therapeutic management, particularly with prescription and dosing of medications. In addition, development and dissemination of best practices for implementation has been an important goal of eMERGE. As a participant in both eMERGE I and II, Northwestern (NU) through its EHR-linked biobank NUgene, has made significant contributions to all of these activities, with several NU investigators playing key leadership roles. In this application we propose to leverage the infrastructure and expertise at NU to (a) discover associations between rare variants in at least 100 sequenced genes and common variants from the eMERGE GWAS dataset from phenotypes derived from EHR data mining, (b) enroll 2000 patients into the study who agree to receive genomic information and allow the information to be stored in their EHR, (c) return clinically actionable results to healthcare providers and patients to determine utility and clinical outcomes, and (d) develop and share best practices related to returning genomic results, educating physicians and patients, and the related ethical, legal and social implications. Successfully achieving these goals will provide new scientific knowledge and significant real world experience that will advance the NHGRI vision for both discovery and implementation across the spectrum of its strategic vision.

Public Health Relevance

This project answers questions about applying genomic analysis in the clinical and preventive care of patients. We propose to study patient and physician choices using genomic medicine in a real world clinical setting.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01HG008673-04S1
Application #
9882459
Study Section
Special Emphasis Panel (ZHG1)
Program Officer
Rowley, Robb Kenneth
Project Start
2015-09-01
Project End
2020-06-30
Budget Start
2019-04-11
Budget End
2020-06-30
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Northwestern University at Chicago
Department
Anatomy/Cell Biology
Type
Schools of Medicine
DUNS #
005436803
City
Chicago
State
IL
Country
United States
Zip Code
60611
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Wei, Wei-Qi; Li, Xiaohui; Feng, Qiping et al. (2018) LPA Variants Are Associated With Residual Cardiovascular Risk in Patients Receiving Statins. Circulation 138:1839-1849
Hylind, Robyn; Smith, Maureen; Rasmussen-Torvik, Laura et al. (2018) Great expectations: patient perspectives and anticipated utility of non-diagnostic genomic-sequencing results. J Community Genet 9:19-26
Wang, Liuyang; Pittman, Kelly J; Barker, Jeffrey R et al. (2018) An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease. Cell Host Microbe 24:308-323.e6
Mosley, Jonathan D; Shoemaker, M Benjamin; Wells, Quinn S et al. (2017) Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes. Circ Cardiovasc Genet 10:
Sanderson, Saskia C; Brothers, Kyle B; Mercaldo, Nathaniel D et al. (2017) Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US. Am J Hum Genet 100:414-427
Gustafson, Erin; Pacheco, Jennifer; Wehbe, Firas et al. (2017) A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records. IEEE Int Conf Healthc Inform 2017:83-90
Barefield, David Y; Puckelwartz, Megan J; Kim, Ellis Y et al. (2017) Experimental Modeling Supports a Role for MyBP-HL as a Novel Myofilament Component in Arrhythmia and Dilated Cardiomyopathy. Circulation 136:1477-1491
Dumitrescu, Logan; Ritchie, Marylyn D; Denny, Joshua C et al. (2017) Genome-wide study of resistant hypertension identified from electronic health records. PLoS One 12:e0171745
Almoguera, Berta; Vazquez, Lyam; Mentch, Frank et al. (2017) Identification of Four Novel Loci in Asthma in European American and African American Populations. Am J Respir Crit Care Med 195:456-463

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