Genomic medicine offers hope for improved diagnostic methods and for more effective, patient specific therapies. Genome-wide associated studies (GWAS) elucidate genetic markers that improve clinical understanding of risks and mechanisms for many diseases and conditions and that may ultimately guide diagnosis and therapy on a patient-specific basis. The previous two cycles of this effort (2011-2014 and 2014-2018) introduced the phenome-wide association study (PheWAS) as a systematic and efficient approach to identify novel disease-variant associations and discover pleiotropy using electronic health records (EHRs). This proposal will develop novel methods to identify associations based on patterns of phenotypes using a phenotype risk score (PheRS) methodology to systematically search for the influence of Mendelian disease variants on common disease. By doing so, it also creates a way to assess pathogenicity for rare variants, and will identify patients at highest risk of having undiagnosed Mendelian disease. The project is enabled by large DNA biobanks coupled to de-identified copies of EHR. This project has four specific aims. First, we will develop and validate PheRS for assessment of variant pathogenicity by leveraging billing codes, laboratory data, and NLP features in its predictive algorithms.
The second aim i s to apply PheRS in huge populations to create a robust repository of rare variant associations in diverse populations (eMERGE Network and large national cohorts, which could approach 2 million people with genotype data).
The third aim i s to assess Mendelian disease penetrance and evaluate PheRS as a tool to identify patients at risk for undiagnosed Mendelian disease.
The fourth aim i s make these tools and resources broadly available to aid in variant interpretation and facilitate others running PheRS. The tools generated from this project will validate new approaches to interpreting the function of rare variants, improve basic understanding of Mendelian disease, greatly enhance our understanding of the contribution of Mendelian disease variants to common disease and traits, and offers a potential approach to identify subpopulations of patients for whom new therapies may offer benefit.

Public Health Relevance

Genomic medicine offers hope for improved diagnosis and for more effective, patient- specific therapies. This Phenotype Risk Score (PheRS) proposal, built on the prior PheWAS methodology, provides a systematic approach to search for patients with inborn genetic diseases in the electronic health record data and identifies those at highest risk of having undiagnosed diseases. The PheRS method not only has enormous potential to inspire and inform translational science, it also has the potential to impact general practice medicine in a clinical setting.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Special Emphasis Panel (ZLM1)
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Ye, Jane
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Vanderbilt University Medical Center
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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
Zhou, Wei; Nielsen, Jonas B; Fritsche, Lars G et al. (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet 50:1335-1341
Rhoades, Seth D; Bastarache, Lisa; Denny, Joshua C et al. (2018) Pulling the covers in electronic health records for an association study with self-reported sleep behaviors. Chronobiol Int 35:1702-1712
Barnado, April; Carroll, Robert J; Casey, Carolyn et al. (2018) Phenome-wide association study identifies marked increased in burden of comorbidities in African Americans with systemic lupus erythematosus. Arthritis Res Ther 20:69
Bastarache, Lisa; Hughey, Jacob J; Hebbring, Scott et al. (2018) Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science 359:1233-1239
Robinson, Jamie R; Denny, Joshua C; Roden, Dan M et al. (2018) Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records. Clin Transl Sci 11:112-122
Zhao, Junfei; Cheng, Feixiong; Jia, Peilin et al. (2018) An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies. Genome Med 10:7
Mosley, Jonathan D; Feng, QiPing; Wells, Quinn S et al. (2018) A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. Nat Commun 9:3522
Bloodworth, Melissa H; Rusznak, Mark; Bastarache, Lisa et al. (2018) Association of ST2 polymorphisms with atopy, asthma, and leukemia. J Allergy Clin Immunol 142:991-993.e3
Denny, Joshua C; Van Driest, Sara L; Wei, Wei-Qi et al. (2018) The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development. Clin Pharmacol Ther 103:409-418

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