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 understanding of risks and causes for many diseases, and may guide diagnosis and therapy on a patient-specific basis. This project will take another approach to identify gene-disease associations: perform "reverse GWAS," or phenome- wide association study (PheWAS), to determine which phenotypes are associated with a given genotype. The project is enabled by a large DNA biobank coupled to a de- identified copy of the electronic medical record. This project has four specific aims. First, the project will develop and validate a standardized approach to extract disease phenotypes from EMR records, integrating national standard terminologies of clinical disorders and descriptors relating to treatment and diagnosis of each disease to create a sharable knowledge base. The project will use natural language processing, structured data queries, and heuristic and machine learning methods to accurately identify patients with each disease and corresponding controls.
The second aim i s to perform PheWAS analyses using existing genotype data. To validate the method, the project will use PheWAS to "rediscover" SNPs with known disease associations. The project will also investigate statistical methods for large-scale multiple hypothesis testing to discover novel phenotype associations.
The third aim i s to apply the PheWAS algorithms in four other sites with EMR-linked DNA biobanks and compare results. In the fourth aim, the project will validate novel phenotype-genotype associations discovered through PheWAS with new genotyping in a previously untested population. The tools generated from this project will not only make PheWAS possible, but will also broadly enable clinical research and subsequent genetic studies.

Public Health Relevance

The promise of genomic medicine is to predict individuals'disease risk and treatment given their genetic information. This project will develop methods to identify diseases from electronic medical records and then find novel genetic associations from existing genomic data.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM010685-02
Application #
8326646
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
2011-09-01
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
2
Fiscal Year
2012
Total Cost
$332,514
Indirect Cost
$119,364
Name
Vanderbilt University Medical Center
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Mosley, Jonathan D; van Driest, Sara L; Wells, Quinn S et al. (2016) Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data. Circ Cardiovasc Genet 9:521-530
Wei, Wei-Qi; Teixeira, Pedro L; Mo, Huan et al. (2016) Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J Am Med Inform Assoc 23:e20-7
Doss, Jayanth; Mo, Huan; Carroll, Robert J et al. (2016) Phenome-Wide Association Study of Rheumatoid Arthritis Subgroups Identifies Association between Seronegative Disease and Fibromyalgia. Arthritis Rheumatol :
Claar, Dru D; Larkin, Emma K; Bastarache, Lisa et al. (2016) A Phenome-Wide Association Study Identifies a Novel Asthma Risk Locus Near TERC. Am J Respir Crit Care Med 193:98-100
Heatherly, Raymond; Rasmussen, Luke V; Peissig, Peggy L et al. (2016) A multi-institution evaluation of clinical profile anonymization. J Am Med Inform Assoc 23:e131-7
Simonti, Corinne N; Vernot, Benjamin; Bastarache, Lisa et al. (2016) The phenotypic legacy of admixture between modern humans and Neandertals. Science 351:737-41
Mosley, Jonathan D; Witte, John S; Larkin, Emma K et al. (2016) Identifying genetically driven clinical phenotypes using linear mixed models. Nat Commun 7:11433
Li, Haiquan; Achour, Ikbel; Bastarache, Lisa et al. (2016) Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions. NPJ Genom Med 1:
Wang, Xusheng; Pandey, Ashutosh K; Mulligan, Megan K et al. (2016) Joint mouse-human phenome-wide association to test gene function and disease risk. Nat Commun 7:10464
Mosley, Jonathan D; Brittain, Evan L; Loyd, James E et al. (2015) Letter by Mosley Regarding Article, "Iron Homeostasis and Pulmonary Hypertension: Iron Deficiency Leads to Pulmonary Vascular Remodeling in the Rat". Circ Res 117:e56-7

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