Experiences from Electronic Health Records (EHRs) and what we can expect in the future

Transcription

1 Emerging Approaches for Environmental Health Data Integration Experiences from Electronic Health Records (EHRs) and what we can expect in the future Marylyn D. Ritchie, PhD Department of Genetics Institute for Biomedical Informatics University of Pennsylvania

2 EPIC EHR

3 Allscripts EHR

4 Electronic Health Records (EHRs) Formerly known as Electronic Medical Records (EMRs) Purpose Medical care Billing insurance Ordering procedures and medications Scheduling Developed in 1990s Widespread adoption after the American Recovery and Reinvestment Act mandated that all public and private healthcare providers were required to adopt them by January 1, 2014

5 Can EHR be used for biomedical research? Am J Hum Genet Apr 9;86(4):

6 BioVU Pilot Project First 10,000 people in the Vanderbilt BioVU biorepository Selected 5 phenotypes with successful GWAS findings Crohn s disease, Multiple Sclerosis, Rheumatoid Arthritis, and Type II Diabetes, and Atrial fibrillation All of these GWAS were from traditional epidemiological cohort/case-control studies Genotyped 10,000 people for XX SNPs Testing for association for each SNP with the phenotype from GWAS GOAL: to determine which replicated in a phenotype derived from an EHR Ritchie MD, Denny JC, Crawford DC, Havens A, Weiner J, Pulley JM, Basford M, Balser JR, Masys DR, Haines JL, Roden DM. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. American Journal of Human Genetics, 86(4): (2010).

7 emerge Network

8 emerge Network NHGRI initiative Started in 2007 Biorepository linked to electronic health records (EHRs) Different sets of genetic data available on subsets of individuals across the network Genome-wide genotype data (GWAS) PGRNseq Whole exome sequence Whole genome sequence emergeseq Wide range of phenotypes derived from EHR

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10 Marshfield Personalized Medicine Research Project (PMRP)

11 McCarty CA, Peissig PL, Caldwell MD, Wilke RA. The Marshfield Clinic Personalized Medicine Research Project: Scientific update and lessons learned in the first 6 years. PER MED 2008;5:

12 Marshfield PMRP PMRP has been a participating site in emerge since ,947 participants with GWAS array genotype data imputed to 1000Genomes and now also HRC 2,271 of these participants also completed a questionnaire that was part of the PhenX Toolkit project alcohol use, smoking, sun exposure, hand use preference, depression, and stroke history Also participated in the Diet History Questionnaire (DHQ) and the Baecke Physical Activity Questionnaire

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17 Marshfield PMRP PMRP has been a participating site in emerge since ,947 participants with GWAS array genotype data imputed to 1000Genomes and now also HRC 2,271 of these participants also completed a questionnaire that was part of the PhenX Toolkit project alcohol use, smoking, sun exposure, hand use preference, depression, and stroke history Also completed DHQ and Baecke activity questionnaires Environment-wide association analyses

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22 Marshfield PMRP PMRP has been a participating site in emerge since ,947 participants with GWAS array genotype data imputed to 1000Genomes and now also HRC 2,271 of these participants also completed a questionnaire that was part of the PhenX Toolkit project alcohol use, smoking, sun exposure, hand use preference, depression, and stroke history Also completed DHQ and Baecke activity questionnaires Environment-wide association analyses Genetic association analyses Main effects Gene-gene interactions Gene-environment interactions

23 Gene-environment interactions Filtering E through EWAS

24 Gene-environment interactions Filtering E through EWAS Example: cataract

25 Gene-environment interactions Filtering E through EWAS Example: cataract

26 Gene-environment interactions Filtering E through EWAS Example: cataract

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28 From 530,000 SNPs and 12 PhenX variables 288 p<1x10-4

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31 Future

32 Linking EHR data through geocodes Geocoding is the process of transforming a description of a location such as a pair of coordinates, an address, or a name of a place to a location on the earth's surface The resulting locations are output as geographic features with attributes, which can be used for mapping or spatial analysis

33 Geocode participants Link geocodes with public/private databases of exposures Link EHR phenotypes with environmental and social exposures EWAS GxE

34 Summary Electronic Health Records are a useful source of phenotype data on large research populations Data integration of Electronic Health Records with environmental exposures continues to emerge Enables high-throughput environment analyses: Environment-wide Association Studies (EWAS) Gene-environment Association Studies Geocoding research participants linked to their EHR will enable more connections with environmental data Requires using optimal geocoding methodologies Also requires appropriate privacy protections (address is considered one of the 18 HIPAA identifiers) data broker/irb

35 Acknowledgements Anna Basile, PhD former student Yuki Bradford, bioinformatics analyst Scott Dudek, software developer Alex Frase, software developer Binglan Li, PhD student Anastasia Lucas, bioinformatics analyst Jason Miller, PhD, Post doc Anurag Verma, PhD, staff scientist Shefali Verma, PhD, staff scientist Sudha Veturi, PhD, Post doc Blair Zhang, PhD student