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Published on: August 15, 2019
Evaluating Phenotypic Data Elements for Genetics and Epidemiological Research: Experiences from the eMERGE and PhenX
Jyotishman Pathak1, Helen Pan, Janey Wang
1Mayo Clinic, Rochester, MN;
Summary
Integrating electronic medical record (EMR) data with genome-wide association studies (GWAS) enables disease research. Standardizing clinical data improves analysis for identifying genetic variants linked to complex diseases.
Area of Science:
- Genomics
- Clinical Informatics
- Biomedical Data Science
Background:
- Genome-wide association studies (GWAS) combined with electronic medical record (EMR) data offer powerful insights into complex diseases.
- Challenges in integrating diverse EMR data, including non-standardized clinical information and phenotypes, hinder comprehensive analysis.
- The eMERGE (Electronic Medical Record and Genomics) network aims to overcome these challenges by standardizing data for genetic research.
Purpose of the Study:
- To present methods for mapping phenotypic data elements from the eMERGE network.
- To facilitate the integration of clinical data from EMRs with GWAS for disease susceptibility studies.
- To enhance data interoperability and promote cross-study data pooling for genotype-phenotype association analyses.
Main Methods:
- Phenotypic data elements from the eMERGE network were mapped to established standards.
- Utilized PhenX (Consensus Measures for Phenotypes and Exposures) and NCI's caDSR (Cancer Data Standards Registry and Repository) for data standardization.
- Developed and applied methods for harmonizing diverse clinical data and phenotypes.
Main Results:
- Successful mapping of eMERGE phenotypic data to PhenX and caDSR standards.
- Demonstrated the feasibility of standardizing varied clinical data for genetic research.
- Identified that adopting multiple standards enhances data accessibility and interoperability.
Conclusions:
- Standardizing clinical data from EMRs is crucial for effective GWAS.
- Adopting multiple biomedical terminologies and standards broadens study reach and enhances data integration.
- Improved data interoperability through standardization facilitates the detection of complex genotype-phenotype associations.
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