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Defining Phenotypes from Clinical Data to Drive Genomic Research.
Jamie R Robinson1,2, Wei-Qi Wei1, Dan M Roden1,3,4
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
Annual Review of Biomedical Data Science
|June 10, 2021
Summary
Electronic health records (EHRs) provide rich patient data for genomic research. Repurposing EHRs enhances the discovery of genetic influences on diseases and drug responses.
Area of Science:
- Genomics
- Biomedical Informatics
- Clinical Research
Background:
- Longitudinal patient data in electronic health records (EHRs) is increasingly linked to DNA biobanks.
- EHRs offer extensive health-related phenotypes, including drug responses, expanding research possibilities.
- Early research relied on manual chart reviews, but modern approaches use algorithms on large datasets.
Purpose of the Study:
- To review current methods, impact, challenges, and opportunities for using clinical data to define patient phenotypes for genomics discovery.
- To highlight the value of repurposing electronic health record data in genomic research.
Main Methods:
- Utilizing rule-based and machine learning algorithms on large electronic health record corpora.
- Analyzing genome-wide and phenome-wide data derived from EHRs.
- Leveraging linked EHR and DNA biobank data for research.
Main Results:
- Repurposing EHR data is a powerful method for identifying genomic influences on diseases and traits.
- EHR data significantly expands the phenome available for genetic discovery.
- The use of EHR data has demonstrated success in elucidating genomic impacts on drug response phenotypes.
Conclusions:
- Repurposing clinical data from EHRs is crucial for advancing genomics discovery.
- The application of EHR data in large cohort studies is expected to increase.
- EHR data enables a deeper understanding of the genetic underpinnings of health and disease.
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