A/Professor Lea K Davis, Vanderbilt University, USA : "Beyond biomarkers: Mining clinical lab data from the Electronic Health Record to advance psychiatric genomics"
Speaker:
A/Professor Lea K Davis
Dept of Medicine
Vanderbilt University, Nashville TN37235, USA
Title: "Beyond biomarkers: Mining clinical lab data from the Electronic Health Record to advance psychiatric genomics"
Abstract:
Abstract: Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are typically stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations in the form of clinical lab tests. A total of 3,075 quantitative lab tests were extracted from Vanderbilt University Medical Center’s (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with measured levels of the biomarker and known biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD), then tested the approach to discover novel associations with major depression (MDD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of lipids, CAD, and MDD polygenic risk scores across 278 heritable lab tests. Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses. We present two methods, results, and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.
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