Jessica K Dennis1,2,3, Julia M Sealock1,2, Peter Straub1,2, Younga H Lee4,5,6, Donald Hucks1,2, Ky'Era Actkins1,2,7, Annika Faucon1,2, Yen-Chen Anne Feng4,6,8, Tian Ge4,5,6, Slavina B Goleva1,2,9, Maria Niarchou1,2, Kritika Singh1,2, Theodore Morley1,2, Jordan W Smoller4,5,6, Douglas M Ruderfer1,2,10,11, Jonathan D Mosley11, Guanhua Chen12, Lea K Davis13,14,15,16,17,18. 1. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. 2. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. 3. Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada. 4. Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA. 5. Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA. 6. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA. 7. Department of Microbiology, Immunology, and Physiology, Meharry Medical College, Nashville, TN, 37232, USA. 8. Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA. 9. Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. 10. Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. 11. Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. 12. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA. 13. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. lea.k.davis@gmail.com. 14. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. lea.k.davis@gmail.com. 15. Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. lea.k.davis@gmail.com. 16. Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. lea.k.davis@gmail.com. 17. Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. lea.k.davis@gmail.com. 18. Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, 511-A Light Hall, 2215 Garland Ave, Nashville, TN, 37232, USA. lea.k.davis@gmail.com.
Abstract
BACKGROUND: Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are 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. METHODS: A total of 3075 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 biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. RESULTS: Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. CONCLUSIONS: Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.
BACKGROUND: Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are 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. METHODS: A total of 3075 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 biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. RESULTS: Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. CONCLUSIONS: Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.
Entities:
Keywords:
Biomarkers; Electronic health records; Genetic epidemiology; Population genetics
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