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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Cross Biobank Comparison of Phenomic Profiles
Abigail Newbury1,2, Xinzhuo Jiang1, Karthik Natarajan1
1Department of Biomedical Informatics, Columbia University, New York City, NY, US.
None:
The All of Us (AoU) Research Program and UK Biobank (UKBB) boast a wealth of EHR data, which can be harnessed to refine cohort selection via rule-based phenotyping algorithms. The Observational Health Data Sciences and Informatics (OHDSI) Phenotype Library (PL) hosts many complex phenotyping rules. Here, we compare prevalence for 423 OHDSI PL cohorts in AoU and UKBB. For three select diseases (T2D, COPD, Acute MI), we analyze differences in demographics, social determinants of health (SDOH), geographic prevalence, and genome-wide association study (GWAS) results. We found that AoU has a significantly higher prevalence for 80% of phenotypes compared to UKBB. We also found that for the select diseases, SDOH variables between the two biobanks differ significantly. Findings for each of these three diseases confirm known regions of high risk. Additionally, GWAS in UKBB discovered more genes associated with each of the three diseases than GWAS in AoU.
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