DEB2 is a medication indication and adverse effect knowledgebase derived from five publicly available sources: the VA’s National Drug File-Reference Terminology, MEDLINE, the US Food and Drug Administration’s drug product labels (via the SIDER2 database), the MedlinePlus consumer health information website, and DrugBank, a manually-curated drug target database. All medications, indications, and adverse effects in DEB2 are represented using the RxNorm and SNOMED-CT terminologies.
(reported odds ratios 1.14-2.36) in at least two previous studies. We developed automated phenotype identification algorithms that used NLP techniques (to identify key findings, medication names, and family history), billing code queries, and structured data elements (such as laboratory results) to identify cases (n=70-698) and controls (n=808-3818). Final algorithms achieved positive predictive values (PPV) of ≥97% for cases and 100% for controls on randomly selected cases and controls.
Methods to identify gene-disease associations primarily rely on clinical trials or observational cohorts and, more recently, Electronic Medical Record-linked DNA Biobanks. At Vanderbilt, we have used an EMR-linked DNA biobank called BioVU to derive case and controls populations using data within the EMR to define clinical phenotypes. Genetic data for these EMR-linked association studies are redeposited into BioVU for future EMR-linked studies. This has opened the possibility of "reverse GWAS" or "Phenome-wide association studies" (PheWAS).