Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record

Abstract

Large-scale DNA databanks linked to electronic medical record (EMR) systems have been proposed as an approach for rapidly generating large, diverse cohorts for discovery and replication of genotype-phenotype associations. However, the extent to which such resources are capable of delivering on this promise is unknown. We studied whether an EMR-linked DNA biorepository can be used to detect known genotype-phenotype associations for five diseases. Twenty-one SNPs previously implicated as common variants predisposing to atrial fibrillation, Crohn disease, multiple sclerosis, rheumatoid arthritis, or type 2 diabetes were successfully genotyped in 9483 samples accrued over 4 mo into BioVU, the Vanderbilt University Medical Center DNA biobank. Previously reported odds ratios (OR(PR)) ranged from 1.14 to 2.36. For each phenotype, natural language processing techniques and billing-code queries were used to identify cases (n = 70-698) and controls (n = 808-3818) from deidentified health records. Each of the 21 tests of association yielded point estimates in the expected direction. Previous genotype-phenotype associations were replicated (p < 0.05) in 8/14 cases when the OR(PR) was > 1.25, and in 0/7 with lower OR(PR). Statistically significant associations were detected in all analyses that were adequately powered. In each of the five diseases studied, at least one previously reported association was replicated. These data demonstrate that phenotypes representing clinical diagnoses can be extracted from EMR systems, and they support the use of DNA resources coupled to EMR systems as tools for rapid generation of large data sets required for replication of associations found in research cohorts and for discovery in genome science.

Enhancing Uterine Fibroid Research Through Utilization of Biorepositories Linked to Electronic Medical Record Data

“…Algorithms were then assessed by calculating PPV, NPV, sensitivity, and specificity as well as evaluating the associations between patient characteristics and indications for imaging with fibroid risk. [11][12][13] Indications for pelvic imaging were determined by manual review of participants EMR. All indications were collected and those reported are the most common indications across records.…”

Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

“…However, Tessier-Sherman and colleagues found that a carefully constructed algorithm for identifying hypertensive patients from claims data only found 43-61% of patients with elevated blood pressure values in their medical charts [25]. Ritchie et al describe a solution for this problem, which consists of a sophisticated, iterative approach to algorithm construction [26]. In their approach, clinical experts are consulted to develop an algorithm that selects cases via disease-specific combinations of billing codes, patient encounters, laboratory data, and NLP techniques on unstructured patient records.…”

Phenome-Wide Association Studies: Embracing Complexity for Discovery

“…Another example are the measures available within EHR, where patients have collections of International Classification of Disease (ICD) billing codes, free text, longitudinal clinical laboratory variable measures, and imaging data. To date, ICD-9 codes and phenotyping algorithms have both shown utility in association studies [20][21][22][23] .…”