Identification of Transdiagnostic Childhood Externalizing Pathology Within an Electronic Medical Records Database and Application to the Analysis of Rare Copy Number Variation.

Abstract

Externalizing traits and behaviors are broadly defined by impairments in self-regulation and impulse control that typically begin in childhood and adolescence. Externalizing behaviors, traits, and symptoms span a range of traditional psychiatric diagnostic categories. In this study, we sought to generate an algorithm that could reliably identify transdiagnostic childhood-onset externalizing cases and controls within a university hospital electronic health record (EHR) database. Within the Vanderbilt University Medical Center (VUMC) EHR, our algorithm identified cases with a clinician-validated positive predictive value of 90% and controls with a negative predictive value of 88%. In individuals of genetically defined European ancestry (CEU-clustered; N_case = 487, N_control = 5638), case status was significantly associated with psychiatric comorbidity and with elevated externalizing polygenic scores (OR: 1.20; 95% CI: 1.09-1.33; p = 1.14 × 10^-3; based on published genome-wide association data). To test whether our cohort definitions could be applied to generate novel genetic insights, we examined rare (allele frequency < 0.5%) copy number variation. An association (OR: 9.70; CI: 3.24-29.0) was identified in the CEU-clustered cohort on chromosome 2 (chr2: 45,408,678-45,551,530; duplication), although the statistical strength of this association was modest (p = 0.052). We also examined the role of an externalizing burden score based on the number of externalizing diagnoses present in cases and found similar results to our case-control analysis. This analysis identified several other statistically significant CNV region associations. This study provides a framework for identifying childhood externalizing case-control cohorts within an EHR. Future work should validate this framework within other health systems. A broadly applicable algorithm, like this one, may allow for detection of rare outcomes or outcomes in populations historically excluded from genomic research through meta-analysis of data across health care systems.