Pediatric considerations for pharmacogenetic selective serotonin reuptake inhibitors clinical decision support.

Pharmacogenetic testing for psychiatry is growing at a rapid pace, with multiple sites utilizing results to help clinical decision-making. Genotype-guided dosing and drug selection have been implemented at several sites, including Vanderbilt University Medical Center, where clinical decision support (CDS) based on pharmacogenetic results went live for selective serotonin reuptake inhibitors in 2020 for both adult and pediatric patients. Effective and appropriate implementation of CYP2D6- and CYP2C19-guided CDS for the pediatric population requires consideration of the evidence for the pharmacogenetic associations, medication indications, and appropriate alternative therapies to be used when a pharmacogenetic contraindication is identified. In this article, we review these pediatric pharmacogenetic considerations for selective serotonin reuptake inhibitor CDS. We include a case study, the current literature supporting clinical recommendations, considerations when designing pediatric CDS, future implications, and examples of sertraline, (es)citalopram, paroxetine, and fluvoxamine alerts.

A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers.

Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.