We describe here the design and initial implementation of the eMERGE-PGx project. eMERGE-PGx, a partnership of the eMERGE and PGRN consortia, has three objectives : 1) Deploy PGRNseq, a next-generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1–3 year timeframe across several clinical sites; 2) Integrate well-established clinically-validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and assess process and clinical outcomes of implementation; and 3) Develop a repository of pharmacogenetic variants of unknown significance linked to a repository of EHR-based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site-specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to manage incidental findings, and patient and clinician education methods.

Genetic variation can affect drug response in multiple ways, though it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE-PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE-PGx data release includes sequence-derived data from ~5000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled CADD score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Early and accurate identification of adverse drug events (ADEs) is critically important for public health. We have developed a novel approach for predicting ADEs, called predictive pharmacosafety networks (PPNs). PPNs integrate the network structure formed by known drug-ADE relationships with information on specific drugs and adverse events to predict likely unknown ADEs. Rather than waiting for sufficient post-market evidence to accumulate for a given ADE, this predictive approach relies on leveraging existing, contextual drug safety information, thereby having the potential to identify certain ADEs earlier. We constructed a network representation of drug-ADE associations for 809 drugs and 852 ADEs on the basis of a snapshot of a widely used drug safety database from 2005 and supplemented these data with additional pharmacological information. We trained a logistic regression model to predict unknown drug-ADE associations that were not listed in the 2005 snapshot. We evaluated the model's performance by comparing these predictions with the new drug-ADE associations that appeared in a 2010 snapshot of the same drug safety database. The proposed model achieved an AUROC (area under the receiver operating characteristic curve) statistic of 0.87, with a sensitivity of 0.42 given a specificity of 0.95. These findings suggest that predictive network methods can be useful for predicting unknown ADEs.

Background Whole-exome sequencing (WES) finds a CKD-related mutation in approximately 20% of patients presenting with CKD before 25 years of age. Although provision of a molecular diagnosis could have important implications for clinical management, evidence is lacking on the diagnostic yield and clinical utility of WES for pediatric renal transplant recipients. Methods To determine the diagnostic yield of WES in pediatric kidney transplant recipients, we recruited 104 patients who had received a transplant at Boston Children's Hospital from 2007 through 2017, performed WES, and analyzed results for likely deleterious variants in approximately 400 genes known to cause CKD. Results By WES, we identified a genetic cause of CKD in 34 out of 104 (32.7%) transplant recipients. The likelihood of detecting a molecular genetic diagnosis was highest for patients with urinary stone disease (three out of three individuals), followed by renal cystic ciliopathies (seven out of nine individuals), steroidresistant nephrotic syndrome (nine out of 21 individuals), congenital anomalies of the kidney and urinary tract (ten out of 55 individuals), and chronic glomerulonephritis (one out of seven individuals). WES also yielded a molecular diagnosis for four out of nine individuals with ESRD of unknown etiology. The WESrelated molecular genetic diagnosis had implications for clinical care for five patients. Conclusions Nearly one third of pediatric renal transplant recipients had a genetic cause of their kidney disease identified by WES. Knowledge of this genetic information can help guide management of both transplant patients and potential living related donors.

No abstract