Abstract
Recent advances in next-generation sequencing technology have led to the production of an unprecedented volume of genomic data, thus further advancing our understanding of the role of genetic variation in clinical pharmacogenomics. In the present study, we used whole exome sequencing data from 50,726 participants, as derived from the DiscovEHR cohort, to identify pharmacogenomic variants of potential clinical relevance, according to their occurrence within the PharmGKB database. We further assessed the distribution of the identified rare and common pharmacogenomics variants amongst different GnomAD subpopulations. Overall, our findings show that the use of publicly available sequence data, such as the DiscovEHR dataset and GnomAD, provides an opportunity for a deeper understanding of genetic variation in pharmacogenes with direct implications in clinical pharmacogenomics.
Highlights
The DiscovEHR cohort is the result of the collaboration between Geisinger (GHS) and the Regeneron Genetics Center
The project-level VCF file, including all variants reported in the DiscovEHR cohort, was retrieved from the DiscovEHRshare website bcftools and was used in order to extract variants located in the 231 Drug Metabolizing Enzymes and Transporters (DMET) pharmacogenes of interest (Table S1)
In line with the findings by Dewey et al (2016), who assessed all functional variants within the DiscovEHR cohort, we found that the majority of the PGx variants are single nucleotide variations (SNVs) (N = 51,212), whilst insertion/deletion variants are found in lower numbers (N = 2,947) [2]
Summary
The DiscovEHR cohort is the result of the collaboration between Geisinger (GHS) and the Regeneron Genetics Center. It is comprised of samples of GHS patients, who consented to participate in the Geisinger MyCode Community Health initiative [1,2]. 18,852 genes in 50,726 DiscovEHR participants were sequenced at the Regeneron Genetics Center. The high-throughput sequencing data combined with deidentified longitudinal electronic health records (EHR) and other demographic details are used for genetic research purposes. The genetic data from DiscovEHR have been successfully used by GHS to detect causative variants associated with a variety of diseases, such as hereditary breast and ovarian cancer, familial hypercholesterolemia, Lynch syndrome, cardiomyopathy and many others that, once confirmed in a clinical laboratory, are returned to participants as part of clinical care [3,4]
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