The [attached] article is a bombshell report and, to our knowledge, represents the first study of its kind. Rather than a genome-wide association study (GWAS), authors performed an avante gard data-mining in silico approach — to search for DNA variants in or near each of the 108 G-protein-coupled receptor genes (GPCRs) known to exist in the human genome. In the field of pharmacology and drug response, these 108 genes are the known targets of 475 prescription drugs that have been approved by the U.S. Food and Drug Administration (FDA). These 475 drugs, which comprise ~34% of all prescription drugs, account for a global sales volume of >US$180 billion annually..!!
Each of the genomes of almost 68,500 individuals was separately investigated for missense variants in and near each of the GPCR genes. Then the authors searched the literature for the clinical associations with altered drug response in these individuals. To estimate the de novo missense mutation rate within these GPCR genes, authors in addition identified novel mutations from >1,700 control trios (having no reported pathological conditions) –– which were compiled from ten different studies registered in the “denovo-database,” an intriguing collection of germline de novo variants (http://denovo-db.gs.washington.edu/denovo-db/).
To demonstrate proof-of-principle, authors then experimentally showed that certain variants of the mu-opioid and cholecystokinin receptors resulted in altered drug responses and/or idiosyncratic dose-independent adverse drug reactions. These amazing results — on just two of the 108 GPCR genes — underscore the need to characterize DNA variants among all 108 of the GPCR genes. Authors suggest that “the ultimate results of this novel type of in silico study might enhance prescription precision, improve patients’ quality-of-life, and remove some of the economic and societal burden caused by variability in drug response.”
We anticipate that such “dry-lab” data-mining studies, i.e. just sitting in front of a computer and searching databases online — such as this landmark publication [attached] — are likely to become a major new way to approach pharmacogenomics research in the near future..!! J
Cell Jan 2o18; 172: 41–54