Towards precision medicine: interrogating the human genome to identify drug pathways associated with potentially functional, population-differentiated polymorphisms

Abstract

Drug response variations amongst different individuals/populations are influenced by several factors including allele frequency differences of single nucleotide polymorphisms (SNPs) that functionally affect drug-response genes. Here, we aim to identify drugs that potentially exhibit population differences in response using SNP data mining and analytics. Ninety-one pairwise-comparisons of >22,000,000 SNPs from the 1000 Genomes Project, across 14 different populations, were performed to identify ‘population-differentiated’ SNPs (pdSNPs). Potentially-functional pdSNPs (pf-pdSNPs) were then selected, mapped into genes, and integrated with drug–gene databases to identify ‘population-differentiated’ drugs enriched with genes carrying pf-pdSNPs. 1191 clinically-approved drugs were found to be significantly enriched (Z > 2.58) with genes carrying SNPs that were differentiated in one or more population-pair comparisons. Thirteen drugs were found to be enriched with such differentiated genes across all 91 population-pairs. Notably, 82% of drugs, which were previously reported in the literature to exhibit population differences in response were also found by this method to contain a significant enrichment of population specific differentiated SNPs. Furthermore, drugs with genetic testing labels, or those suspected to cause adverse reactions, contained a significantly larger number (P < 0.01) of population-pairs with enriched pf-pdSNPs compared with those without these labels. This pioneering effort at harnessing big-data pharmacogenomics to identify ‘population differentiated’ drugs could help to facilitate data-driven decision-making for a more personalized medicine.

PPARG (Pro12Ala) genetic variant and risk of T2DM: a systematic review and meta-analysis

“…SNPs are responsible for more than 80 percent of the variation between individuals which makes them ideal for genotype and phenotype association studies. Genetic association studies as powerful approach have identified several SNPs that are significantly associated with T2DM susceptibility 9 , 10 .…”

Rates of genetic testing in patients prescribed drugs with pharmacogenomic information in FDA-approved labeling

“…Implementing genetic testing as a means of avoiding ADRs thus has the potential to both enhance health outcomes and provide economic savings to healthcare delivery systems [5,7,15]. This initiative also fits well within the recent movement toward individualized and/or precision medicine wherein adaptations to standardized interventions may be facilitated by genetic analysis [16].…”

Robust Performance of Potentially Functional SNPs in Machine Learning Models for the Prediction of Atorvastatin-Induced Myalgia

“…Three separate groups of SNPs were used as inputs into the machine learning models. These were: 1) SNPs that were most highly associated with myalgia from our results, 2) SNPs residing in 128 genes in the atorvastatin pathway from the drug databases Drugbank, CHEMBL, CTD and PharmGKB as previously obtained by our group (Supplementary Table S1) (Bachtiar et al, 2019b), and 3) SNPs in nine genes reported to be associated with atorvastatin-induced myalgia from the literature (Supplementary Table S2) (Ruano et al, 2007;Ruano et al, 2011;Brunham et al, 2018). SNPs in these three groups were ranked by their p-value of association with myalgia from our univariate analysis, and the top 50 overall, pf and non-pf SNPs from these three groups were extracted and separately used for training the models.…”

“…For coding SNPs, functionality was determined based on whether the SNP resides within protein modification sites such as phosphorylation sites, within important protein domains/functional regions, or are predicted to affect exonic splice enhancer/silencer sites or nonsensemediated decay. Furthermore, within the coding region, synonymous mutations were assessed for significant codon usage bias as this could potentially influence the speed of the translation process (Kimchi-Sarfaty et al, 2007), while predicted deleteriousness was used for selecting non-synonymous pfSNPs (Bachtiar et al, 2019b). In addition to the pfSNP resource, expression quantitative trait loci (eQTLs) from the GTEx database (gtexportal.org) (Carithers et al, 2015), the eqtlGen consortium (eqtlgen.org) (Võsa et al, 2018) and the Jansen study (eqtl.onderzoek.io) (Jansen et al, 2017) were also used to identify potentially functional SNPs (pfSNPs).…”