Genomics-first association of pharmacogenomic risk phenotypes with adverse drug reactions in a healthcare-based population.

Genotype-predicted drug response phenotypes and their co-occurrence with dispensed medicines among 738,531 participants in the UK Our Future Health study

Pharmacogenomics (PGx) can improve safety and effectiveness of commonly dispensed medicines, but its value at the population level depends on how often clinically actionable PGx phenotypes co-occur with the medicines they affect. We assessed this co-occurrence in a cross-sectional analysis of Our Future Health (OFH), a new UK national biobank, by applying Pharmacogenomics Clinical Annotation Tool (PharmCAT v3.1.1) to imputed genotypes from 738,531 participants across 17 pharmacogenes with established PGx prescribing guidelines. Every participant had at least one actionable PGx phenotype, with a mean of 6.1 (SD 1.3). The number of actionable PGx phenotypes was similar across genetically inferred ancestry groups, although the pharmacogenes contributing to that count differed between groups. Using linked primary care dispensing records, 36.8% (95% CI 36.7-36.9) had been dispensed at least one medicine between April 2018 and June 2025 matched to a gene for which they carried an actionable PGx phenotype. Co-occurrence rose with age, ranging from 43.7% to 58.9% across ancestry groups among those aged [≥]70 years. Participants carried an actionable PGx phenotype for a mean of 13.8 (SD 6.5) of the 33 medicines dispensed in English primary care with PGx prescribing guidance, of which a mean of 0.6 (SD 1.0) had been dispensed. Co-occurrence was concentrated in a few widely dispensed classes, principally proton-pump inhibitors and antidepressants acting through CYP2C19 and statins through SLCO1B1. These findings highlight opportunities to optimise treatment for a large proportion of patients receiving routine medications and identify where pre-emptive PGx testing could have the greatest clinical benefit.

Pharmacogenomics of Adverse Drug Reactions: Integrating Genetic Biomarkers into Precision Pharmacotherapy

The integration of pharmacogenomics into routine healthcare has the potential to optimize individualized drug therapy, minimize preventable ADRs, and accelerate the transition toward precision medicine, ultimately improving clinical outcomes and healthcare quality.

Model-based clinical utility of pharmacogenetic testing and its potential population impact on the use of essential medicines in Zimbabwe

Introduction Pharmacogenomic-related adverse drug reactions and treatment failure contribute to global morbidity. Evidence for PGx-guided therapy in sub-Saharan Africa remains limited with few implementation studies. This study evaluated the potential clinical utility and projected population-level impact of pharmacogenomic testing in Zimbabwe. Methodology A cross-sectional analysis used the Zimbabwe Essential Medicines List, DPWG guidelines, Zimbabwean genotype/phenotype data, and locally approved Summary of Product Characteristics. For the actionable gene–drug pairs identified, the number needed to genotype values were calculated from DPWG-defined absolute risk reduction and local genotype-phenotype frequencies. Potential clinical utility was assessed using the Clinical Implication Score framework and compared with Dutch population data. Results Among 308 medicines screened, 30 (9.7%) contained actionable pharmacogenomic biomarkers, corresponding to 38 gene–drug pairs, all classified as vital or essential medicines. Lower NNG values were observed for UGT1A1–atazanavir (5), UGT1A1–irinotecan (9), CYP2B6–efavirenz (26), and CYP2C9–phenytoin (25), whereas 61% of gene–drug pairs had NNG values > 1,000 compared with 47% in the Dutch population. Overall, 81.6% of PGx recommendations were concordant across populations, while 21.1% were reclassified, primarily due to differences in local genetic profiles and the limited inclusion of PGx information in the Zimbabwean SmPCs, with 42% lacking PGx information compared with 24% in the Dutch dataset. Conclusion Many essential medicines in Zimbabwe have actionable pharmacogenomic biomarkers, but gaps in local genomic guidance remain. Strengthening local evidence generation and PGx implementation is essential.

Pharmacogenomic landscape in Thailand: Array-based profiling and EMR-linked medication exposure

Pharmacogenomic (PGx) data in Thailand remain limited, and genetics-only surveys rarely quantify “realized actionability”—the overlap between actionable PGx phenotypes and real-world medication exposure. We profiled 4,662 Thai adults using SNP-array data and a pre-specified PGx panel (11 genes; 26 markers) with a hybrid required/optional calling policy for diplotype/phenotype assignment. CPIC level A/B gene–drug relationships were linked to hospital electronic medical record (EMR) prescription/dispensation data to quantify drug-specific realized actionability. Overall callability across gene-results was 98.62%, exceeding 99% for most genes and lower for CYP2C19 (95.99%) and NUDT15 (90.28%). Across nine phenotype-coded genes, 95.99% carried ≥1 CPIC-actionable result (median 2; IQR 2–3). Actionable prevalence among callable individuals was highest for CYP3A5 (58.54%) and CYP2C19 (56.67%), followed by ABCG2 (45.10%) and UGT1A1 (27.37%). EMR linkage identified 1,529 (32.58%) participants exposed to ≥1 study medication; omeprazole (n = 658) and statins were most common (atorvastatin n = 606; simvastatin n = 603). Among users, actionable phenotypes were frequent for CYP2C19–omeprazole (55.02%) and SLCO1B1–statins (21.95–23.05%). In conclusion, an Asian-optimized SNP array supports scalable PGx phenotyping in Thai adults. EMR linkage quantifies realized actionability and highlights high-yield targets (CYP2C19–proton pump inhibitors; SLCO1B1–statins) for pre-emptive implementation.

Pharmacogenetics and Pharmacogenomics as Enabling Technologies for Precision Drug Development and Personalized Therapy.

Comparative evaluation of genotyping and low-pass sequencing for pharmacogenetic variant and phenotype inference

Background. Pharmacogenetic (PGx) testing can guide drug prescribing but remains limited by the genomic assay used. Genotyping arrays are widely implemented yet limited to predefined variants, whereas low-pass whole-genome sequencing (LP-WGS) is not constrained by fixed probe design and may provide broader PGx variant availability after imputation. Methods. We compared Illumina Global Screening Array (GSA) v3 with ~1x LP-WGS for PGx profiling in 500 hospital biobank participants with electronic health record evidence of exposure to pharmacogenetically actionable drugs and reported adverse drug reactions. Concordance was evaluated genome-wide, at 20 actionable pharmacogenes for PharmCAT-derived star alleles and metabolizer phenotypes, and for HLA alleles. Results. Genome-wide concordance between imputed array and LP-WGS data was high (median 99.63%; interquartile range, 99.59%-99.64%). For pharmacogenetically relevant variants, LP-WGS captured a larger fraction, particularly rare alleles absent from the array data, whilst maintaining high concordance at shared sites. Predicted phenotype concordance exceeded 98% for most genes, although gene-specific differences in phenotype classification were observed. LP-WGS reduced missing phenotype assignments for selected loci, particularly CYP2C19 and NAT2, by improving resolution of star-allele structure. However, in structurally complex or incompletely characterized genes such as CYP2C9 and CYP2D6, broader variant recovery increased indeterminate classifications rather than consistently improving clinical interpretability. For HLA loci, concordance varied by imputation strategy, with SNP2HLA performing marginally better utilizing the GSA array compared to the LP-WGS approach. Conclusions. Overall, LP-WGS provides broader variant coverage and improved resolution for selected pharmacogenes but did not resolve all clinically important loci. These findings support further evaluation of LP-WGS as a scalable PGx screening approach, especially where long-term genomic data reuse is a priority.