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Open access Aug 2026

Side effects in hypertension treatment: a pharmacogenomic analysis.

BACKGROUND AND AIMS Up to half of patients switch or discontinue antihypertensive medications within the first year, but underlying mechanisms remain elusive. This study aimed to identify genetic predictors of antihypertensive medication use trajectories within the first year. METHODS Using longitudinal medication data from >400 000 genotyped antihypertensive medication users across three cohorts (FinnGen, the UK Biobank, and the Estonian Biobank), short-term antihypertensive medication use trajectories were classified as Continue, Switch, or Discontinue. Genome-wide association studies were performed across five medication classes. RESULTS In total, 14 genome-wide significant loci were identified for switching from angiotensin-converting enzyme inhibitors (ACEI) and dihydropyridine calcium channel blockers (dCCB) to other antihypertensive medications. For ACEI switching, evidence converged on the neurotensin-NTSR1 pathway, including a 320-fold Finnish-enriched protective missense variant in the neurotensin receptor gene NTSR1 (rs148569146 [G301R], odds ratio [OR] 0.49, P = 3.3 × 10-43) and a variant near RASSF9 (rs181941187, OR = 0.74, P = 1.2 × 10-49) tagging the neurotensin gene NTS. In drug-gene interaction analyses, NTSR1 G301R was associated with reduced ACEI-induced cough risk (OR 0.39, P = 8.1 × 10-4). The dCCB switching locus at CYP3A43 was in near-complete linkage (r2 = 0.99) with the functional CYP3A4*22 allele (rs35599367, OR 1.23, P = 6.1 × 10-10). A polygenic risk score (PRS) for ACEI switching predicted two-fold ACEI cough risk in the top 10% PRS compared with the middle 20% in an independent sample of the Estonian Biobank. CONCLUSIONS These findings extend the bradykinin hypothesis of ACEI-induced cough by implicating neurotensin-NTSR1 signalling, pinpoint CYP3A4*22 as a novel functional predictor of dCCB switching with potential for genotype-guided prescribing, and validate medication use trajectories as a framework for pharmacogenetic discovery.

F. Vaura, Kristi Krebs, T. Kiiskinen et al. · 0 citations
Open access Aug 2026

Detecting CYP2C19 deletions from genotyping array signals using neural networks

This work developed a neural network model, nnCNV, to predict deletions in the CYP2C19 pharmacogene region from array intensity signals and demonstrated that long-range information, which cannot be utilized by hidden Markov models, can improve CNV calling.

Burak Yelmen, R. Hofmeister, Viido Kaur Lutsar et al. · 0 citations

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