摘要

Objectives: Androgenetic alopecia (AGA) remains a highly prevalent disorder in both sexes, with variable response to current therapies (minoxidil, finasteride, dutasteride). Our aim was to apply a 26-SNP pharmacogenetic panel to evaluate single-locus associations, SNP–SNP interactions, and functional pathways influencing treatment response, using real-world clinical data. Introduction: Pharmacogenetic stratification holds promise for increasing therapeutic predictability and adherence in AGA. Beyond single variant associations, multi-locus and pathway-level analyses may reveal converging mechanisms underlying variability in response, guiding more rational use of drug combinations and informing long-term treatment strategies. Materials / method: Clinical and genetic data were analysed using the SNPassoc R package, applying multiple inheritance models (codominant, dominant, recessive, log-additive) and adjusting for covariates including BMI, gender, and co-treatments. Single SNP associations were complemented by SNP–SNP interaction tests and bioinformatic enrichment with STRING-based functional discovery to highlight relevant biological networks. Analyses were stratified by treatment subgroups (minoxidil, finasteride, dutasteride). Results: Significant associations were confirmed for variants in SULT1A1 and PTGES2 with minoxidil efficacy (p<0.01), and for SRD5A1 and SRD5A2 with finasteride/dutasteride response (p<0.05). Interaction analysis revealed epistatic effects between prostaglandin and androgen pathway SNPs, amplifying predictive power beyond single markers. Enrichment analysis highlighted convergence in vasodilation, androgen metabolism, and inflammatory signalling. Patients with favourable multilocus profiles showed superior clinical improvement and higher probability of long-term response. Conclusion: A 26-SNP framework not only predicts individual drug response but also reveals functional networks relevant to therapy choice in AGA. Integrating single-locus, interaction, and pathway-level insights strengthens the clinical utility of genetic testing, supporting the design of more effective, combination-based treatment strategies. Further validation in larger cohorts is warranted to consolidate pharmacogenetic guidance in alopecia management.