Vaquerizo Villar, FernandoÁlvarez Escudero, JuliánVeiras Del Rio, SoniaCarracedo Álvarez, ÁngelTamayo Gómez, Eduardo2026-01-222026-01-222025-12-15Vaquerizo-Villar, F., Hernandez-Beeftink, T., Heredia-Rodríguez, M., Gómez-Sánchez, E., Lorenzo-López, M., López-Herrero, R., Bardaji-Carrillo, M., Tamayo-Velasco, Á., Martín-Fernández, M., Sánchez-de-Prada, L., Álvarez-Escudero, J., Veiras, S., Baluja, A., Gonzalo-Benito, H., Martínez-Paz, P., García-Concejo, A., Fernández-Rodríguez, A., Jiménez-Sousa, M. A., Resino, S., … Tamayo, E. (2025). Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach. Frontiers in Medicine, 12, 1644800. https://doi.org/10.3389/fmed.2025.16448002296-858Xhttps://hdl.handle.net/10347/45355Background: Early detection of sepsis is essential for its successful management. Although genome-wide association studies (GWAS) have shown potential in identifying sepsis-related genetic variants, they often involve heterogeneous patient groups and use single-locus analysis methods. Here, we aim to identify new sepsis susceptibility loci in post-surgical patients using an explainable artificial intelligence (XAI) approach applied to GWAS data. Methods: GWAS was performed in 750 post-operative patients with sepsis and 3,500 population controls. We applied a novel XAI-based methodology to GWAS-derived single nucleotide polymorphisms (SNPs) to predict sepsis and prioritize new genetic variants associated with post-operative sepsis susceptibility. We also assessed functional and enrichment effects using empirical data from integrated software tools and datasets, with the top-ranked variants and associated genes. Results: Our XAI-GWAS approach showed a notable performance in predicting post-surgical sepsis and prioritized SNPs (such as rs17653532, rs1575081785, and rs74707084) with higher contribution to post-operative sepsis prediction. It also facilitated the discovery of post-operative sepsis risk loci with important functional implications related to gene expression regulation, DNA replication, cyclic nucleotide signaling, cell proliferation, and cardiac dysfunction. Conclusion: The combination of GWAS and XAI prioritized loci associated with post-operative sepsis susceptibility. The determination of key genes, such as PRIM2, SYNPR, and RBSN, through pre-operative blood tests could enhance risk stratification, enable early detection of post-operative sepsis, and guide targeted interventions to improve patient outcomes. Further research with additional and ethnically diverse cohorts comprising sepsis and non-sepsis patients undergoing major surgery is needed to validate these exploratory findingseng© 2025 Vaquerizo-Villar, Hernandez-Beeftink, Heredia-Rodríguez, Gómez-Sánchez, Lorenzo-López, López-Herrero, Bardaji-Carrillo, Tamayo-Velasco, Martín-Fernández, Sánchez-de-Prada, Álvarez-Escudero, Veiras, Baluja, Gonzalo-Benito, Martínez-Paz, García-Concejo, Fernández-Rodríguez, Jiménez-Sousa, Resino, Martínez-Campelo, Suárez-Pajés, Quintela, Cruz, Carracedo, Villar, Flores, Hornero and Tamayo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Explainable artificial intelligence (XAI)Genome-wide association study (GWAS)SepsisPersonalized medicineSurgical patients320102 Genética clínicajournal article10.3389/fmed.2025.1644800open access