Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach

Abstract

Background: 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 findings