Machine learning-driven discovery of host genetic factors for paratuberculosis in goats within the one health framework

Abstract

Paratuberculosis, caused by Mycobacterium avium subsp. paratuberculosis (MAP), remains a persistent One Health concern due to its slow clinical course, environmental resilience, wide circulation in ruminant systems, and unresolved zoonotic implications. To characterise MAP exposure across T & uuml;rkiye's goat populations, we conducted a nationwide genomic survey encompassing seven breeds from 36 farms in 11 provinces. High-density SNP genotyping combined with mutual-information-based feature preselection retained informative, non-redundant loci capturing both linear and nonlinear components of disease architecture. Nine complementary machine-learning models were applied to identify host genetic factors underlying MAP infection, and an ensemble importance framework resolved 31 FDR-controlled SNPs consistently associated with MAP status. Functional annotation implicated immune processes including cytokine-receptor signalling, antigen presentation, glycan-mediated T-cell regulation, and NF-kappa B-linked inflammation. Concordance with mixed linear models and genome-wide McNemar tests suggested that both additive and non-additive genetic effects shape the observed signal. These reproducible, albeit preliminary, markers outline a genomic foundation for breeding MAP-resilient goats and point to opportunities for reducing pathogen shedding at its source within a broader One Health strategy.