Machine Learning–Driven Discovery of Host Genetic Factors for Paratuberculosis in Goats Within the One Health Framework

Machine Learning–Driven Discovery of Host Genetic Factors for Paratuberculosis in Goats Within the One Health Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Machine Learning–Driven Discovery of Host Genetic Factors for Paratuberculosis in Goats Within the One Health Framework Yalçın Yaman, Ahmet ESER, Devran Coşkun, Ramazan Aymaz, Yiğit Emir Kişi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8537890/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in BMC Veterinary Research → Version 1 posted 10 You are reading this latest preprint version 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 Türkiye’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-κB-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. Paratuberculosis Johne’s disease Machine learning–based GWAS Host genetic resistance Caprine genomics One Health Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2026 Read the published version in BMC Veterinary Research → Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor invited by journal 16 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8537890","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582314435,"identity":"a496477a-c101-4a84-97b3-6a6ce5c8cd6e","order_by":0,"name":"Yalçın 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