Machine learning--driven discovery of host genetic factors for paratuberculosis in goats within the one health framework
Yazarlar (11)
Doç. Dr. Yalçın Yaman Siirt Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ahmet Eser Siirt Üniversitesi, Türkiye
Doç. Dr. Devran Coşkun Siirt Üniversitesi, Türkiye
Ramazan Aymaz
Sheep Breeding Research Institute, Türkiye
Sheep Breeding Research Institute, Türkiye
Yiğit Emir Kişi
Siirt Üniversitesi, Türkiye
Siirt Üniversitesi, Türkiye
Murat Keleş
Sheep Breeding Research Institute, Türkiye
Sheep Breeding Research Institute, Türkiye
Serdar Yağcı
Tarimsal Araştirmalar ve Politikalar Genel Müdürlüğü, Ankara, Türkiye
Tarimsal Araştirmalar ve Politikalar Genel Müdürlüğü, Ankara, Türkiye
Doç. Dr. Özgül Gülaydin Siirt Üniversitesi, Türkiye
Serkan Süleyman Şengül
Sheep Breeding Research Institute, Türkiye
Sheep Breeding Research Institute, Türkiye
Prof. Dr. Kıvanç İrak Siirt Üniversitesi, Türkiye
Prof. Dr. Memiş BOLACALI Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | BMC Veterinary Research (Q1) | ||
| Dergi ISSN | 1746-6148 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | |||
| Makale Dili | İngilizce | Basım Tarihi | 04-2026 |
| Cilt / Sayı / Sayfa | 22 / 1 / – | DOI | 10.1186/s12917-026-05430-x |
| Makale Linki | https://link.springer.com/article/10.1186/s12917-026-05430-x | ||
| UAK Araştırma Alanları |
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| Özet |
| 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 ... |
| Anahtar Kelimeler |
| Caprine genomics | Host genetic resistance | Johne’s disease | Machine learning–based GWAS | One Health | Paratuberculosis |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 3 |