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<p>Paratuberculosis, caused by <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> (MAP), is a chronic, incurable enteritis of ruminants in which late-onset clinical signs preclude timely intervention, making early genomic stratification a key control lever. We investigated the genetic architecture underlying the host antibody response to MAP — measured as anti-MAP ELISA serostatus — using genome-wide SNP data from 474 animals representing seven indigenous Turkish goat breeds sampled across 11 provinces (237 seropositive, 237 seronegative; estimated true seroprevalence 14.1%). Fourteen architecturally diverse machine learning frameworks — encompassing regularized regression, GBLUP, kernel machines, tree ensembles, deep neural networks, and a meta-ensemble — were evaluated under repeated stratified cross-validation using both discriminative and calibration metrics. Mutual information–based preselection served a dual function: reducing computational dimensionality while prioritizing markers with detectable phenotypic association. Six regularized and kernel-based models converged at a statistically indistinguishable discrimination ceiling (mean AUC ≈ 0.982; range 0.002), a convergence pattern consistent with an additive component captured by the available SNP data. Critically, whereas ROC-AUC varied only 1.14-fold across all models, Brier score varied 4.85-fold (0.046–0.223), with tree ensembles exhibiting systematic miscalibration despite competitive discrimination — indicating that predicted probabilities vary substantially across model architectures and cannot be reduced to discrimination metrics alone when informing breeding or biosecurity decisions. Post-hoc offset recalibration across realistic field seroprevalence conditions preserved tier-level performance rankings across the tested prevalence scenarios. These findings indicate that genome-wide SNP variation encodes sufficient signal to support accurate and calibrated classification of anti-MAP ELISA serostatus in indigenous goat populations, and provide a calibration-aware framework potentially extensible to other complex disease traits in livestock.</p>

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Calibration-aware genomic prediction of paratuberculosis serostatus in indigenous Turkish goats

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

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  • Yalçın Yaman,
  • Ahmet Eser,
  • Devran Coşkun,
  • Ramazan Aymaz,
  • Yiğit Emir Kişi,
  • Murat Keleş,
  • Serdar Yağcı,
  • Özgül Gülaydın,
  • Serkan Süleyman Şengül,
  • Kıvanç İrak,
  • Memiş Bolacalı

摘要

Paratuberculosis, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic, incurable enteritis of ruminants in which late-onset clinical signs preclude timely intervention, making early genomic stratification a key control lever. We investigated the genetic architecture underlying the host antibody response to MAP — measured as anti-MAP ELISA serostatus — using genome-wide SNP data from 474 animals representing seven indigenous Turkish goat breeds sampled across 11 provinces (237 seropositive, 237 seronegative; estimated true seroprevalence 14.1%). Fourteen architecturally diverse machine learning frameworks — encompassing regularized regression, GBLUP, kernel machines, tree ensembles, deep neural networks, and a meta-ensemble — were evaluated under repeated stratified cross-validation using both discriminative and calibration metrics. Mutual information–based preselection served a dual function: reducing computational dimensionality while prioritizing markers with detectable phenotypic association. Six regularized and kernel-based models converged at a statistically indistinguishable discrimination ceiling (mean AUC ≈ 0.982; range 0.002), a convergence pattern consistent with an additive component captured by the available SNP data. Critically, whereas ROC-AUC varied only 1.14-fold across all models, Brier score varied 4.85-fold (0.046–0.223), with tree ensembles exhibiting systematic miscalibration despite competitive discrimination — indicating that predicted probabilities vary substantially across model architectures and cannot be reduced to discrimination metrics alone when informing breeding or biosecurity decisions. Post-hoc offset recalibration across realistic field seroprevalence conditions preserved tier-level performance rankings across the tested prevalence scenarios. These findings indicate that genome-wide SNP variation encodes sufficient signal to support accurate and calibrated classification of anti-MAP ELISA serostatus in indigenous goat populations, and provide a calibration-aware framework potentially extensible to other complex disease traits in livestock.