Miranda Benjamin, Momen Mehdi, Sample Susannah J, Muir Peter
Comparative Orthopaedic & Genetics Research Laboratory, Department of Surgical Science, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States.
Front Vet Sci. 2025 Aug 26;12:1625953. doi: 10.3389/fvets.2025.1625953. eCollection 2025.
Canine cruciate ligament rupture (CR) is a common, complex, polygenic, orthopaedic disease in dogs that results in serious financial burden and patient morbidity even in the face of surgical correction. The goal of this study was to evaluate the clinical utility of CR polygenic risk score (PRS) prediction models using genome-wide SNP data from a large reference population of Labrador Retriever dogs.
Using 10-fold cross-validation and an independent validation population, we assessed Bayesian and machine learning models with and without covariates using both genome-wide SNPs as well as genic SNPs. Models were tuned by optimizing numbers of CR risk SNPs selected by genome-wide association and adjusting posterior probability thresholds to maximize prediction accuracy.
Models that included clinical covariates (sex, neuter status, age, weight, withers height, as well as the first 10 principal components from the genetic relationship matrix) universally yielded higher accuracy up to 88.5% compared to 77% without covariates. Prediction accuracy for some models was reduced when only genic SNPs were used suggesting SNPs in non-coding regions could influence the CR disease risk.
Our results confirm that PRS models provide sufficient predictive accuracy for clinical application in veterinary medicine and offer a viable, early-life screening tool for personalized care and selective breeding to reduce CR incidence in high-risk breeds. Our results further confirm that CR is a complex polygenic disease in which genome-wide risk SNPs influence disease pathogenesis.
犬类十字韧带断裂（CR）是犬类中一种常见、复杂的多基因骨科疾病，即使经过手术矫正，也会给患者带来严重的经济负担和发病风险。本研究的目的是利用来自拉布拉多猎犬大型参考群体的全基因组单核苷酸多态性（SNP）数据，评估CR多基因风险评分（PRS）预测模型的临床实用性。
使用10倍交叉验证和一个独立验证群体，我们评估了使用全基因组SNP以及基因SNP的有无协变量的贝叶斯和机器学习模型。通过优化全基因组关联选择的CR风险SNP数量并调整后验概率阈值来调整模型，以最大化预测准确性。
与无协变量的模型相比，包含临床协变量（性别、绝育状态、年龄、体重、肩高以及遗传关系矩阵的前10个主成分）的模型普遍产生更高的准确性，最高可达88.5%，而无协变量的模型为77%。当仅使用基因SNP时，一些模型的预测准确性降低，这表明非编码区域的SNP可能会影响CR疾病风险。
我们的结果证实，PRS模型为兽医学临床应用提供了足够的预测准确性，并为个性化护理和选择性育种提供了一种可行的早期筛查工具，以降低高危品种的CR发病率。我们的结果进一步证实，CR是一种复杂的多基因疾病，其中全基因组风险SNP影响疾病发病机制。
