Canine hip dysplasia (CHD) and rupture of the cranial cruciate ligament (RCCL) are two complex inherited orthopedic traits of dogs. These two traits may occur concurrently in the same dog. Genomic prediction of these two diseases would benefit veterinary medicine, the dog’s owner, and dog breeders because of their high prevalence, and because both traits result in painful debilitating osteoarthritis in affected joints. In this study, 842 unique dogs from 6 breeds with hip and stifle phenotypes were genotyped on a customized Illumina high density 183 k single nucleotide polymorphism (SNP) array and also analyzed using an imputed dataset of 20,487,155 SNPs. To implement genomic prediction, two different statistical methods were employed: Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian method called BayesC. The cross-validation results showed that the two methods gave similar prediction accuracy (r = 0.3–0.4) for CHD (measured as Norberg angle) and RCCL in the multi-breed population. For CHD, the average correlation of the AUC was 0.71 (BayesC) and 0.70 (GBLUP), which is a medium level of prediction accuracy and consistent with Pearson correlation results. For RCCL, the correlation of the AUC was slightly higher. The prediction accuracy of GBLUP from the imputed genotype data was similar to the accuracy from DNA array data. We demonstrated that the genomic prediction of CHD and RCCL with DNA array genotype data is feasible in a multiple breed population if there is a genetic connection, such as breed, between the reference population and the validation population. Albeit these traits have heritability of about one-third, higher accuracy is needed to implement in a natural population and predicting a complex phenotype will require much larger number of dogs within a breed and across breeds. It is possible that with higher accuracy, genomic prediction of these orthopedic traits could be implemented in a clinical setting for early diagnosis and treatment, and the selection of dogs for breeding. These results need continuous improvement in model prediction through ongoing genotyping and data sharing. When genomic prediction indicates that a dog is susceptible to one of these orthopedic traits, it should be accompanied by clinical and radiographic screening at an acceptable age with appropriate follow-up.

犬髋关节发育不良 (CHD) 和颅交叉韧带 (RCCL) 断裂是犬的两种复杂的遗传性骨科特征。这两种特征可能同时发生在同一只狗身上。这两种疾病的基因组预测将有利于兽医、狗的主人和狗饲养者，因为它们的流行率很高，并且因为这两种特征都会导致受累关节疼痛性衰弱的骨关节炎。在这项研究中，来自 6 个品种的具有髋关节和膝关节表型的 842 只独特狗在定制的 Illumina 高密度 183 k 单核苷酸多态性 (SNP) 阵列上进行基因分型，并使用 20,487,155 个 SNP 的估算数据集进行分析。为了实现基因组预测，采用了两种不同的统计方法：基因组最佳线性无偏预测 (GBLUP) 和称为 BayesC 的贝叶斯方法。r= 0.3–0.4) 对于多品种种群中的 CHD（以 Norberg 角测量）和 RCCL。对于 CHD，AUC 的平均相关性为 0.71 (BayesC) 和 0.70 (GBLUP)，属于中等水平的预测准确度，与 Pearson 相关性结果一致。对于 RCCL，AUC 的相关性略高。来自估算基因型数据的 GBLUP 预测准确性与来自 DNA 阵列数据的准确性相似。我们证明，如果参考种群和验证种群之间存在遗传联系（例如品种），则使用 DNA 阵列基因型数据对 CHD 和 RCCL 进行基因组预测在多品种种群中是可行的。尽管这些性状的遗传率约为三分之一，在自然种群中实施需要更高的准确性，并且预测复杂的表型将需要在一个品种内和跨品种的狗数量更多。这些骨科特征的基因组预测有可能以更高的准确性在临床环境中实施，以进行早期诊断和治疗，以及选择用于繁殖的狗。这些结果需要通过持续的基因分型和数据共享来持续改进模型预测。当基因组预测表明狗易受这些骨科特征之一的影响时，应在可接受的年龄进行临床和放射学筛查，并进行适当的随访。这些骨科特征的基因组预测可以在临床环境中实施，用于早期诊断和治疗，以及选择用于繁殖的狗。这些结果需要通过持续的基因分型和数据共享来持续改进模型预测。当基因组预测表明狗易受这些骨科特征之一的影响时，应在可接受的年龄进行临床和放射学筛查，并进行适当的随访。这些骨科特征的基因组预测可以在临床环境中实施，用于早期诊断和治疗，以及选择用于繁殖的狗。这些结果需要通过持续的基因分型和数据共享来持续改进模型预测。当基因组预测表明狗易受这些骨科特征之一的影响时，应在可接受的年龄进行临床和放射学筛查，并进行适当的随访。