Breed effects on hip and elbow scores and their standard errors.

<p>A breed was required to have a minimum of 10 dogs recorded as “degenerative joint disease I or II, or III” to be included in the analysis, which led to only 21 breeds remaining for elbow scores evaluation. All the 74 breeds satisfied the requirement on hip scores.</p

Trends of inbreeding coefficients over 40 years for small- and large-population breeds.

<p>The small and large population breeds were separated by a threshold value of 50,000 dogs. Breeds with 50,000 or less dogs were defined as small population breeds, while breeds with more than 50,000 dogs were the large population breeds, which included the Labrador Retriever, Golden Retriever, German Shepherd Dog, and Rottweiler.</p

The estimated genetic parameters and their standard deviations (SD) under different models^*.

*<p>All the models are performed with original hip score and different transformations of the elbow score. The transformations include Log, square root (SQRT) and inverse (INV). In each transformation of elbow score, the hip score remains the status of original score. The model of Original (O) was performed on the original scores on both hip and elbow. BLM (O) means the bi-variate linear model with original hip and elbow conformation scores as observations; BLM (LOG) means the bi-variate linear model with original hip conformation scores and log transformations for elbow scores as observations; BLM (SQRT) means the bi-variate linear model with original hip scores and square root transformations for elbow scores as observations; BLM (INV) means the bi-variate linear model with original hip scores and inverse transformations for elbow scores as observations. is the additive genetic variance, is the variance of combination effects of test year and test month, is the residual variance, is the phenotypic variance, is the heritability, subscript 1 means the trait of hip joint conformation scores, subscript 2 means the trait of elbow joint conformation scores. is the additive genetic covariance between these two traits, is the residual covariance between them, is the additive correlation between them, is the residual correlation between them.</p

Distribution of hip and elbow scores released between 1974 and 2009.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076390#pone-0076390-g002" target="_blank">Figure 2A:</a> Only three categories of hip scores (Excellent, Good and Fair) were jointly released as Normal between 1974 and 1985 and separately reported after 1985. There was no release on other categories before 2000. Then, all categories were released and reported separately; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076390#pone-0076390-g002" target="_blank">Figure 2B:</a> Elbow I meant “Normal”, Elbow II, III, IV meant osteoarthritis (degenerative joint disease) level I, II and III respectively. Few elbow scores were released except for category “Normal” before 2000. The reports were heavily biased against reporting poor hip and elbow scores in the first 30 years.</p

Number of dogs with hip and elbow scores during 1974 to 2009.

<p>Red bar indicates the number for hip scores, and blue bar for elbow scores. There were 760,455 and 135,409 hip and elbow scores, respectively, in 74 dog breeds collected by the Orthopedic Foundation for Animals (OFA) during this period.</p

The annual genetic improvement in each breed over 40 years.

<p>The annual genetic improvement is represented by the average increments of estimated breeding value (EBV) per year and its standard errors. The blue bar indicates the genetic change for hip dysplasia on the vertical axis on the left, the red bar indicates the genetic change for elbow dysplasia on the vertical axis on the right. A breed was required to have a minimum of 10 dogs recorded as “degenerative joint disease I or II, or III” to be included in the analysis, which led to only 21 breeds remaining for elbow scores evaluation. All the 74 breeds satisfied the requirement on hip scores.</p

The means and standard deviations (SD) of hip and elbow scores. These statistics and number of scores (N) are characterized by sex and category of age in months.

<p>The means and standard deviations (SD) of hip and elbow scores. These statistics and number of scores (N) are characterized by sex and category of age in months.</p

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<p>Canine hip dysplasia, a debilitating orthopedic disorder that leads to osteoarthritis and cartilage degeneration, is common in several large-sized dog breeds and shows moderate heritability suggesting that selection can reduce prevalence. Estimating genomic breeding values require large reference populations, which are expensive to genotype for development of genomic prediction tools. Combining datasets from different countries could be an option to help build larger reference datasets without incurring extra genotyping costs. Our objective was to evaluate genomic prediction based on a combination of UK and US datasets of genotyped dogs with records of Norberg angle scores, related to canine hip dysplasia. Prediction accuracies using a single population were 0.179 and 0.290 for 1,179 and 242 UK and US Labrador Retrievers, respectively. Prediction accuracies changed to 0.189 and 0.260, with an increased bias of genomic breeding values when using a joint training set (biased upwards for the US population and downwards for the UK population). Our results show that in this study of canine hip dysplasia, little or no benefit was gained from using a joint training set as compared to using a single population as training set. We attribute this to differences in the genetic background of the two populations as well as the small sample size of the US dataset.</p

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<p>Canine hip dysplasia, a debilitating orthopedic disorder that leads to osteoarthritis and cartilage degeneration, is common in several large-sized dog breeds and shows moderate heritability suggesting that selection can reduce prevalence. Estimating genomic breeding values require large reference populations, which are expensive to genotype for development of genomic prediction tools. Combining datasets from different countries could be an option to help build larger reference datasets without incurring extra genotyping costs. Our objective was to evaluate genomic prediction based on a combination of UK and US datasets of genotyped dogs with records of Norberg angle scores, related to canine hip dysplasia. Prediction accuracies using a single population were 0.179 and 0.290 for 1,179 and 242 UK and US Labrador Retrievers, respectively. Prediction accuracies changed to 0.189 and 0.260, with an increased bias of genomic breeding values when using a joint training set (biased upwards for the US population and downwards for the UK population). Our results show that in this study of canine hip dysplasia, little or no benefit was gained from using a joint training set as compared to using a single population as training set. We attribute this to differences in the genetic background of the two populations as well as the small sample size of the US dataset.</p

A novel iterative mixed model to remap three complex orthopedic traits in dogs

<div><p>Hip dysplasia (HD), elbow dysplasia (ED), and rupture of the cranial (anterior) cruciate ligament (RCCL) are the most common complex orthopedic traits of dogs and all result in debilitating osteoarthritis. We reanalyzed previously reported data: the Norberg angle (a quantitative measure of HD) in 921 dogs, ED in 113 cases and 633 controls, and RCCL in 271 cases and 399 controls and their genotypes at ~185,000 single nucleotide polymorphisms. A novel fixed and random model with a circulating probability unification (FarmCPU) function, with marker-based principal components and a kinship matrix to correct for population stratification, was used. A Bonferroni correction at p<0.01 resulted in a P< 6.96 ×10⁻⁸. Six loci were identified; three for HD and three for RCCL. An associated locus at CFA28:34,369,342 for HD was described previously in the same dogs using a conventional mixed model. No loci were identified for RCCL in the previous report but the two loci for ED in the previous report did not reach genome-wide significance using the FarmCPU model. These results were supported by simulation which demonstrated that the FarmCPU held no power advantage over the linear mixed model for the ED sample but provided additional power for the HD and RCCL samples. Candidate genes for HD and RCCL are discussed. When using FarmCPU software, we recommend a resampling test, that a positive control be used to determine the optimum pseudo quantitative trait nucleotide-based covariate structure of the model, and a negative control be used consisting of permutation testing and the identical resampling test as for the non-permuted phenotypes.</p></div