Genome-wide Association And Genomic Prediction Dissecting Canine Hip Articular Disease

Canine hip dysplasia (CHD) is a common polygenic trait characterized by hip malformation that results in osteoarthritis (OA). Almost all breeds of dog are afflicted with the trait, the prevalence in some big frame breeds is over 50%. Canine hip dysplasia is a complex genetics disease, estimated heritability ranges between about 0.2 and 0.7.A total of 721 dogs, including both an association and linkage population, were genotyped. The association population included 8 pure breeds (Labrador retriever, Greyhounds, German shepherd, Newfoundland, Golden retriever, Rottweiler, Border Collie and Bernese Mountain Dog). The linkage population included Labrador retrievers, Greyhounds, and their crosses. Of these, 366 dogs were genotyped by Illumina Canine SNP20 chip with 22,000 single nucleotide polymorphism (SNP) loci and a targeted screen across 8 chromosomes with 3,324 SNPs was performed on 551 dogs (196 dogs were common to both sets). A compressed mixed linear model approach was used to perform an association study on this combined association and linkage population. The study identified 4 susceptibility genes associated with CHD (COL15A1, EVC, PTPRD and MAGP1) and 2 genes (PARD3B and REG3A) associated with hip OA.To establish a predictive method using whole genome genotyping for early intervention in canine hip dysplasia (CHD) risk management, for the prevention of the progression of secondary osteoarthritis (OA), and for selective breeding. Two sets of dogs (six breeds) were genotyped with dense SNPs covering the entire canine genome. The first set contained 359 dogs upon which a predictive formula for genomic breeding value (GBV) was derived by using their estimated breeding value (EBV) of the Norberg angle (a measure of CHD) and their genotypes. To investigate how well the formula would work for an individual dog with genotype only, a cross validation was performed by masking the EBV of one dog at a time. The genomic data and the EBV of the remaining dogs were used to predict the GBV for the single dog that was left out. The second set of dogs included 38 new Labrador retriever dogs, which had no pedigree relationship to the dogs in the first set. The cross validation showed a strong correlation (R2>0.7) between the EBV and the GBV. The independent validation showed a moderate correlation (R=0.5) between GBV for the Norberg angle and the observed Norberg angle (no EBV was available for the new 38 dogs). Sensitivity, specificity, positive and negative predictive values of the genomic data were all above 70%. Prediction of CHD from genomic data is feasible, and can be applied for risk management of CHD and early selection for genetic improvement to reduce the prevalence of CHD in breeding programs. The prediction can be implemented before maturity, at which age current radiographic screening programs are traditionally applied, and as soon as DNA is available.A reasonable accurate prediction could be achieved based on genetic markers covering the entire genome. The Bayesian method that calculates the effect for each marker, gave the best accuracy among the available statistical methods of genomic prediction. Due to its heavy computation demand, it is not feasible for analyzing dense markers resulted from next generation genotyping technology, e.g. genotyping for sequencing. In this study, we modified the GBLUP method through compressed kinship to optimize model fit. This new approach was called cBLUPg can keep the advantage on computing speed while increase the accuracy of prediction. In addition applied the new genomic prediction method to canine hip dysplasia, we validate it for four species (Human, Maize, Arabidopsis and Rice). The new method improve accuracy 5~15% than the Bayes B method and increased speed around hundred-folds.