189 Single-Step Gblup Evaluation for Behavioral Traits in Labrador Retrievers Used as Guide Dogs

Abstract

Abstract The objective of this study was to evaluate the accuracy of prediction in a genomic selection program for behavior traits in a population of Labrador Retrievers used as service dogs. Phenotypic data were collected in 4841 Labrador Retrievers with ages ranging from 3 months to 2.5 years, for 17 phenotypes from the International Working Dog Registry behavior checklist. The behavior checklist is a document that standardizes a scoring system for the reaction of an individual dog to environmental stimulus. Such scores are used to assess behavior and suitability of a dog for training. Pedigree contained 23,593 animals with birth dates ranging from 1991 to 2019. Genomic data were available for 457 individuals and obtained by low-pass whole genome sequences and reduced to a 250K SNP chip. Breeding values were calculated using a single trait animal model that included the fixed effects of sex, year of birth, and a contemporary group that included month/year of behavior test and organization that hosted the test. Variance components were estimated using AIREML. Genomic information was included in the model under a single-step GBLUP (ssGBLUP) approach by substituting the pedigree numerator relationship matrix with a matrix that combined pedigree and genomic relationships. Additionally, the genomic relationship matrix was modified under a weighted ssGBLUP (wssGBLUP) approach that allowed SNPs to have different distributions. Accuracies were evaluated in a 5-fold cross-validation that simulated a forward-in-time prediction. Heritabilities were low to moderate on all traits and varied from 0.025 to 0.37. Prediction based solely on pedigree information averaged 0.49 and ranged from 0.34 to 0.69. ssGBLUP increased the average accuracy to 0.55 and ranged from 0.34 to 0.72. Genomic estimated breeding values were more accurate than those computed with pedigrees for most traits. Gains in accuracy were limited by the small number of genotyped animals and are expected to increase as more animals are genotyped. The differences seen between the ssGBLUP approach and the wssGBLUP were minimal, and accuracies decreased after the second iteration. Those results indicate that behavior traits in this population are likely highly polygenic and would not benefit from weighted approaches. However, interpretation may change, as the limitations of the current study are due to the small number of genotyped individuals. Better SNP weight estimates may occur with more animals enrolled in the program, and with that a better description of the genetic architecture of the traits. The gains in accuracy show that genomic selection can help with improvement by identifying which young dogs have the highest genetic merit for the desired traits and are the best choices to keep as replacement breeders. The use of ssGBLUP is adequate for this canine data where not all animals are genotyped, and its use is recommended in selection programs focused on service dogs.