Genomic information increases prediction accuracy of behavior traits of Labrador Retrievers used as guide dogs

TL;DR

This study shows that using genomic data improves the accuracy of predicting behavior traits in Labrador Retrievers used as guide dogs.

Contribution

The study demonstrates that genomic selection increases prediction accuracy for behavior traits in service dogs.

Findings

Genomic prediction accuracies ranged from 0.32 to 0.63, outperforming pedigree-based predictions.

Genomic estimates of heritability for behavior traits ranged from 0.08 to 0.21.

Including SNP genotype data improved selection accuracy for young dogs with high genetic merit.

Abstract

This study aimed to evaluate the accuracy of prediction of breeding values in a genomic selection program for behavior traits in a population of Labrador Retrievers used as guide dogs. Implementing genomic selection as a new tool in service dogs has the potential to increase genetic gain, improving the performance of populations. Additionally, genomic predictions may help service dog organizations in identifying training candidates with higher accuracy.

Phenotypes for 17 traits on 4,841 Labrador Retrievers collected from 2008 to 2019 from the International Working Dog Registry’s (IWDR) behavior checklist were analyzed. The Behavior Checklist (BCL) standardizes a scoring system for a dog’s reaction to a variety of environmental stimuli. Data are used to assess a dog’s behavior and suitability for training as well as genetic selection using a selection index of prioritized traits with estimated breeding values. Genomic data were available for 1076 individuals from whole genome sequences and reduced to 94 K SNPs. Variance components were estimated using AIREML. Genomic information was included under a single-step GBLUP approach. Accuracies were evaluated among a sample of the higher accuracy animals using the linear regression method. Genomic estimates of heritability ranged from 0.08 to 0.21. Accuracies were calculated with the LR method and ranged from 0.30 to 0.58 for pedigree information, with an average of 0.46. Accuracies of genomic predictions ranged from 0.32 to 0.63, with an average of 0.50, and were higher than pedigree predictions for all traits.

The gains in accuracy from inclusion of SNP genotype data show that genomic prediction using single-step GBLUP can improve selection by identifying the cohort of young dogs that have the highest genetic merit for the desired traits. Gains in validation accuracy were limited by the small number of genotyped animals and are expected to increase as more animals are genotyped.

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