Genomic prediction of traits related to canine hip dysplasia - PubMed (original) (raw)

Enrique Sánchez-Molano et al. Front Genet. 2015.

Abstract

Increased concern for the welfare of pedigree dogs has led to development of selection programs against inherited diseases. An example is canine hip dysplasia (CHD), which has a moderate heritability and a high prevalence in some large-sized breeds. To date, selection using phenotypes has led to only modest improvement, and alternative strategies such as genomic selection (GS) may prove more effective. The primary aims of this study were to compare the performance of pedigree- and genomic-based breeding against CHD in the UK Labrador retriever population and to evaluate the performance of different GS methods. A sample of 1179 Labrador Retrievers evaluated for CHD according to the UK scoring method (hip score, HS) was genotyped with the Illumina CanineHD BeadChip. Twelve functions of HS and its component traits were analyzed using different statistical methods (GBLUP, Bayes C and Single-Step methods), and results were compared with a pedigree-based approach (BLUP) using cross-validation. Genomic methods resulted in similar or higher accuracies than pedigree-based methods with training sets of 944 individuals for all but the untransformed HS, suggesting that GS is an effective strategy. GBLUP and Bayes C gave similar prediction accuracies for HS and related traits, indicating a polygenic architecture. This conclusion was also supported by the low accuracies obtained in additional GBLUP analyses performed using only the SNPs with highest test statistics, also indicating that marker-assisted selection (MAS) would not be as effective as GS. A Single-Step method that combines genomic and pedigree information also showed higher accuracy than GBLUP and Bayes C for the log-transformed HS, which is currently used for pedigree based evaluations in UK. In conclusion, GS is a promising alternative to pedigree-based selection against CHD, requiring more phenotypes with genomic data to improve further the accuracy of prediction.

Keywords: Labrador Retrievers; dogs; genomic selection; hip dysplasia.

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Figures

Figure 1

Figure 1

Impact of SNP density on prediction accuracy. GBLUP correlations (r) for THS for different numbers of SNP markers; markers were chosen at random (dashed line) or based on _p_-values from GWAS performed in the training populations (straight line). The number of analyzed SNPs (crosses) were 1% (1063), 10% (10,629), 20% (21,257), 50% (53,141), 75% (79,712), and 100% (106,282).

Figure 2

Figure 2

Comparison of correlations (r) for GWAS-markers. GBLUP was tested by estimating effects in the validation populations of the top SNPs identified by GWAS analysis performed in the training populations. Traits presented are (A) HS (circles) and THS (triangles). (B) NA_right (triangles), NA_left (circles), and NA_total (crosses). (C) SUB_right (triangles), SUB_left (circles), and SUB_total (crosses). (D) CrAE_right (triangles), CrAE_left (circles), and CrAE_total (crosses).

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