Efficiency of genomic prediction for boar taint reduction in Danish Landrace pigs

Abstract

Genetic selection against boar taint, which is caused by high skatole and androstenone concentrations in fat, is a more acceptable alternative than is the current practice of castration. Genomic predictors offer an opportunity to overcome the limitations of such selection caused by the phenotype being expressed only in males at slaughter, and this study evaluated different approaches to obtain such predictors. Samples from 1000 pigs were included in a design which was dominated by 421 sib pairs, each pair having one animal with high and one with low skatole concentration (≥0.3 μg/g). All samples were measured for both skatole and androstenone and genotyped using the Illumina SNP60 porcine BeadChip for 62 153 single nucleotide polymorphisms. The accuracy of predicting phenotypes was assessed by cross-validation using six different genomic evaluation methods: genomic best linear unbiased prediction (GBLUP) and five Bayesian regression methods. In addition, this was compared to the accuracy of predictions using only QTL that showed genome-wide significance. The range of accuracies obtained by different prediction methods was narrow for androstenone, between 0.29 (Bayes Lasso) and 0.31 (Bayes B), and wider for skatole, between 0.21 (GBLUP) and 0.26 (Bayes SSVS). Relative accuracies, corrected for h(2) , were 0.54-0.56 and 0.75-0.94 for androstenone and skatole respectively. The whole-genome evaluation methods gave greater accuracy than using only the QTL detected in the data. The results demonstrate that GBLUP for androstenone is the simplest genomic technology to implement and was also close to the most accurate method. More specialised models may be preferable for skatole.

Keywords: Bayes; androstenone; genomic best linear unbiased prediction; genomic selection; skatole.

Figure 1

A comparison of estimated SNP effects, defined as the average value over realisations, obtained for five Bayesian regression methods. The upper plots correspond to skatole and the lower plots correspond to androstenone, both measured as μg/g fat tissue. Coordinate length for both x and y axes ranges from −0.03 to 0.03.

Figure 2

Scatterplot of the first two principal components (PC1 vs. PC2) on the GEBV for androstenone concentrations between all the methods. Each point represents a different method as follows: □ GBLUP, ■ Bayes A, ○ Bayes B, ● Bayes C, Δ Bayes SSVS, ▲ Bayesian Lasso.

Figure 3

Scatterplot of the first two principal components (PC1 vs. PC2) on the genomic estimated breeding values for skatole concentrations amongst all the methods. Each point represents a different method as follows: □ GBLUP, ■ Bayes A, ○ Bayes B, ● Bayes C, Δ Bayes SSVS, ▲ Bayesian Lasso.