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Androstenone and skatole accumulate in the fat of mature non- castrated male pigs and is detected as boar taint - an offensive odour affecting the smell and taste of cooked pork. Genetic variation exists for their concentrations and so selection may provide a more sustainable solution than castration for reducing taint in commercial pigs. Genetic progress is limited by the inability to measure taint prior to selection in young animals. The study objectives were to investigate the potential value of genomic selection methodology for predicting breeding values of androstenone and skatole within a commercial Danish Landrace population. Five hundred male pigs with high skatole (>0.3 μg/g) at slaughter were matched with low skatole male litter mates and measured for androstenone. Phenotypes for both traits were corrected for farm as a fixed effect and age as a covariate prior to genetic analysis. Genotyping was performed using the Illumina SNP60 porcine beadchip. After QC (call rate >0.95, MAF >0.01), 42, 916 SNPs and 938 individuals remained. A 5- fold cross- validation study (~751 training and ~187 validation animals per group) was carried out to compare the accuracy of GBLUP, Bayes A, Bayes SSVS and Bayesian Lasso to predict the unobserved phenotypes. Accuracy was calculated as the correlation between the estimated genomic breeding value and phenotype of validation animals. The range of accuracies obtained by different methods was narrow for androstenone, between 0.29 (Bayes SSVS) and 0.30 (Bayes A), but wider for skatole, between 0.21 (GBLUP) and 0.26 (Bayes SSVS). Relative accuracies corrected for h2, were 0.55- 0.56 and 0.77-0.96 for androstenone and skatole respectively, although these are biased by the selection of the cases and controls. Increasing the size of the training set should further improve genomic prediction, especially for androstenone. The results demonstrate that genomic selection has great potential for reducing boar taint in commercial pigs.

This study focused on haplotype effects and linkage disequilibrium (LD) for the K232A locus and the promoter VNTR in the DGAT1 gene. Analyses were carried out in three German Holstein Frisian populations (including 492, 305, and 518 animals) for milk yield, milk fat and protein yield, and milk fat and protein content. We found that effects of the promoter VNTR were not significant and explain only a small amount of the variation of the QTL on BTA14. Haplotype effects were less significant than the K232A locus by itself, but the haplotype containing the A allele of the K232A locus and allele 3 with five repeats of the promoter VNTR showed negative effects on protein content when paternally inherited, whereas the haplotype with the A allele and VNTR allele 2 (with six repeats) increased the protein content. Significant differences between these two haplotypes occurred for protein yield as well, pointing to a linked effect that is picked up by the haplotypes rather than a direct effect of the VNTR. The linkage disequilibrium, estimated by D', showed values between 0.29 and 0.59 which is unexpectedly low for a distance of similar to 10kb. Only a very low correlation between the two loci was observed due to the almost similar frequencies of haplotypes containing the A or K allele of the K232A locus.

As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis

A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed

International audience; Abstract – Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from 10 countries performed and discussed the statistical analysesof real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays from a direct comparison of two treatments (dye-balanced). While there was broader agreement with regards to methods of microarray normalisation and significance testing, there were major differences with regards to quality control.The quality control approaches varied from none, through using statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful in facilitating interaction between scientists with a diverse background but a common interest in microarray analyses.