Social interactions between individuals living in a group can have both positive and negative effects on welfare, productivity, and health of these individuals. Negative effects of social interactions in livestock are easier to observe than positive effects. For example, laying hens may develop feather pecking, which can cause mortality due to cannibalism, and pigs may develop tail biting or excessive aggression. Several studies have shown that social interactions affect the genetic variation in a trait. Genetic improvement of socially-affected traits, however, has proven to be difficult until relatively recently. The use of classical selection methods, like individual selection, may result in selection responses opposite to expected, because these methods neglect the effect of an individual on its group mates (social genetic effects). It has become clear that improvement of socially-affected traits requires selection methods that take into account not only the direct effect of an individual on its own phenotype but also the social genetic effects, also known as indirect genetic effects, of an individual on the phenotypes of its group mates. Here, we review the theoretical and empirical work on social genetic effects, with a focus on livestock. First, we present the theory of social genetic effects. Subsequently, we evaluate the evidence for social genetic effects in livestock and other species, by reviewing estimates of genetic parameters for direct and social genetic effects. Then we describe the results of different selection experiments. Finally, we discuss issues concerning the implementation of social genetic effects in livestock breeding programs. This review demonstrates that selection for socially-affected traits, using methods that target both the direct and social genetic effects, is a promising, but sometimes difficult to use in practice, tool to simultaneously improve production and welfare in livestock.

BackgroundIn many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method.ResultsIn total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB.ConclusionsTo the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.

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Increasing uniformity of traits is an important objective in livestock production. This study focused on the BWcomparison of a double hierarchical GLM (DHGLM) with the conventional analysis of uniformity, using within-litter variation in birth weight (BW0) in pigs as a case. In pigs, within-litter variation of BW0 is a trait in which uniformity is important in breeding practice. Traditionally, uniformity has been studied by analysis of SD or variances. In DHGLM, differences between animals are studied by analyzing the residual variance of the trait and estimating its variance components. Here we used data on BW0, recorded in 2 sow lines (Large White and Landrace), to compare the estimation of genetic parameters and breeding values for uniformity from DHGLM and traditional analysis of the variance. Comparison of DHGLM with the conventional analysis using the logarithm-transformed variance of BW0 was possible because both methods were on the same scale and the models contained the same random effects. In addition, the genetic CV at the residual SD level (GCV) was proposed as a measure expressing the potential response to selection. Three-fold cross-validation was performed to study predictive ability of both methods. The estimated GCV was highly similar using both methods. Results indicate that the SD of BW0 can be decreased by up to approximately 10% after 1 generation of selection, indicating good prospects for response to selection. The correlation between EBV (0.88 in both sow lines) obtained from both methods indicated high similarity between conventional analysis and DHGLM. Comparison of accuracies of EBV showed that the methods were comparable, with moderate accuracies achieved with approximately 100 piglets per maternal grandsire. Cross-validation also indicated very similar predictive ability in estimating EBV for BW0 variation for both methods. Therefore, it was concluded that conventional analysis and DHGLM produced highly comparable results. Still, the DHGLM potentially has a broader application than conventional analysis to study uniformity of traits, because it also can be used for traits with single observations per animal.

The global temperatures are increasing. This increase is partly due to methane (CH4) production from ruminants, including dairy cattle. Recent studies on dairy cattle have revealed the existence of a heritable variation in CH4 production that enables mitigation strategies based on selective breeding. We have exploited the available heritable variation to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Although the detected regions explained only a small proportion of the heritable variance, we showed that potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.