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Livestock populations can have different genetic backgrounds and may vary in their capacity to respond to environmental changes. Our findings suggest that improved chicken breeds differ in growth performance and phenotypic plasticity (yield stability) when they are introduced into new tropical environments. Dual consideration of productivity and phenotypic plasticity gives opportunities to select or recommend genotypes with optimal performance and wider adaptability for smallholder farmers raising livestock in different agroecologies.

Livestock production systems of the developing world use indigenous breeds that locally adapted to specific agro-ecologies. Introducing commercial breeds usually results in lower productivity than expected, as a result of unfavourable genotype by environment interaction. It is difficult to predict of how these commercial breeds will perform in different conditions encountered in e.g. sub-Saharan Africa. Here, we present a novel methodology to model performance, by using growth data from different chicken breeds that were tested in Ethiopia. The suitability of these commercial breeds was tested by predicting the response of body weight as a function of the environment across Ethiopia. Phenotype distribution models were built using machine learning algorithms to make predictions of weight in the local environmental conditions based on the productivity for the breed. Based on the predicted body weight, breeds were assigned as being most suitable in a given agro-ecology or region. We identified the most important environmental variables that explained the variation in body weight across agro-ecologies for each of the breeds. Our results highlight the importance of acknowledging the role of environment in predicting productivity in scavenging chicken production systems. The use of phenotype distribution models in livestock breeding is recommended to develop breeds that will better fit in their intended production environment.

Social interactions among individuals are abundant, both in wild and in domestic populations. With social interactions, the genes of an individual may affect the trait values of other individuals, a phenomenon known as indirect genetic effects (IGEs). IGEs can be estimated using linear mixed models. Most IGE models assume that individuals interact equally to all group mates irrespective of relatedness. Kin selection theory, however, predicts that an individual will interact differently with family members versus non-family members. Here, we investigate kin- and sex-specific non-genetic social interactions in group-housed mink. Furthermore, we investigated whether systematic non-genetic interactions between kin or individuals of the same sex influence the estimates of genetic parameters. As a second objective, we clarify the relationship between estimates of the traditional IGE model and a family-based IGE model proposed in a previous study. Our results indicate that male siblings in mink show different non-genetic interactions than female siblings in mink and that this may impact the estimation of genetic parameters. Moreover, we have shown how estimates from a family-based IGE model can be translated to the ordinary direct–indirect model and vice versa. We find no evidence for genetic differences in interactions among related versus unrelated mink.

Alemu, SW(2015) Indirect Genetic effects for Group-housed Animals. Joint PhD thesis between Aarhus University, Denmark and Wageningen University, the Netherlands. Social interactions among individuals are common both in plants and animals. With social interactions, the trait value of an individual may be influenced by the genes of its interacting partners, a phenomenon known as indirect genetic effects (IGE). An IGE is heritable effect of an individual on trait values of another individual. A large body of literature has shown that social interactions can create addition heritable variation in both plants and animals, for both behavioural and production traits. When IGE are estimated it is usually assumed that an individual interacts equally with all its group mates, irrespective of genetic relatedness. This assumption may not be true in mixed groups of kin and non-kin, where an individual may interact systematically different with kin and non-kin. Current IGE models ignore such systematically different interactions between kin and non-kin. Thus, the main aim of this thesis was to develop and apply statistical methods to estimate IGE when interactions differ between kin and non-kin. Social interactions are important in mink that are kept in groups for the production of fur. Group housing of mink increases aggression behaviours, which is reflected by an increase in the number of bite marks on the pelts, and reduces the welfare of the animals. We estimated the genetic parameter for bite mark traits in group-housed mink, to investigate the prospects for genetic improvement of bite mark traits. We found that there are good prospects to produce mink that have a low level of biting. Finally, we further concluded that genetic parameter estimation for bite mark score should take into account systematic interactions due to sex or kin. In this thesis we also investigated genomic selection for socially affected traits, considering survival time in two lines of brown e

Background Since the recommendations on group housing of mink (Neovison vison) were adopted by the Council of Europe in 1999, it has become common in mink production in Europe. Group housing is advantageous from a production perspective, but can lead to aggression between animals and thus raises a welfare issue. Bite marks on the animals are an indicator of this aggressive behaviour and thus selection against frequency of bite marks should reduce aggression and improve animal welfare. Bite marks on one individual reflect the aggression of its group members, which means that the number of bite marks carried by one individual depends on the behaviour of other individuals and that it may have a genetic basis. Thus, for a successful breeding strategy it could be crucial to consider both direct (DGE) and indirect (IGE) genetic effects on this trait. However, to date no study has investigated the genetic basis of bite marks in mink. Result and discussion A model that included DGE and IGE fitted the data significantly better than a model with DGE only, and IGE contributed a substantial proportion of the heritable variation available for response to selection. In the model with IGE, the total heritable variation expressed as the proportion of phenotypic variance (T2) was six times greater than classical heritability (h2). For instance, for total bite marks, T2 was equal to 0.61, while h2 was equal to 0.10. The genetic correlation between direct and indirect effects ranged from 0.55 for neck bite marks to 0.99 for tail bite marks. This positive correlation suggests that mink have a tendency to fight in a reciprocal way (giving and receiving bites) and thus, a genotype that confers a tendency to bite other individuals can also cause its bearer to receive more bites. Conclusion Both direct and indirect genetic effects contribute to variation in number of bite marks in group-housed mink. Thus, a genetic selection design that includes both direct genetic and indirect genetic eff