Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment which is a potential environmental concern due to run-off, and sometimes only offers limited protection due to pathogen resistance for example (Chen et al., 2010). Genetic analyses require large sample sizes and hence disease phenotypes often need to be obtained from field data. Disease data from field studies is often binary, indicating whether an individual became infected or not following exposure to infectious pathogens. In genetic analyses of binary disease data, however, exposure is often considered as an environmental constant and thus potential variation in host infectivity is ignored. Host infectivity is the propensity of an infected individual to infect others. The lack of attention to genetic variation in infectivity stands in contrast to its important role in epidemiology. The theory of indirect genetic effects (IGE), also known as associative or social genetic effects, provides a promising framework to account for genetic variation in infectivity as it investigates heritable effects of an individual on the trait value of another individual. Chapter 2 examines to what extent genetic variance in infectivity/susceptibility is captured by a conventional model versus an IGE model. The results show that, unlike a conventional model, which does not capture the variation in infectivity when it is present in the data, a model which takes IGEs into account captures some, though not all, of the inherent genetic variation in infectivity. The results also show that genetic evaluations that incorporate variation in infectivity can increase response to selection and reduce future disease risk. However, the results of this study also reveal severe shortcomings in using the standard IGE model to estimate genetic variance in infectivity caused by ignoring dynamic aspects of disease transmission. Chapter 3 explores to what extent the standard IGE model could be adapted for use with binary infectious disease data taking account of dynamic properties within the remit of a conventional quantitative genetics mixed model framework and software. The effect of including disease dynamics in this way was assessed by comparing the accuracy, bias and impact for estimates obtained for simulated binary disease data with two such adjusted IGE models, with the Standard IGE model. In the first adjusted model, the Case model, it was assumed that only infected individuals have an indirect effect on their group mates. In the second adjusted IGE model, the Case-ordered model, it was assumed that only infected individuals exert an indirect effect on susceptible group mates only. The results show that taking the disease status of individuals into account, by using the Case model, considerably improves the bias, accuracy and impact of genetic infectivity estimates from binary disease data compared to the Standard IGE model. However, although heuristically one would assume that the Case-ordered model would provide the best estimates, as it takes the disease dynamics into account, in fact it provides the worst. Moreover, the results suggest that further improvements would be necessary in order to achieve sufficiently reliable infectivity estimates, and point to inadequacy of the statistical model. In order to derive an appropriate relationship between the observed binary disease trait and underlying susceptibility and infectivity, epidemiological theory was combined with quantitative genetics theory to expand the existing framework in Chapter 4. This involved the derivation of a genetic-epidemiological function which takes dynamic expression of susceptibility and infectivity into account. When used to predict the outcome of simulated data it proved to be a good fit for the probability of an individual to become infected given its own susceptibility and the infectivity of its group mates. Using the derived function it was demonstrated that the use of a linear IGE model would result in biased estimates of susceptibility and infectivity as observed in Chapters 2 & 3. Following the results of Chapter 4, the derived expression was used to develop a Markov Chain Monte Carlo (MCMC) algorithm in order to estimate breeding values in susceptibility and infectivity in Chapter 5. The MCMC algorithm was evaluated with simulated disease data. Prior to implementing this algorithm with real disease data an adequate experimental design must be determined. The results suggest that there is a trade-off for the ability to estimate susceptibility and infectivity with regards to group size; this is in line with findings for IGE models. A possible compromise would be to place relatives in both larger and smaller groups. The general discussion addresses such questions regarding experimental design and possible areas for improvement of the algorithm. In conclusion, the thesis advances and develops a novel approach to the analysis of binary infectious disease data, which makes it possible to capture genetic variation in both host susceptibility and infectivity. This approach has been refined to make those estimates increasingly accurate. These breeding values will provide novel opportunities for genome wide association studies and may lead to novel genetic disease control strategies tackling not only host resistance but also the ability to transmit infectious agents.

When an individual interacts socially with a conspecific, their behavioural phenotype is affected directly by their genotype (‘direct genetic effect’, DGE), but may also be affected indirectly by the genotype of the opposing individual (‘indirect genetic effect’, IGE). While there is no doubt that IGEs occur in various organisms and contexts, it is unknown how properties of the environment may influence the relative magnitude of DGEs, IGEs, and their covariance. To gain insight into this, I examined territorial interactions in Drosophila melanogaster. Due to their short generation time and relatively simple care requirements, D. melanogaster has been used extensively in quantitative genetic research. Using offspring from a half-sib breeding design, I constructed an arena for documenting multiple dyadic territoriality assays with two sizes of a food resource. With this apparatus, 618 territoriality contests between 1,236 individuals were recorded and scored for four key aggressive behaviours. The results revealed significant genetic variation in how opponent effects on focal individuals changed between environments (i.e., genetic variation in the plasticity of IGEs). In addition, changes in DGEs and IGEs between environments were strongly and positively correlated (i.e., there was a DGE × IGE × environment interaction), although confirmation of this result in further studies is warranted because it was non-significant (P = 0.10), likely due to large uncertainties arising in part from some small variance component estimates. As a high throughput system for quantify IGEs in territoriality in Drosophila, my approach holds promise but there are issues to resolve, including automating phenotyping behaviors in place of manual scoring to enable many more trials. Additionally, modifications to increase humidity during trials might result in increased expression of certain territorial behaviours.

The evolution of social behaviour has been studied using different frameworks based on game theory and quantitative genetics. While both approaches provide a conceptually clear explanation of evolution of social behaviour, both have been limited in their applicability to empirical systems, mainly due to difficulties in measuring model parameters. Here, I develop a new quantitative genetics approach to the study of the evolution of social behaviours based on indirect genetic effects (IGEs), which parameters can be readily determined by empirical studies. IGEs describe effects of an individual's genotype on phenotypes of social partners, which may indirectly affect their fitness. Unlike traditional quantitative genetics assuming a non-genetical, non-heritable environment, IGE models assume that part of the environment is social, provided by parents and other interacting partners, thus has a genetic basic and can be heritable. In this study I explore the effects of IGEs on the magnitude and range of phenotypic values in a focal individual. I show that social interactions may not only cause indirect genetic effects but can also modify direct genetic effects. I demonstrate that interactions can substantially alter group mean phenotype and variance. This may lead to scenarios in which between group phenotypic variation is much higher than within group variation despite similar underlying genetic properties of different groups. Further, I analyse how IGEs influence levels of selection and predictions about evolutionary trajectories. I show that IGEs can create selection pressure at the group level, leading to evolution of behaviours that would not evolve otherwise. Moreover, I demonstrate that IGEs may lead to differences in the direction of evolutionary response between genotypes and phenotypes. Building on these results, I show that IGE models can be translated to and are fully compatible with traditional kin and multilevel selection models. I express costs and benefits in IGE parameters and determine the conditions under which social interactions lead to the evolution of cooperative or harmful behaviours. Therefore, the model I propose combines the conceptual clarity of kin and multilevel selection models with the applicability of IGE models, which parameters can be empirically determined, facilitating the testing of model predictions. Finally, I show that the use of IGE models is strongly limited by the underlying assumption of linearity. I prove that the modelling of interaction dynamics leads to steady state solutions found by IGE models only under limited conditions. In this light, I discuss the relevance of results published previously and propose a solution of how this problem can be addressed.

A trait must genetically correlate with fitness in order to evolve, however, theory suggests that strong directional selection should erode additive genetic variance (Va) in fitness and limit future evolutionary potential. Sexual antagonism and temporal fluctuations in selection are mechanisms that could maintain Va in fitness. Maternal genetic effects could be an additional source of adaptive genetic variation. I used ‘animal models’ to examine a long-term population of red squirrels to determine 1) if either sexual antagonism or temporal fluctuations in selection were maintaining direct Va in fitness or 2) if maternal genetic effects were a source of indirect Va in fitness. While there were environmental trade-offs on juvenile survival, neither sexual antagonism nor temporal fluctuations in selection maintained Va in fitness. Maternal genetic effects on fitness were significant and provide the Va in fitness needed for rapid microevolution. This is the first instance of maternal genetic effects demonstrated as the only genetic variance available for microevolution. / Northern Scientific Training Program, the Arctic Institute of North America, American Society of Mammologists, Queen Elizabeth II Graduate Scholarship in Science and Technology, NSERC Discovery (to Andrew McAdam), NSF (to Andrew McAdam)

Complex phenotypes are traits which are influenced by many factors, and not just a single gene, as for classical Mendelian traits. The brain, and its resultant behaviour, gives us a large subset of complex phenotypes to examine. Variation in these traits is affected by a range of different influences, both genetic and environmental, including social interactions and the effects of parents. Systems-genetics provides us with a framework in which to examine these complex traits, seeking to connect genetic variants to the phenotypes they cause, through intermediate phenotypes, such as gene expression and protein levels. This approach has been developed to exploit and analyse massive data sets generated for example in genomics and transcriptomics. In the first half of this thesis, I combine genetic linkage data from the BXD recombinant inbred mouse panel with genome-wide association data from humans to identify novel candidate genes, and use online gene annotations and functional descriptions to support these candidates. Firstly, I discovered MGST3 as a novel regulator of hippocampus size, which may be linked to neurodegenerative disorders. Secondly, I identified that CMYA5, MCTP1, TNR and RXRG are associated with mouse anxiety-like phenotypes and human bipolar disorder, and provide evidence that MCTP1, TNR and RXRG may be acting via inter-cellular signalling in the striatum. The second half of this thesis uses different cross-fostering designs between genetically variable BXD lines and the genetically uniform C57BL/6J strain to identify indirect genetic effects and the loci underlying them. With this, I have found novel loci expressed in mothers that alter offspring behaviour, novel loci expressed in offspring affecting the level of maternal care, and novel loci expressed in offspring, which alter the behaviour of their nestmates, as well as the level of maternal care they receive. Further I provide evidence of co-adaptation between maternal and offspring genotypes, and a positive indirect genetic effect of offspring on their nestmates, supportive of a role for kin selection. Finally, I demonstrate that the BXD lines can be used to investigate genes with parent-of-origin dependent expression, which have an indirect genetic effect on maternal care. In conclusion, this thesis identifies a number of novel loci, and in some cases genes, associated with complex traits. Not only are these techniques applicable to other phenotypes and other questions, but the candidates I identify can now be examined further in vitro or in vivo.

A rich diversity of social behaviours exists in the animal kingdom, and these behaviours have evolved to perform a variety of adaptive functions. Social behaviours show variation both among and within species, however the mechanisms that give rise to this variation are not well understood. Using fruit flies (Drosophila melanogaster), my goal was to uncover the genetic and behavioural mechanisms that underpin natural variation in two different social behaviours: sociability and sexual aggression. First, I showed that sociability, which is the tendency of animals to engage in friendly activities together, is influenced by indirect genetic effects (IGEs), and that encounters among individuals drive these effects (Chapter 2). I then showed that sociability and social plasticity have low-moderate heritability (Chapter 3), and sociability is not correlated between the sexes or with activity. I then generated lineages of flies with high and low sociability using artificial selection (Chapter 4). The evolved lineages had significantly diverged sociability which was not associated with fitness measures or nearest-neighbor distances, but was negatively correlated with intrasexual aggression (Chapter 4). Finally, in sexual aggression, which I quantified as male forced copulation rate, I showed that evolved differences and differences due to social plasticity were both associated with the differential expression of many genes, but only a few of these genes were significant in both (Chapter 5). I also showed that these sets of genes are enriched in neuropeptide hormone and serotonin gene ontology categories, and that 4 of 7 chosen genes were validated for their effects on sexual aggression. Overall, this thesis sheds light on the complex mechanisms that underlie variation in these social behaviours, and it paves the way for future research to further elucidate some of these mechanisms, especially on the genetic basis of sociability using the evolved lineages I generated. / Thesis / Doctor of Philosophy (PhD) / Individual animals tend to vary in many traits including social behaviours. Using fruit flies, my goal was to understand what causes individuals to vary in two social behaviours: sociability and sexual aggression. I found that highly sociable flies tended to influence other flies to become more sociable due to a change in how much these flies interacted. I also found that individual differences in sociability are moderately heritable, and the genetic variation contributing to this is different between the sexes. Also, less sociable flies tended to be more aggressive than highly sociable flies. Finally, for sexual aggression, I showed that variation in a male’s success in forcibly mating with a female was associated with changes in the expression of hundreds of genes, but these changes were mostly unique for evolved versus environmentally induced variation. Future work will similarly look to identify genes involved with individual differences in sociability.