Wider implications

Studying the genomics of behaviour in farmed fish may have important applications for breeding programs and welfare. For example, by investigating selection lines (Chapter 3) it may be possible to identify genetic markers with which to aid marker-assisted selection programs (Yeo et al. 2000; Avila et al. 2005). Furthermore, novel candidates identified using gene transcription of stress responsiveness and aggressive behaviour may act as biomarkers to detect the effects of stress and aggression produced by aquaculture procedures, such as overcrowding.

Heterozygosity is correlated with many fitness-related traits, such as survival (Coulson et al. 1998; Silva et al. 2009), reproductive success (Olano-Marin et al. 2011; Wetzel et al. 2012), disease resistance (Acevedo-Whitehouse et al. 2005; Rijks et al. 2008) and growth rate (Pogson & Fevolden 1998; Bierne et al. 2000). Moreover, the expression of a number of important behavioural traits, such as aggression (Charpentier et al. 2008) and territoriality (Lieutenant-Gosselin & Bernatchez 2006), is associated with heterozygosity. Heterozygosity is often related to fitness traits due a reduction in the expression of deleterious recessive alleles (dominance) or heterozygote advantage (overdominance) (Slate et al. 2004). When fitness-related traits are correlated with heterozygosity, this relationship may be caused by two effects when neutral markers are used (Hansson & Westerberg 2002). The first is the local effect hypothesis, where neutral loci are in linkage disequilibrium (LD) with one or more fitness genes. The second is the general effect hypothesis, where the level of heterozygosity across a large set of neutral markers is generally correlated across loci within an individual’s genome (ID), where the heterozygosity at neutral markers is thought to represent genome-wide heterozygosity due to inbreeding (Weir & Cockerham 1973; Szulkin et al. 2010). When this occurs, individuals may show fitness across many phenotypic traits. Heterozygosity at many loci can affect immunocompetence, growth and survival (reviewed in (Kempenaers 2007)) and these traits may enable an individual to increase its competitive ability. This may allow an individual to increase its fitness in terms of survival and reproductive success through gaining food or mates.

In Chapter 2, aggressive rainbow trout were more heterozygous than less aggressive trout, which was most likely due to general effects. This may indicate that individuals exhibit a number of fitness-enhancing traits. In Chapter 4, the transcriptomes of a pool of five aggressive individuals from Chapter 2 were characterised in comparison with less aggressive individuals. Aggressive and less aggressive individuals expressed expressed different genes associated with energy metabolism: aggressive trout expressed genes with antioxidant effects and less aggressive trout expressed genes associated with increased respiration. Whilst the relationship between heterozygosity and gene expression was not explicitly investigated here, there is a possibility that the differential expression of genes may be associated with genome-wide heterozygosity. This may be due to a high number of loci with dominant or overdominant effects, whereby beneficial alleles are expressed more in heterozygous individuals. Individuals that are more efficient at storing or mobilising energy may be better able to compete and thus win territories, food or mates. These results may reveal some of the mechanisms behind the fitness-related trait aggression. However, genes that are differentially expressed between aggressive and less aggressive trout should be genotyped at the corresponding loci to determine heterozygosity and thus discover whether heterozygosity influences the expression of aggressive behaviour.

7.5 Conclusions

These studies aimed to assess the genomic complexity of the evolutionarily important behavioural traits, stress responsiveness, aggression and boldness, including the genomic links between behaviours, so as to provide empirical evidence for underlying mechanisms of behavioural syndromes. In addition, this study aimed to identify candidate genes associated with stress and aggression using novel genomic techniques. The results showed that genetic diversity was linked with aggression but not stress responsiveness or boldness (Chapter 2). Moreover, genome-wide heterozygosity, rather than heterozygosity at single loci, appeared to be associated with aggressiveness. Similarly, genome regions potentially associated with stress responsiveness were located across the genome (Chapter 3). I also showed that genomic control of behaviour was complex, where many genes were associated with aggressive behaviour and these may have pleiotropic or epistatic effects (Chapter 4). Pleiotropic effects may be present in some genes, (e.g. V1a: Chapters 5 & 6; or POMC: Chapter 4), but not others, (e.g. EPD: Chapters 5 & 6). Moreover, I showed that novel applications of techniques can yield novel candidates for behavioural investigations, where I identified genome regions that are potentially associated with stress responsiveness (Chapter 3) and candidate genes associated with aggression using a transcriptome, including unidentified sequences (Chapter 4). These results demonstrated that behavioural ecology can shift from the study or one or a few candidate genes and towards a network view of

genomics, where many genes and their interactions control complex behaviours. Moreover, this idea of multiple genes can be applied to single behavioural traits and to behavioural syndromes, where pleiotropy may be restricted to the effects a few genes, which are regulated by the interactions of many genes. To further the study of behavioural genomics, the impact of environmental conditions and previous experience to investigate the non- genomic control should be considered. Epigenetic or maternal effects may influence intraspecific behaviour and studies are beginning to explore indirect genetic effects. Moreover, there may be regulatory systems and pathways that interact both at the genetic and environmental level, which may be studied with the use of next generational genomic tools.