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Understanding how genetic variation affects organism phenotype and fitness is a fundamental question in biology with implications for biomedical research. Constrained genomic loci (i.e. loci under selective constraint) are depleted of variation due to negative selection and are inferred to be functionally important to organism fitness. Recent sequencing in human populations has allowed researchers to make contemporary estimates of selective constraint across the genome. These estimates provide a basis for identifying functionally important elements and interpreting the fitness consequences of mutations. Mouse models are critical to our understanding of mammalian biology and are used to experimentally assess the function of genomic elements and genetic variation in-vivo. However, how selective constraint shapes patterns of variation across the mouse genome remains relatively understudied. In this thesis, I identify and characterise constrained genes and genomic regions in mice using single nucleotide variation from sequencing in wild populations (Mus musculus and Mus spretus). I quantify how local sequence context affects substitution probabilities across the mouse genome in order to control for differences in mutability when estimating selective constraint. I show a nucleotide’s 7-mer context (i.e. the three bases flanking the nucleotide on each side) explains more variance in substitution rates than either the 3-mer or 5-mer contexts. I then use these estimates of sequence mutability to identify constrained genes and genomic regions. The results show that many highly constrained genes are essential for embryonic development, and that mouse gene constraint is positively correlated with pleiotropy (i.e. a greater degree of constraint is correlated with an increased number of phenotype associations). Importantly, I also observe that the most constrained genes and genomic regions are enriched for variant sites associated with human disease, suggesting pathogenicity may have been conserved between the lineages. I conclude by discussing how these results may help to interpret the functional importance of genomic elements, prioritise disease-associated loci for functional assessment, and their importance for facilitating more reliable inferences from mouse models.

Crocodilians are responsible for more attacks on people than any other large predator, which has important implications for human safety and crocodilian conservation. Understanding the drivers of crocodilian attacks on people could help minimise future attacks and inform conflict management. Crocodilian attacks follow a seasonal pattern for many species; however, there has been limited analyses of the relationship between fine-scale contemporaneous environmental conditions and atack occurrence. Here, we use methods from environmental niche modelling to explore the relationships between abiotic predictors and human attack occurrence at a daily temporal resolution for two species: the Nile crocodile (Crocodylus niloticus) in South Africa and Swaziland (renamed Eswatini), and the American alligator (Alligator mississippiensis) in Florida. Our results indicate that ambient daily temperature in the most important abiotic temporal predictor of attack occurrence for both species, with attack likelihood increasing sharply at temperatures above 18°C and peaking at 28°C. It is likely that this relationship is explained partially by human propensity to spend time in and around water in warmer weather, but also by the effect of temperature on crocodilian hunting behaviour and physiology, especially the ability to digest food. We discuss the potential of our findings to contribute to the management of crocodilians, with benefits for human safety and conservation, as well as the application of environmental niche modelling to analysing human conflict with other species, including ectotherms and endotherms.

Older adults tend to have slower response times (RTs) than younger adults on cognitive tasks. This makes the examination of domain-specific deficits in aging difficult, as differences between conditions in raw RTs (RT costs) typically increase with slower average RTs. Here, we examine the mapping between 2 established approaches to dealing with this confound in the literature. The first is to use transformed RT costs, with the z-score and proportional transforms both being commonly used. The second is to use mathematical models of choice RT behavior, such as the drift-diffusion model (Ratcliff, 1978). We simulated data for younger and older adults from the drift-diffusion model under 4 scenarios: (a) a domain specific deficit, (b) general slowing, (c) strategic slowing, and (d) a slowing of nondecision processes. In each scenario we varied the size of the difference between younger and older adults in the model parameters, and examined corresponding effect sizes and Type I error rates in the raw and transformed RT costs. The z-score transformation provided better control of Type I error rates than the raw or proportional costs, though did not fully control for differences in the general slowing and strategic slowing scenarios. We recommend that RT analyses are ideally supplemented by analyses of error rates where possible, as these may help to identify the presence of confounds. To facilitate this, it would be beneficial to include conditions that elicit below ceiling accuracy in tasks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).