The effects of natural selection on genome-wide patterns of genetic variation

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It is well known that mutations create differences between individuals, and therefore provide the raw material for natural selection and evolution. Depending on their effects on the host's well-being, mutations can be divided into three categories: (1) deleterious mutations, which are harmful to the fitness of their host; (2) advantageous mutations, which increase survival or fertility; (3) neutral mutations, which have little or no effect. A question that has been central to evolutionary genetics is the role of natural selection on these three types of mutations in shaping patterns of genetic variation within populations. In fact, this is one of the questions that have motivated major ongoing DNA sequencing efforts in humans (e.g., the 1000 Genomes Project) and a number of other organisms, such as the fruit fly Drosophila (e.g., the Drosophila Population Genomics Project) and the weedy plant Arabidopsis (e.g., the 1001 Genomes Project). The answer to the above question is fundamentally important for biologists who intend to use these large-scale datasets to decipher the genetic basis of phenotypic variation (e.g., disease susceptibility), to infer evolutionary history, and to identify mutations underlying key functional innovations that have helped the organism better adapt to the environment, because it underlies our understanding of the nature of genetic variation, which is critical for developing reliable methods to gather accurate results from the data. Unfortunately, despite being of both practical and theoretical significance, we still know rather little about the roles that negative selection against deleterious mutations, referred to as background selection or BGS, and positive selection on advantageous mutations, referred to as selective sweeps or SSW, play in controlling the genetic make-up of a population.A major stumbling block is the lack of suitable theoretical tools for predicting the effects of BGS on sequence variability. This has hampered the progress towards a better understanding of the nature of genetic variation, because multiple lines of evidence have suggested that most mutations (especially those in functional parts of the genome) are deleterious, but the consequences of such mutations for genetic variability in nearby genomic regions are not well understood due to the lack of theoretical tools. The first objective of this project is, therefore, to construct a set of BGS models that are not only biologically realistic and computationally efficient, but are also suitable for analysing large-scale datasets. This is made feasible by a BGS model I have recently published. I will improve on this by developing a set of extended models that incorporate several essential biological features that are missing in the original model. I will also develop theoretical tools for analysing sequence variability that incorporate both of the BGS and SSW processes. I will apply these new models to whole-genome sequence datasets, such as that for the house mouse Mus musculus castaneus, which is being generated by my collaborators at the University of Edinburgh. The goal is to understand the relative importance of BGS and SSW in controlling patterns of variation within populations, and to enhance the new methods in such a way that they can become a useful set of tools for analysing the new large-scale sequencing datasets that are currently being generated by researchers on a variety of different organisms.