Spatial structure in a population creates distinctive patterns in genetic data. There are two reasons to model this process. First, since the genetic structure of a population is induced by its historical spatial structure, it can be used to make inference about history and demography. Second, these models provide corrections to other analyses that are confounded by spatial structure. Since is it is now common to collect genome-wide data on many thousands of samples, a major challenge is to develop fast, scalable, approximate algorithms that can analyse these datasets. A practical approach is to focus on subsets of the data that are most informative, for example rare variants. First we look at the problem of estimating selection coefficients in spatially structured populations. We demonstrate this approach using classical datasets of moth colour morph frequencies, and then use it in a model incorporating both ancient and modern DNA to estimate the selective advantage of one of the best known examples of local adaptation in humans, lactase persistence in Europeans. Next, we turn to the problem of association studies in spatially structured populations. We demonstrate that rare variants are more confounded by non-genetic risk than common variants. Excess confounding is a consequence of the fact that rare variants are highly in- formative about recent ancestry and therefore, in a spatially explicit model, about location. Finally, we use this insight into rare variants to develop methods for inference about population history using rare variant and haplotype sharing as simple summary statistics. These approaches are extremely fast and can be applied to genome-wide data on thousands of samples, yet they provide an accurate description of the history of a population, both identifying recent ancestry and estimating migration rates between subpopulations.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:606224 |
| Date | January 2013 |
| Creators | Mathieson, Iain |
| Contributors | McVean, Gilean ; Lindgren, Cecilia |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:85f051b6-2121-49cf-9468-3ca7ba77cc4a |
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