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The detection, structure and uses of extended haplotype identity in population genetic data

In large-scale population genomic data sets, individual chromosomes are likely to share extended regions of haplotype identity with others in the sample. Patterns of local haplotype sharing can be highly informative about many processes including historical demography, selection and recombination. However, in outbred diploid populations, the identification of extended shared haplotypes is not straightforward, particularly in the presence of low levels of genotyping error. Here, we introduce a model-based method for accurately detecting extended haplotype sharing between sets of individuals from unphased data. We describe two implementations of the algorithm that can be applied to data sets consisting of thousands of samples. The method leads naturally to an approach for statistical haplotype estimation, which is shown to be comparable in accuracy to current methods. By applying the method to genome-wide SNP data from over 5,000 samples from the UK we show that the N50 maximal haplotype sharing between unrelated samples is typically 2cM, consistent with a population history of rapid exponential growth that started approx. 125 generations ago. In contrast, within two Greek population isolates of approx. 700 individuals the N50 for maximal haplotype sharing is 12.5cM, while for an unrelated Greek sample of the same size the N50 is 1.3cM. By assessing the size and geographical distribution of maximal haplotype sharing within and between all Greek cohorts, we make inference on the extent of isolatedness of each cohort and on recent migration. We additionally date recent coancestry to about 10 generations for the isolates and 90 generations for the unrelated sample, and finnally attempt to date the time of divergence between them.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:680409
Date January 2014
CreatorsXifara, Dionysia-Kiara
ContributorsMcVean, Gilean
PublisherUniversity of Oxford
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://ora.ox.ac.uk/objects/uuid:9fabc91a-dd07-4deb-b722-f6b9110b34fb

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