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Joint imputation and estimation of haplotype transition probabilities

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If you have a question about this talk, please contact David Knowles .

I’ll show that the Product of Approximate Conditions model as used for imputating missing SNPs in lower density SNP arrays from higher density SNP arrays can be improved by using Expectation Propagation to learn the haplotype transition probabilities. This increases imputation performance and allows imputation without a genetic map. I’ll extend this model to the case where the imputation is based upon multiple reference populations. I’ll also talk about various ways to capture more of population structure, deal with issues such as phasing, increase the amount of evolutionary history captured in the modelling, and how it could be used for locating disease causing SNPs.

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