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Abstract Details
Activity Number:
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436
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Type:
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Invited
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #303884 |
Title:
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Modeling Genetic Variations Using Fragmentation-Coagulation Processes
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Author(s):
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Yee Whye Teh*+ and Charles Blundell
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Companies:
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University College London and University College London
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Address:
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Gatsby Unit, 17 Queen Square, London, WC1N 3AR, United Kingdom
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Keywords:
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Bayesian statistics ;
Bayesian nonparametrics ;
Computational statistics ;
Statistical genetics ;
Stochastic processes
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Abstract:
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Hudson's coalescent with recombination (aka ancestral recombination graph (ARG)) is a well accepted model of genetic variation in populations. With growing amounts of population genetics data, demand for probabilistic models to analyse such data is strong, and the ARG is a very natural candidate. Unfortunately posterior inference in the ARG is intractable, and a number of approximations and alternatives have been proposed.
A popular class of alternatives are based on hidden Markov models (HMMs), which can be understood as approximating the tree-structured genealogies at each point of the chromosome with a partition of the observed haplotypes. However due to the way HMMs parametrize partitions using latent states, they suffer from significant label-switching issues affecting the quality of posterior inferences.
We propose a novel Bayesian nonparametric model for genetic variations based on Markov processes over partitions called fragmentation-coagulation processes. In addition to some interesting properties, our model does not suffer from the label-switching issues of HMMs. We derive an efficient Gibbs sampler for the model and report excellent results on imputation and phasing.
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