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Activity Number: 436
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303884
Title: Modeling Genetic Variations Using Fragmentation-Coagulation Processes
Author(s): Yee Whye Teh*+ and Charles Blundell
Companies: University College London and University College London
Address: Gatsby Unit, 17 Queen Square, London, WC1N 3AR, United Kingdom
Keywords: Bayesian statistics ; Bayesian nonparametrics ; Computational statistics ; Statistical genetics ; Stochastic processes
Abstract:

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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