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Activity Number:
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134
- Recent Development in Methods for Statistical Genetics
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #329130
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Presentation
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Title:
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Sampling Partial Genealogies Using Sequential Importance Sampling
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Author(s):
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Dongmeng Liu* and Jinko Graham
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Companies:
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and Simon Fraser University
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Keywords:
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coalescence;
partial genealogies;
population genetics;
sequential importance sampling;
ancestry
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Abstract:
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A gene genealogy traces the ancestry of segments of DNA sequence back in time to their common ancestor. We cannot observe the genealogies but the DNA sequence data give us information which can be used to sample from the posterior distribution of the underlying genealogies. However, a full genealogy can be so large that it greatly decreases the efficiency of existing sampling techniques. Partial genealogies trace the ancestry to a fixed point back in time only, and can dramatically improve the efficiency of some commonly-used sampling methods. We introduce an algorithm for sampling the partial genealogies of a set of DNA sequences from their posterior distribution. Our algorithm uses sequential importance sampling (SIS) and accommodates coalescence, mutation and recombination events in the ancestral history of the sequences. SIS methods are computationally intensive and have become popular as an alternative to MCMC methods for inference in population genetics.
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Authors who are presenting talks have a * after their name.
