Abstract Details
Activity Number:
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276
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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International Indian Statistical Association
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Abstract #311615
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View Presentation
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Title:
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Species Tree Estimation from SNP Data Under the Coalescent
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Author(s):
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Laura Kubatko*+ and Julia Chifman
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Companies:
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Ohio State University and Wake Forest University
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Keywords:
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phylogenetics ;
coalescent ;
algebraic statistics ;
identifiability ;
genomics
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Abstract:
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It is becoming increasingly common to have SNP data available for species-level phylogenomic inference. Under the coalescent model, each SNP will have an underlying gene tree on which the sequence data evolve. We use techniques from algebraic statistics to show that the species tree is identifiable from SNP data generated under the coalescent. We develop a method based on this result that can be used to estimate the species tree from a sample of SNPs, and we show that the method also works well for multi-locus phylogenomic data. We apply the method to both simulated and empirical genomic data sets.
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Authors who are presenting talks have a * after their name.
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