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Activity Number:
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327
- Statistical Methods in Epigenetics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #313623
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Title:
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Maximum Likelihood Inference of Species Trees from Ranked Gene Trees Under the Coalescent Model
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Author(s):
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Anastasiia Kim* and James Degnan
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Companies:
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University of New Mexico and University of New Mexico
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Keywords:
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species tree;
gene tree;
coalescent;
maximum likelihood;
inference;
algorithm
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Abstract:
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A phylogenetic tree is a diagram that represents the evolutionary relationships among a set of organisms. The multispecies coalescent model is a stochastic process on gene trees contained within the underlying species tree. We consider ranked gene trees, which describe not only the topological structure but also the order in which gene lineages join. We introduce the software PRANC designed to compute the probabilities of ranked gene trees under the coalescent process given a species tree. We develop an algorithm that takes a set of ranked gene trees and infers a maximum likelihood species tree. The algorithm searches a discrete parameter space for a better species tree with a higher likelihood. We evaluate the performance of this method using simulation and by application to the real biological dataset of gibbons.
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Authors who are presenting talks have a * after their name.
