Browsing by Author "Liu, Xinhao"

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    A divide-and-conquer method for scalable phylogenetic network inference from multilocus data
    (Oxford University Press, 2019) Zhu, Jiafan; Liu, Xinhao; Ogilvie, Huw A.; Nakhleh, Luay K.
    Motivation: Reticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting. However, these methods can only handle a small number of loci from a handful of genomes. Results: In this article, we introduce a novel two-step method for scalable inference of phylogenetic networks from the sequence alignments of multiple, unlinked loci. The method infers networks on subproblems and then merges them into a network on the full set of taxa. To reduce the number of trinets to infer, we formulate a Hitting Set version of the problem of finding a small number of subsets, and implement a simple heuristic to solve it. We studied their performance, in terms of both running time and accuracy, on simulated as well as on biological datasets. The two-step method accurately infers phylogenetic networks at a scale that is infeasible with existing methods. The results are a significant and promising step towards accurate, large-scale phylogenetic network inference.
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    Variational Inference Using Approximate Likelihood Under the Coalescent With Recombination
    (2021-04-29) Liu, Xinhao; Nakhleh, Luay K.
    Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. The multispecies coalescent (MSC) model has been widely employed by phylogenetic algorithms to construct the species tree while accounting for incomplete lineage sorting (ILS). However, the no-recombination assumption of the MSC model has been questioned. To analyze large genomic regions, we need to simultaneously account for both ILS and recombination. A promising avenue for the analysis of large genomic alignments, which are now commonplace, are coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. I introduce in this thesis a novel method, VICAR (Variational Inference under the CoAlescent with Recombination), for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables VICAR to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human-chimp-gorilla scenario, I show that VICAR has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies and I report on their accuracy. Furthermore, I illustrate how to scale the method to larger data sets through a divide-and-conquer approach. This accuracy means that my approach is useful now, and by deriving transition rates by simulation it is flexible enough to enable future implementations of all kinds of population models. I have implemented VICAR in the publicly available software package PhyloNet.