The BPP program for species tree estimation and species delimitation

Abstract This paper provides an overview and a tutorial of the BPP program, which is a Bayesian MCMC program for analyzing multi-locus genomic sequence data under the multispecies coalescent model. An example dataset of five nuclear loci from the East Asian brown frogs is used to illustrate four different analyses, including estimation of species divergence times and population size parameters under the multispecies coalescent model on a fixed species phylogeny (A00), species tree estimation when the assignment and species delimitation are fixed (A01), species delimitation using a fixed guide tree (A10), and joint species delimitation and species-tree estimation or unguided species delimitation (A11). For the joint analysis (A11), two new priors are introduced, which assign uniform probabilities for the different numbers of delimited species, which may be useful when assignment, species delimitation, and species phylogeny are all inferred in one joint analysis. The paper ends with a discussion of the assumptions, the strengths and weaknesses of the BPP analysis.

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A Simulation Study to Examine the Information Content in Phylogenomic Data Sets under the Multispecies Coalescent Model

Molecular Biology and Evolution ◽

10.1093/molbev/msaa166 ◽

2020 ◽

Vol 37 (11) ◽

pp. 3211-3224

Author(s):

Jun Huang ◽

Tomáš Flouri ◽

Ziheng Yang

Keyword(s):

Information Content ◽

Mutation Rate ◽

Species Delimitation ◽

Species Tree ◽

Data Sets ◽

Site Mutation ◽

Coalescent Model ◽

Inference Problems ◽

Multispecies Coalescent ◽

Tree Estimation

Abstract We use computer simulation to examine the information content in multilocus data sets for inference under the multispecies coalescent model. Inference problems considered include estimation of evolutionary parameters (such as species divergence times, population sizes, and cross-species introgression probabilities), species tree estimation, and species delimitation based on Bayesian comparison of delimitation models. We found that the number of loci is the most influential factor for almost all inference problems examined. Although the number of sequences per species does not appear to be important to species tree estimation, it is very influential to species delimitation. Increasing the number of sites and the per-site mutation rate both increase the mutation rate for the whole locus and these have the same effect on estimation of parameters, but the sequence length has a greater effect than the per-site mutation rate for species tree estimation. We discuss the computational costs when the data size increases and provide guidelines concerning the subsampling of genomic data to enable the application of full-likelihood methods of inference.

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A Bayesian Implementation of the Multispecies Coalescent Model with Introgression for Phylogenomic Analysis

Molecular Biology and Evolution ◽

10.1093/molbev/msz296 ◽

2019 ◽

Vol 37 (4) ◽

pp. 1211-1223 ◽

Cited By ~ 8

Author(s):

Tomáš Flouri ◽

Xiyun Jiao ◽

Bruce Rannala ◽

Ziheng Yang

Keyword(s):

Gene Flow ◽

Genomic Sequence ◽

Sequence Data ◽

Incomplete Lineage Sorting ◽

Mosquito Species ◽

Phylogenomic Analysis ◽

Data Sets ◽

Lineage Sorting ◽

Coalescent Model ◽

Multispecies Coalescent

Abstract Recent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here, we implement the multispecies-coalescent-with-introgression model, an extension of the multispecies-coalescent model to incorporate introgression, in our Bayesian Markov chain Monte Carlo program Bpp. The multispecies-coalescent-with-introgression model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Reanalysis of data sets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.

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Challenges in Species Tree Estimation Under the Multispecies Coalescent Model

Genetics ◽

10.1534/genetics.116.190173 ◽

2016 ◽

Vol 204 (4) ◽

pp. 1353-1368 ◽

Cited By ~ 77

Author(s):

Bo Xu ◽

Ziheng Yang

Keyword(s):

Species Tree ◽

Coalescent Model ◽

Multispecies Coalescent ◽

Tree Estimation

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The Impact of Cross-Species Gene Flow on Species Tree Estimation

Systematic Biology ◽

10.1093/sysbio/syaa001 ◽

2020 ◽

Vol 69 (5) ◽

pp. 830-847 ◽

Cited By ~ 1

Author(s):

Xiyun Jiao ◽

Tomáš Flouri ◽

Bruce Rannala ◽

Ziheng Yang

Keyword(s):

Gene Flow ◽

Sequence Data ◽

Majority Vote ◽

Gene Tree ◽

Species Tree ◽

Likelihood Method ◽

Estimation Methods ◽

Vote Method ◽

Multispecies Coalescent ◽

Tree Estimation

Abstract Recent analyses of genomic sequence data suggest cross-species gene flow is common in both plants and animals, posing challenges to species tree estimation. We examine the levels of gene flow needed to mislead species tree estimation with three species and either episodic introgressive hybridization or continuous migration between an outgroup and one ingroup species. Several species tree estimation methods are examined, including the majority-vote method based on the most common gene tree topology (with either the true or reconstructed gene trees used), the UPGMA method based on the average sequence distances (or average coalescent times) between species, and the full-likelihood method based on multilocus sequence data. Our results suggest that the majority-vote method based on gene tree topologies is more robust to gene flow than the UPGMA method based on coalescent times and both are more robust than likelihood assuming a multispecies coalescent (MSC) model with no cross-species gene flow. Comparison of the continuous migration model with the episodic introgression model suggests that a small amount of gene flow per generation can cause drastic changes to the genetic history of the species and mislead species tree methods, especially if the species diverged through radiative speciation events. Estimates of parameters under the MSC with gene flow suggest that African mosquito species in the Anopheles gambiae species complex constitute such an example of extreme impact of gene flow on species phylogeny. [IM; introgression; migration; MSci; multispecies coalescent; species tree.]

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MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

Bioinformatics ◽

10.1093/bioinformatics/btaa868 ◽

2020 ◽

Author(s):

John A Rhodes ◽

Hector Baños ◽

Jonathan D Mitchell ◽

Elizabeth S Allman

Keyword(s):

Network Inference ◽

Incomplete Lineage Sorting ◽

R Package ◽

Species Tree ◽

Supplementary Information ◽

Species Trees ◽

Lineage Sorting ◽

Coalescent Model ◽

Multispecies Coalescent ◽

Tree Inference

Abstract Summary MSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting and its network analog. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package implements the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model. Availability MSCquartets is available through the Comprehensive R Archive Network: https://CRAN.R-project.org/package=MSCquartets. Supplementary information Supplementary materials, including example data and analyses, are incorporated into the package.

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Uprooting phylogenetic uncertainty in coalescent species delimitation: A meta-analysis of empirical studies

Current Zoology ◽

10.1093/czoolo/61.5.866 ◽

2015 ◽

Vol 61 (5) ◽

pp. 866-873 ◽

Cited By ~ 13

Author(s):

Itzue W. Caviedes-Solis ◽

Nassima M. Bouzid ◽

Barbara L. Banbury ◽

Adam D. Leaché

Keyword(s):

Species Delimitation ◽

Sequence Data ◽

Meta Analysis ◽

Empirical Studies ◽

Species Tree ◽

Species Trees ◽

Posterior Probabilities ◽

Guide Tree ◽

Tree Topologies ◽

Accurate Quantification

Abstract Phylogenetic and phylogeographic studies rely on the accurate quantification of biodiversity. In recent studies of taxonomically ambiguous groups, species boundaries are often determined based on multi-locus sequence data. Bayesian Phylogenetics and Phylogeography (BPP) is a coalescent-based method frequently used to delimit species; however, empirical studies suggest that the requirement of a user-specified guide tree biases the range of possible outcomes. We evaluate fifteen multi-locus datasets using the most recent iteration of BPP, which eliminates the need for a user-specified guide tree and reconstructs the species tree in synchrony with species delimitation (= unguided species delimitation). We found that the number of species recovered with guided versus unguided species delimitation was the same except for two cases, and that posterior probabilities were generally lower for the unguided analyses as a result of searching across species trees in addition to species delimitation models. The guide trees used in previous studies were often discordant with the species tree topologies estimated by BPP. We also compared species trees estimated using BPP and *BEAST and found that when the topologies are the same, BPP tends to give higher posterior probabilities.

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A Bayesian implementation of the multispecies coalescent model with introgression for comparative genomic analysis

10.1101/766741 ◽

2019 ◽

Cited By ~ 1

Author(s):

Thomas Flouris ◽

Xiyun Jiao ◽

Bruce Rannala ◽

Ziheng Yang

Keyword(s):

Gene Flow ◽

Genomic Sequence ◽

Sequence Data ◽

Incomplete Lineage Sorting ◽

Mosquito Species ◽

Genomic Analysis ◽

Comparative Genomic ◽

Lineage Sorting ◽

Multispecies Coalescent ◽

Important Means

AbstractRecent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here we implement the multispecies-coalescent-with-introgression (MSci) model, an extension of the multispecies-coalescent (MSC) model to incorporate introgression, in our Bayesian Markov chain Monte Carlo (MCMC) program BPP. The MSci model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Re-analysis of datasets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.

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Species Identification by Bayesian Fingerprinting: A Powerful Alternative to DNA Barcoding

10.1101/041608 ◽

2016 ◽

Cited By ~ 3

Author(s):

Ziheng Yang ◽

Bruce Rannala

Keyword(s):

Dna Barcoding ◽

Species Identification ◽

Model Comparison ◽

Sequence Data ◽

Single Species ◽

Species Variation ◽

Distance Threshold ◽

Coalescent Model ◽

Multispecies Coalescent ◽

Bayesian Model Comparison

A number of methods have been developed to use genetic sequence data to identify and delineate species. Some methods are based on heuristics, such as DNA barcoding which is based on a sequence-distance threshold, while others use Bayesian model comparison under the multispecies coalescent model. Here we use mathematical analysis and computer simulation to demonstrate large differences in statistical performance of species identification between DNA barcoding and Bayesian inference under the multispecies coalescent model as implemented in the bpp program. We show that a fixed genetic-distance threshold as used in DNA barcoding is problematic for delimiting species, even if the threshold is "optimized", because different species have different population sizes and different divergence times, and therefore display different amounts of intra-species versus inter-species variation. In contrast, bpp can reliably delimit species in such situations with only one locus and rarely supports a wrong assignment with high posterior probability. While under-sampling or rare specimens may pose problems for heuristic methods, bpp can delimit species with high power when multi-locus data are used, even if the species is represented by a single specimen. Finally we demonstrate that bpp may be powerful for delimiting cryptic species using specimens that are misidentified as a single species in the barcoding library.

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