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Evolutionary Genomics pp 3–28Cite as

Tangled Trees: The Challenge of Inferring Species Trees from Coalescent and Noncoalescent Genes

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 856))

Abstract

Phylogenies based on different genes can produce conflicting phylogenies; methods that resolve such ambiguities are becoming more popular, and offer a number of advantages for phylogenetic analysis. We review so-called species tree methods and the biological forces that can undermine them by violating important aspects of the underlying models. Such forces include horizontal gene transfer, gene duplication, and natural selection. We review ways of detecting loci influenced by such forces and offer suggestions for identifying or accommodating them. The way forward involves identifying outlier loci, as is done in population genetic analysis of neutral and selected loci, and removing them from further analysis, or developing more complex species tree models that can accommodate such loci.

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Authors and Affiliations

  1. Department of Organismic and Evolutionary Biology & Museum of Comparative Zoology, Harvard University, Cambridge, MA, USA

    Christian N. K. Anderson & Scott V. Edwards

  2. Department of Agriculture and Natural Resources, Delaware State University, Dover, DE, USA

    Liang Liu

  3. Department of Statistics, The Ohio State University, Columbus, OH, USA

    Dennis Pearl

Authors
  1. Christian N. K. Anderson
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  2. Liang Liu
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  3. Dennis Pearl
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  4. Scott V. Edwards
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Corresponding author

Correspondence to Scott V. Edwards .

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Editors and Affiliations

  1. Department of Computer Science, ETH Zürich, Universitätsstr. 6, Zürich, 8092, Switzerland

    Maria Anisimova

Appendices

Appendix A: Simulating Gene Trees in Species Trees

Many researchers have found it useful to simulate the evolution of genes over a species tree topology. This can be done to test mathematical models, to get a feel for the amount of divergence expected in real data, or (as described below) to rigorously compare the ability of alternative species histories to account for data in hand. The program produces expected amounts of isolation due to drift, and in the context of Bayesian analysis can be used to infer other parameters regarding the demographic processes occurring at scales finer than the species group. A simple example of how this could be accomplished in Bayesian Serial SimCoal (118, 119) is described below. The suite of tools available through Arlequin (120) and the R-scripts in Phybase (121) can be used to further analyze the output of BayeSSC.

Although species trees can be simulated from a birth and death process using an R package TreeSim (http://cran.r-project.org/web/packages/TreeSim/index.html), researchers often adopt a fixed species tree to simulate genetic trees. Imagine a species tree with ten individuals, four species (with 4, 2, 3, and 1 representatives, respectively), and with known (or previously inferred) split times among taxa. In addition, we will assume for this example that the effective population size N e of each contemporary species is 1,000, and that the size of ancestral populations is the sum of the sizes of their respective descendent population. This situation is analogous to that depicted in Fig. 5. The corresponding NEXUS-formatted species tree is:

Fig. 5.
figure 5_1

The species tree simulated in the Appendix. Branch lengths are in units of generations, and branch widths (population sizes) are in units of individuals. This particular tree has the constraint that ancestral population sizes are the sum of the population sizes of descendent lineages, but of course one can simulate without these constraints using either Serial SimCoal or Phybase.

Full size image

(D:1,500,(C:800,(B:500,A:500):300):700).

Here, branch lengths are in units of generations, which is commensurate with using units of individuals for the population sizes (other simulation methods use units of τ = μt and θ = 4Nμ, in units of substitutions per site, instead of t and N e , respectively).

A simple forward simulation can be run in any version of SimCoal using the following.par file:

 

Species tree input file; 10 taxa, 4 sp

4 demes

Deme sizes (arbitrary in this case)

1000

1000

1000

1000

Number of samples per deme

4

2

3

1

Growth rates

0

0

0

0

Number of migration matrices

0

Historical event: Date from to%mig new_N new_r migmat

3 events

500 1 0 1 2.00 0 0

800 2 0 1 1.50 0 0

1500 3 0 1 1.33 0 0

Mutations per generation for the whole sequence

0.0001

Number of loci

10

Data type: DNA, RFLP, or MICROSAT

DNA

//Mutation rates: Gamma parameters, theta and k

0 0

In this case, the tree was perfectly ordered, so all populations could simply fuse with deme 0, readjusting the population size each time. Of course, there is no need to assume that all populations have the same effective size, nor that N e of ancestral populations was the sum of their N e values of their descendants. If we wished to infer the size of clade AB at the time of the split, for example, we could replace the 2.00 in the first historical event with, for example, {U:0.5,3.0}, which would allow the program to infer the posterior probabilities of clade AB having an N e from 500 to 3,000 individuals. Similarly, if the mutation rate of the gene in question was unknown or if a range of mutation rates would simulate the desiderata, then the mutation rate constant, set in the example above at 0.0001, could be replaced with {E:0.0001}, creating an exponential distribution of mutation rates whose mean was 0.0001. Full documentation on the parameter files, and Bayesian inference using priors instead of constants, can be found at the BayeSSC Web site: http://www.stanford.edu/group/hadlylab/ssc/.

Note that the suite of Bayesian tools available at the Web site can be used to evaluate the relative strength of different species topologies. For example, the correspondence between output from the parameter file above with a perfectly ordered tree (((AB)C)D) and real data can be mathematically compared to the correspondence from a second file, where the tree is balanced with, say, topology ((AB)(CD)) instead.

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Anderson, C.N.K., Liu, L., Pearl, D., Edwards, S.V. (2012). Tangled Trees: The Challenge of Inferring Species Trees from Coalescent and Noncoalescent Genes. In: Anisimova, M. (eds) Evolutionary Genomics. Methods in Molecular Biology, vol 856. Humana Press. https://doi.org/10.1007/978-1-61779-585-5_1

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  • DOI: https://doi.org/10.1007/978-1-61779-585-5_1

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