Last update: Jan 2, 2018, Contributors: Dominik Schrempf, Minh Bui

Polymorphism-aware models

Use population data to improve inferences.

Polymorphism-aware phylogenetic Models (PoMo) improve phylogenetic inference using population data (site frequency data). Thereby they builds on top of DNA substitution models and naturally account for incomplete lineage sorting. In order to run PoMo, you need more sequences per species/population (ideally five or more chromosomes per species/population) so that information about the site frequency spectrum is available.

Currently this model is only available in the beta version 1.6. Please download it from here:

TIP: For a quick overview of all PoMo related options in IQ-TREE, run the command iqtree -h and scroll to the heading POLYMORPHISM AWARE MODELS (PoMo).

If you use PoMo, please cite Schrempf et al., 2016:

Dominik Schrempf, Bui Quang Minh, Nicola De Maio, Arndt von
Haeseler, and Carolin Kosiol (2016) Reversible polymorphism-aware
phylogenetic models and their application to tree inference.
J. Theor. Biol., 407, 362–370.

Counts files

The input of PoMo is allele frequency data. Especially, when populations have many individuals it is preferable to count the number of bases at each position compared to providing data for each chromosome in a FASTA file. Thereby file size is decreased and parsed faster.

Counts files contain:

  • One headerline that specifies the file as counts file and states the number of populations (leaves on the tree) as well as the number of sites (separated by white space).

  • A second headerline with white space separated headings: CRHOM (chromosome), POS (position) and sequence names.

  • Many lines with counts of A, C, G and T bases and their respective positions.


  • Lines before the first headerline starting with # are treated as comments.


CHROM  POS  Sheep    BlackSheep  RedSheep  Wolf     RedWolf
1      1    0,0,1,0  0,0,1,0     0,0,1,0   0,0,5,0  0,0,0,1
1      2    0,0,0,1  0,0,0,1     0,0,0,1   0,0,0,5  0,0,0,1
9      8373 0,0,0,1  1,0,0,0     0,1,0,0   0,1,4,0  0,0,1,0
Y      9999 0,0,0,1  0,1,0,0     0,1,0,0   0,5,0,0  0,0,1,0

The download also includes an example counts file called This file is a subset of the great ape data that has been analyzed in one of our publications. It includes twelve great ape population with one to 23 inividuals each (two to 56 chromosomes).

Conversion scripts

If you do not want to create counts files with your own scripts, you can use the python script that we provide. For detailed instructions, please refer to the GitHub repository of the counts file library cflib.

First running example

You can now start to reconstruct a maximum-likelihood tree from this alignment by entering (assuming that is in the same folder):

iqtree -s -m HKY+P

or, e.g.,

iqtree -nt 4 -s -m HKY+P

if you use the multicore (OMP) version. -s specifies of the alignment file and -m the model (HKY substitution model with polymorphisms; PoMo), similar to the standard IQ-TREE usage. At the end of the run IQ-TREE writes the same output files as in the standard version (see tutorial).

  • the main report file that is self-readable. You should look at this file to see the computational results. It also contains a textual representation of the final tree.
  • the ML tree in NEWICK format, which can be visualized by any supported tree viewer programs like FigTree or iTOL.
  • log file of the entire run (also printed on the screen). To report bugs, please send this log file and the original alignment file to the authors.

The default prefix of all output files is the alignment file name. However, you can always change the prefix using the -pre option, e.g.:

iqtree -s -pre myprefix

This prevents output files to be overwritten when you perform multiple analyses on the same alignment within the same folder.

Substitution models

Different DNA substitution models can be selected with the -m option. E.g., to select the GTR model, run IQ-TREE with:

iqtree -s -m GTR+P

TIP: For a quick overview of all available models in IQ-TREE, run the command iqtree -h and scroll to the heading POLYMORPHISM AWARE MODELS (PoMo).

Virtual population size

PoMo describes the evolution of populations along a phylogeny by means of a virtual population of constant size N, which defaults to nine (for details, see Schrempf et al., 2016). This is a good and stable default value. If only very few chromosomes have been sequenced per population (e.g., two to four), N should be lowered to the average number of chromosomes per population. If enough data is available and calculations are not too time consuming, we advise to increase N up to a maximum of 19. You can choose odd values from three to 19 as well as 2 and 10. E.g., to set N to 19:

iqtree -s -m HKY+P+N19

Level of polymorphism

As of version 1.6, IQ-TREE with PoMo also allows fixation of the level of heterozygosity, which is also called Watterson’s theta or 4Nu. When analyzing population data, the amount of polymorphism is inferred during maximization of the likelihood. However, in some situations it may be useful to set the level of polymorphism to the observed value in the data (empirical value):

iqtree -s -m HKY+P{EMP}

or to set the level of polymorphism by hand, e.g.,:

iqtree -s -m HKY+P{0.0025}

Together with the ability to set model parameters, the model can be fully specified, e.g.:

iqtree -s -m HKY{6.0}+P{0.0025}

This sets the transition to transversion ratio to a value of 6.0 and the level of polymorphism to a value of 0.0025. In this case, IQ-TREE only performs a tree search because the model is fully specified.

Sampling method

For advanced users. PoMo offers different methods to read in the data (Schrempf et al., 2016). Briefly, each population and site are treated as follows

  1. Weighted binomial (default, +WB): assign the likelihood of each PoMo state to its probability of leading to the observed data, assuming it is binomially sampled. Example:

     iqtree -s -m HKY+P+WB
  2. Weighted hypergeometric (+WH): assign the likelihood of each PoMo state to its probability of leading to the observed data, assuming it is hypergeometrically sampled. Example:

     iqtree -s -m HKY+P+WH
  3. Sampled: randomly draw N samples with replacement from the given data. The N picked samples constitute a PoMo state which will be assigned a likelihood of 1. All other PoMo states have likelihood 0. Example:

     iqtree -s -m HKY+P+S

We expect a slight overestimation of the heterozygosity for weighted binomial sampling. This is because monomorphic (fixed) states can be reached from polymorphic states during the sampling step, while polymorphic states cannot be reached from monomorphic states (sampling does not involve mutation). I.e., only when the level of heterozygosity at the leaves is overestimated, the sampling step leads to the correct amount of heterozygosity observed in the data.

If you wish to avoid this effect, use weighted hypergeometric sampling. However, we have observed that weighted binomial sampling is more stable.

State frequency type

Similar to standard models, the state frequency type can be selected with +F model string modifiers. The default is to set the state frequencies (i.e., the frequencies of the nucleotides A, C, G and T) to the observed values in the data (empirical value). To estimate the allele frequencies together with the rate parameters during maximization of the likelihood, use:

iqtree -s -m GTR+P+FO

Rate heterogeneity

Recently, PoMo allows inference with different rate categories. As of version 1.6, only discrete Gamma rate heterogeneity is supported. Please be aware, that for biological and mathematical reasons (we cannot simply scale the full transition matrix but have to separate the mutational component from genetic drift), the run time scales linearly with the number of rate categories. In the future, we plan to work on decreasing run time as well as implement more rate heterogeneity types. To use a discrete Gamma model with 4 rate categories, use:

iqtree -s -m HKY+P+G4

Bootstrap branch support

Bootstrapping works as expected with PoMo. The standard non-parametric bootstrap is invoked by the -b option, e.g., for 100 replicates:

iqtree -s -m HKY+P -b 100

To overcome the computational burden required by the non-parametric bootstrap, IQ-TREE introduces an ultra fast bootstrap approximation (UFBoot) that is orders of magnitude faster than the standard procedure and provides relatively unbiased branch support values. To run UFBoot, use the option -bb, e.g., for 1000 replicates:

iqtree -s -m HKY+P -bb 1000

For a detailed description, please refer to the bootstrap tutorial.

Interpretation of branch lengths

PoMo estimates the branch length in number of mutations and frequency shifts (drift) per site. The number of drift events compared to the number of mutations becomes higher if the virtual population size is increased. To get the branch length measured in number of substitutions per site which enables a comparison to the branch length estimated by standard DNA substitution models, it has to be divided by N^2. PoMo also outputs the total tree length measured in number of substitutions per site in An example of the relevant section:

NOTE: The branch lengths of PoMo measure mutations and frequency shifts.
To compare PoMo branch lengths to DNA substitution models use the tree length
measured in substitutions per site.

Total tree length (sum of branch lengths)
 - measured in number of mutations and frequency shifts per site: 0.71200751
 - measured in number of substitutions per site (divided by N^2): 0.00879022
Sum of internal branch lengths
- measured in mutations and frequency shifts per site: 0.01767814 (2.48285810% of tree length)
- measured in substitutions per site: 0.01767814 (2.48285810% of tree length)