Coalescent parameters

The coalescent theory

The coalescent theory is a statistical framework describing the property of the genealogy of a sample based on the population’s properties. The coalescent only addresses the history of a given sample (backward in time, until the most recent common ancestor). By being sample-centered, and offering the possibility of ignoring, among the events having affected the population, all those that did not affect the sample, the coalescent theory is an efficient analytical and computational tool. The basic idea is to model simulated data sets based on a (potentially complex) population structure and history. There are many computer programs performing coalescence simulations, offering a wide array of functionalities. EggLib incorporates a coalescent simulator, pursuing those particular aims:

  • Integrate several tools within a single, consistent, piece of software (data management, diversity statistics, coalescent simulation) for improved efficiency when they are used together.

  • Support features not available elsewhere, or not together (arbitrary mutation models, homoplasy, delayed samples, diploid model with partial self-fertilization).

There are various variants of coalescent models, differing in how many factors they implement regarding subpopulation, reproduction regime, recombination and gene conversion, and so on. In short, the coalescent simulator of EggLib assumes a model with \(N_0\) diploid individuals (but it is straighforward to consider it as a model with \(2N_0\) haploid individuals). This number of individuals is expressed per population when there are several. The size of populations can be set individually, and changed over time. Each population can have its own self-fertilization rate (which can be changed over time as well). All rates of the migration rate matrix can be set independently, and changed over time. There are instant events set at fixed point in time that can implement large-scale events (population extinction, founding events, and introgression). Finally, recombination and a wide array of mutation models are allowed, enabling the simulation of various types of genetic markers with or without homoplasy.

The basics

The coalescent simulator lies in a module of its own, coalesce. The interface is the class coalesce.Simulator. To perform simulations, you need to create an instance of this class. There can be several instances at the same time if needed. If simulations must be run sequentially, even with different models, it is better to use the same instance to profit from the optimization system of EggLib. However, if the number of populations should change, it is required to create a new instance (see the next paragraph).

The constructor of Simulator instance takes one required argument, the number of populations. It is possible to pass almost all parameters as additional arguments (they must be passed in the keyword=value format). See the example below:

coal = egglib.coalesce.Simulator(1, num_chrom=[20], theta=4.0)

This line defines a model with a single population, with 20 samples and a \(\theta\) of 4.0. The created instance, here named coal, can directly be used to perform simulations and/or setting parameter values (see below).

Note on the number of populations

A characteristic of EggLib’s coalescent simulator is that the number of populations is fixed and immutable during the course of a simulation. This does not mean that classical features of population genetics features such as population extinction, fusion, and split cannot be implemented. All those events can be implemented in a way directly comparable to other pieces of software. A non-existent population is a population that does not contain any samples, and to which all pairwise migration rates are set to zero. Then populations can appear to go extinct or, on the contrary to be created using this combination of parameters. This can be understood as a fixed number of demes in an environment that provides a constant number of sites able to harbour a population. Populations are extinct when the demes does not contain samples (and is not allowed to receive any). We stick however to the term of population.

From a programming point of view, this means that the user of the Simulator class is required to set the number of populations to the total number of populations over the full history of the model. In practice, populations which will be created (or will be created by a split, thinking backward), must already be present since the start of the simulation (with zero samples and with all migration rates to them set to 0). Consider the following model:


It should be noted that, in this kind of representation of coalescent models, the present is a the bottom but, since the coalescent is backward in time, those models must be read from bottom to top, as indicated by the arrow. Still, the “biological” course of time is from the top to the bottom.

In this model we have two populations that merge at some point back in time. Based on EggLib’s principle of constant population number, one population is assumed to merge into the other (here, the second population merges the first one), and will remain as some kind of empty slot. (It is possible to “reactivate” a population further back in time if needed by activating migration rates to this population and/or setting an instant introgression event.)

To implement this model, the following code will suffice:

coal = egglib.coalesce.Simulator(2, num_chrom=[20, 20], theta=4.0)
coal.params.add_event('merge', T=0.1, src=1, dst=0)

The parameters dictionary-like

You may have noticed that the last example used the attribute params of the Simulator instance. This attribute is holding all simulation parameters and can be used to modify almost all parameters. Actually, most parameters can be both set either through the constructor or this property although there are several exceptions (see the review of parameters in the next section).

params acts like a dictionary (actually it is a coalesce.ParamDict instance). Compared with normal dict, it has additional methods and, most importantly, it won’t let you add or remove keys (which would not have any meaning). There are also restrictions on the values parameters can take.

If one uses print(coal.params), it won’t yield anything useful. However, it is possible to convert the parameters into a genuine dict, and display it:

>>> coal = egglib.coalesce.Simulator(num_pop=1)
>>> print(dict(coal.params))
{'num_pop': 2, 'num_sites': 0, 'recomb': 0.0, 'theta': 4.0, 'num_mut': 0, 'mut_model': 'KAM', 'TPM_proba': 0.5, 'TPM_param': 0.5, 'num_alleles': 2, 'rand_start': False, 'num_chrom': [20, 20], 'num_indiv': [0, 0], 'N': [1.0, 1.0], 'G': [0.0, 0.0], 's': [0.0, 0.0], 'site_pos': [], 'site_weight': [], 'migr_matrix': [[None, 0.0], [0.0, None]], 'trans_matrix': [[None, 1.0], [1.0, None]], 'events': [<event_index=0;src=1;dst=0;cat=merge;T=0.1>], 'max_iter': 100000}

It is also possible to generate a string summary of the current value of parameters. This is more readable, but the format might be changed (for example if new parameters are added) so it should not be used in programs unless as for information only (note that the parameter max_iter is not included):

>>> print(coal.params.summary())
Number of populations: 2
    Population 1:
        Single samples: 20
        Double samples: 0
        Relative size: 1
        Growth rate: 0
        Selfing rate: 0
    Population 2:
        Single samples: 20
        Double samples: 0
        Relative size: 1
        Growth rate: 0
        Selfing rate: 0
Recombination rate: 0
Migration matrix:
    0        0
    0        0
Mutation rate: 4
Fixed number of alleles: 0
Mutation model: KAM
Number of alleles: 2
Random start allele: 0
Custom transition matrix: 0
    1        1
    1        1
Number of mutable sites: 0
Number of changes: 2
    Change 1: Admixture
        Date: 0.1
        Population: 1
        Other population: 0
        Probability: 1
    Change 2: Pairwise migration rate change
        Date: 0.1
        Source: 0
        Destination: 1
        Rate: 0

Below we list all parameters of the coalescent simulator, with useful details.

Number of populations and population properties

The table below lists all parameters related to the population structure. The temporal changes of this structure as described in Other parameters.






Number of populations


Required; fixed


Number of haploid samples



Number of diploid samples



Population sizes



Population growth/decline rates



Population selfing rates



Global migration rate


Only in constructor


Pairwise migration rates

0.0 for all

List-like parameters

Except num_pop and migr, all parameters are lists (migr_matrix is a square matrix). Their dimension is determined by the value of num_pop provided to the constructor. These arguments must be provided as a list of length matching the number of populations:

>>> coal = egglib.coalesce.Simulator(num_pop=4, num_chrom=[20,20,20,20], N=[1,1,1,0.2])

When using the dict-like features of params, it is possible to get or set only one item using the bracket operator:

>>> print coal.params['N'][3]
>>> coal.params['N'][2] = 0.5
>>> print(coal.params['N'])
[1.0, 1.0, 0.5, 0.2]

The whole list of values, or a slice of the list, can be set at once:

>>> coal.params['G'] = 1, 2, 3, 4
>>> coal.params['G'][2:4] = 2.5, 2.7
>>> print(coal.params['G'])
[1.0, 2.0, 2.5, 2.7]

The concerned parameters are:

  • num_chrom and num_indiv – EggLib’s coalescent simulator uses a diploid model (assuming a reference population with \(N_0\) diploid individuals), so it is possible to define samples as diploid (individuals for which both chromosomes are sampled) or haploid (individuals for which one random chromosome is sampled). If s is 0 (see below), there is no difference between using num_indiv=x or num_chrom=2*x, and the model is equivalent to a haploid model with \(2N_0\) individuals. It is possible to mix haploid and diploid samples (the total sample size is always equal to num_chrom + 2*num_indiv).

  • N – The relative size of populations (expressed relatively to the standard, current, population size). The default, 1.0, means that all populations have the size of the reference population. Within the framework of the coalescent theory, it is never needed to assume a value for \(N_0\) and several other parameters a expressed relatively to it.

  • G – Exponential growth/decline rate. If the rate is larger than zero, the size of the population decreases exponentially backward in time (the population has been growing exponentially if we think forward). If the rate is smaller than zero, the size of the population increases exponentially (shrinking exponentially if we think forward). In the later case, past population size can become so large that it is not possible to complete the simulation due to computational limitations. Negative values of G should be used with caution and, probably, used with a past event stopping the growth. The size of the population at time \(t\) in the passed is given by \(N_o \exp^{Gt}\), as in the ms software.

  • s - The self-fertilization rate. It is the probability (between 0 and 1, both included) that one individual is the offspring of an occurrence of selfing reproduction. Note that different populations can have different values. In this case, the user should be aware that individuals migrating between populations with varying values of s will assume the self-fertilization of the new population after migration.

The migration matrix

The migr argument of the Simulator constructor allows to avoid setting the whole matrix if all pairwise rates are identical. The value that must be provided as migr value is per-population overall emigration rate, such that each pairwise migration rate will be set to migr/(num_pop-1):

>>> coal = egglib.coalesce.Simulator(num_pop=4, migr=6.0)
>>> print(coal.params['migr_matrix'])
[[None, 2.0, 2.0, 2.0], [2.0, None, 2.0, 2.0], [2.0, 2.0, None, 2.0], [2.0, 2.0, 2.0, None]]

It is also possible to use the method set_migr() of params:

>>> coal.params.set_migr(1.5)
>>> print(coal.params['migr_matrix'])
[[None, 0.5, 0.5, 0.5], [0.5, None, 0.5, 0.5], [0.5, 0.5, None, 0.5], [0.5, 0.5, 0.5, None]]

The argument migr_matrix represents the full matrix of pairwise rates. The matrix above reads as:

















Note that diagonal value are set to None.

Using params, it is possible to set individual pairwise rates within the migration matrix, using the [from, to] operator, where from is the index of the source population, and to is the index of the destination population:

>>> coal.params['migr_matrix'][0, 1] = 4.0
>>> print(coal.params['migr_matrix'])
[[None, 4.0, 0.5, 0.5], [0.5, None, 0.5, 0.5], [0.5, 0.5, None, 0.5], [0.5, 0.5, 0.5, None]]

This operator can also be used to read values.

Alternatively, it is also possible to set the whole matrix in one call:

>>> coal.params['migr_matrix'] = [[None, 1.0, 0.1, 0.1],
...                               [1.0, None, 1.0, 0.1],
...                               [0.1, 1.0, None, 1.0],
...                               [0.1, 0.1, 1.0, None]]

The argument must be a num_pop * num_pop nested list. Therefore, the diagonal must be included in the provided value, all diagonal values are explicitly required to be None. It is possible to set the full matrix as a constructor argument (using the keyword argument migr_matrix).

Mutation models

EggLib’s coalesce module provides a flexible mutation model supporting the standard two-allele model without homoplasy (infinite site model; ISM) or realistic nucleotide mutation models (four alleles with homoplasy and transition/transversion substitution bias), or microsatellite models. The available parameters allow to extend the range of models that can be implemented. The table below presents the parameters that can be set:






\(\theta=4N_0\mu\) parameter


Cannot be set together


Fixed number of mutations



Mutation model


KAM – fixed number of alleles

IAM – infinite number of alleles

SMM – stepwise mutation model

TPM – two-phase mutation model


Number of possible alleles


Only for KAM model


Pick start allele randomly


Only for KAM model


Allele substitution rates

All equal

Only for KAM model


Non-stepwise probability


Only for TPM model


Non-stepwise parameter


Only for TPM model


Number of mutation sites


0 stands for ISM


Position of sites

Evenly spread

If num_sites is not 0


Mutation weight of sites

All equal to 1

If num_sites is not 0

The parameters theta and num_mut control the number of mutations occurring in simulations. If the later option is used, the number of mutations is fixed. In the other case, the number of mutations is drawn randomly based on the statistical parameter.

Description of models

The different models are listed below:

  • KAM (K-allele model) is a model where alleles can take a finite number of values. The number of alleles is given by num_alleles (default: 2, which is the minimum allowed). The allelic values are in the range [0, num_alleles-1]. This model can be configured with the following options:

    • num_alleles, as stated already. Use 2 for standard diallelic markers. To simulate DNA sequence, use num_alleles=4. The identity of the four bases is a matter of convention.

    • rand_start tells if the start (ancestral) allele should be drawn randomly among the available values. By default, the start allele is 0.

    • trans_matrix gives the matrix of transition weights among alleles. The usage of this matrix is identical to the one for the migration matrix. The matrix has dimension num_alleles*num_alleles, with non-diagonal values giving the relative weights of each transition. The entry [i,j] gives the relative weight of the substitution from allele i to allele j. For example, for setting DNA sequence with a transition/transversion rate ratio of 4, one can use:

      num_allele=4, trans_matrix=[[None, 2, 1, 1],
                                  [2, None, 1, 1],
                                  [1, 1, None, 2],
                                  [1, 1, 2, None]]

      The structure of the matrix in the above example is conditioned on the order of alleles. Here we assumed that the bases are sorted in the order: T, C, A, and G.

  • In the IAM (infinite allele model), all mutations create a new allele. In this model, it is guaranteed that all identical alleles are identical by descent. The allelic value are arbitrary and should not be considered as allele size.

  • In the SMM (stepwise mutation model), allelic values are meant to represent sizes and each mutation step increments or decrements the value by one unit. The start value is 0, and, therefore, positive and negative values are equally possible. Note that, to implement microsatellite data, it can be necessary to multiply the allelic values by the assumed repeat size and shift them to the reference locus size (in order to avoid negative value) before processing data in other software.

  • The TPM (two-phase model) is a generalisation of the SMM. In this model, mutation steps can be either of one unit, or drawn from a geometric distribution (in either case they can be either positive or negative). This model has two parameters:

    • TPM_proba, the probability that a mutation step is drawn from the geometric distribution.

    • TPM_param, the parameter of the geometric distribution.

    The generalized stepwise mutation model (a model where all steps are drawn from the geometric distribution) can be implemented by using this combination of parameters: mut_model=TPM, TPM_proba=1, TPM_param=a with a the desired distribution parameter.


The default is equivalent to model=KAM, num_alleles=2, num_sites=0 (see below for the number of sites). The other mutation models are designed a priori to represent microsatellite markers and, most likely, they should be used with a fixed number of sites. If one wants to emulate realistic DNA sequences with homoplasy, the number of sites should also be set to a finite value.

Number of sites

The parameter num_sites determines the number of sites of the simulated genetic segment. There are two main situations:

  • num_sites=0 (the default) corresponds to the infinitely-many site model. In this model, the number of sites is assumed to be large enough (compared to the mutation rate) so that each mutation necessarily hits a new site. In this case, a site is generated for each mutation. Only sites with a mutation are exported, and thereby all exported sites are polymorphic with exactly two alleles. This is the most time-efficient option.

  • num_sites=L with L larger than 0. In this case, there are a finite number of sites and each mutation hits randomly one of the sites. As a result, there can be sites without mutation, others with one mutation exactly, and some with more than one mutation. In the latter case, unless the model is IAM, there can be homoplasy (identity by state but not by descent). If the simulated segment is supposed to represent a stretch of DNA sequence, num_sites is the length of the region, in base pairs. To simulate a single microsatellite marker, use num_sites=1. It is possible to simulate several microsatellite markers, or other type of individual markers (such as individuals SNPs).

    In this case, it is possible to use two additional options: site_pos and site_weight. These two options are lists of length matching the value of num_sites, that can be used the same way as the lists describing population properties (List-like parameters).

    • site_pos gives the position of all sites (in the range [0,1]). The more distant two sites are, the more recombination is likely to occur between them (if recomb is more than 0 of course). By default, the sites are spread evenly along the interval. The first site will always be at position 0 and the last one at position 1 (if there is one site, it will be placed at position 0.5 although this has little relevance regarding recombination).

    • site_weight gives the relative probability that mutation hits each site. To be used if the mutation rate varies over sites. By default, all weights are equal to 1 (note that the absolute value is irrelevant, what matters is the ratio of the weights between sites). For example, to implement three linked microsatellite markers with respective per-site \(\theta\) values of 1.4, 2.4, and 1.9, it is possible to use num_sites=3, theta=5.7, site_weight=[1.4, 2.4, 1.9] (5.7 being the total mutation rate). Unlinked markers must be simulated independently.

Other parameters

The other parameters are listed in the table below. They are detailed in the following paragraphs.





List of historical events



\(\rho=4N_0c\) parameter



Maximum number of coalescent iterations


Historical events

The coalescent simulator support historical changes of most parameters. The change specifications are held in a specific entry of the parameter dictionary-like, at the key events.

By default, the list of events is empty:

>>> coal = egglib.coalesce.Simulator(num_pop=4, num_chrom=[10, 10, 10, 10], theta=1)
>>> print(coal.params['events'])

Events can be created using the method add_event(). This method takes as arguments a string identifying the type of event, the date of the event (in the past, in units of \(4N_0\) generations), and parameters depending on the type of event. Here are two examples using size, the event allowing to implement past changes of population size:

>>> coal.params.add_event(cat='size', T=0.4, idx=0, N=0.1)
>>> coal.params.add_event(cat='size', T=0.5, idx=0, N=1.0)
>>> print(coal.params['events'])
[<event_index=0;idx=0;N=0.1;cat=size;T=0.4>, <event_index=1;idx=0;N=1.0;cat=size;T=0.5>]

The events can be added in any order: they will be sorted automatically based on their date. If their date changes, the sorting will be updated appropriately and automatically.

As you can see, when printed, events show a string presenting their parameters, but the string should not be used to extract information in programs. Rather, a dictionary can be obtained using the syntax below:

>>> print(coal.params['events'][0])
{'idx': 0, 'N': 0.1, 'cat': 'size', 'T': 0.4}

To modify the content of an event, one must used a method named update():

>>> coal.params['events'].update(0, N=0.05)
>>> print(coal.params['events'])
[<event_index=0;T=0.4;idx=0;N=0.05;cat=size>, <event_index=1;T=0.5;idx=0;N=1.0;cat=size>]

The list below presents the list of available events and the list of their parameters. Note that arguments cat (event code) and T (time of the event) are always required. More details (in particular for complex types of events) are given in Use of historical events.

  • size – change size of a population.

    • idx – population index (if omitted, all populations).

    • N – new population size, expressed relatively to N_0.

  • migr – change all migration rates.

    • M – new migration rate, such that all pairwise migration rates will be equal to M/(num_pop-1).

  • pair_migr – change a pairwise migration rate.

    • src – source population index.

    • dst – destination population index.

    • M – new pairwise migration rate.

  • growth – change exponential growth/decline rate of a population.

    • idx – population index (if omitted, all populations).

    • G – new exponential growth/decline rate.

  • selfing – change selfing rate for a population.

    • idx – population index (if omitted, all populations).

    • s – new selfing rate.

  • bottleneck – apply a bottleneck. In this implementation, bottlenecks are assumed to be short enough to have negligible length (such that, in particular, no mutations can occur within this time frame). Therefore, such bottlenecks are implemented as a random amount of coalescence events (excluding all other events), controlled by the parameter S.

    • idx – population index (if omitted, all populations).

    • S – bottleneck strength.

  • recombination – change recombination rate.

    • R – new recombination rate.

  • admixture – move random lineages from one population to another.

    • src – source population.

    • dst – destination population.

    • proba – instant migration probability (not related to migration rates)

  • merge – move all lineages from a population to another, then set all migration rates to the first population to 0.

    • src – source population index (the one that will be virtually removed).

    • dst – destination population.

  • sample – perform a delayed sampling.

    • idx – population index.

    • label – group label to apply to samples belonging to this sampling (this allows to treat the sampling as an independent population in the generated data).

    • num_chrom – number of haploid samples.

    • num_indiv – number of diploid samples.


Recombination is implemented over a continuous interval, controlled by a single parameter. The continuous interval means that each occurrence of recombination yields a breakpoint randomly placed on the interval [0,1] representing the simulated chromosome, thus generating a new segment (which is represented by a genealogical tree of its own).

The recombination rate is not allowed to vary along the simulated genetic segment (although it is allowed to vary discretely over time, see the historical events above), but one can control the amount of recombination between sites by adjusting their positions (see Number of sites).

Maximum number of iterations

The number of iterations in the coalescent process is bounded, in order to prevent an infinite loop in situations where the final coalescence can never be concluded (see Completing simulations). If this limit is reached by error (perhaps with extreme values of some parameters), the bound can be lifted using the max_iter parameter.