- Open Access
GENOMEPOP: A program to simulate genomes in populations
© Carvajal-Rodríguez; licensee BioMed Central Ltd. 2008
- Received: 05 February 2008
- Accepted: 30 April 2008
- Published: 30 April 2008
There are several situations in population biology research where simulating DNA sequences is useful. Simulation of biological populations under different evolutionary genetic models can be undertaken using backward or forward strategies. Backward simulations, also called coalescent-based simulations, are computationally efficient. The reason is that they are based on the history of lineages with surviving offspring in the current population. On the contrary, forward simulations are less efficient because the entire population is simulated from past to present. However, the coalescent framework imposes some limitations that forward simulation does not. Hence, there is an increasing interest in forward population genetic simulation and efficient new tools have been developed recently. Software tools that allow efficient simulation of large DNA fragments under complex evolutionary models will be very helpful when trying to better understand the trace left on the DNA by the different interacting evolutionary forces. Here I will introduce GenomePop, a forward simulation program that fulfills the above requirements. The use of the program is demonstrated by studying the impact of intracodon recombination on global and site-specific dN/dS estimation.
I have developed algorithms and written software to efficiently simulate, forward in time, different Markovian nucleotide or codon models of DNA mutation. Such models can be combined with recombination, at inter and intra codon levels, fitness-based selection and complex demographic scenarios.
GenomePop has many interesting characteristics for simulating SNPs or DNA sequences under complex evolutionary and demographic models. These features make it unique with respect to other simulation tools. Namely, the possibility of forward simulation under General Time Reversible (GTR) mutation or GTR×MG94 codon models with intra-codon recombination, arbitrary, user-defined, migration patterns, diploid or haploid models, constant or variable population sizes, etc. It also allows simulation of fitness-based selection under different distributions of mutational effects. Under the 2-allele model it allows the simulation of recombination hot-spots, the definition of different frequencies in different populations, etc. GenomePop can also manage large DNA fragments. In addition, it has a scaling option to save computation time when simulating large sequences and population sizes under complex demographic and evolutionary situations. These and many other features are detailed in its web page .
- Forward Simulation
- General Time Reversible
- Codon Model
- Migration Level
- Variable Population Size
There are several situations in population biology research where simulation of DNA sequences is useful. Simulations have been used to for hypothesis testing [2–4], to study the impact of differing demographic scenarios on patterns of human diversity , or to simulate the evolution of complex diseases in human populations [6, 7]. In addition, population simulation of genetic datasets is also used to estimate population parameters [8–10].
One of the most exciting research areas in the current context of population genetics is the HapMap project. Knowledge about patterns of linkage disequilibrium (LD) in humans is very important from a genomic point of view. The existence of linkage or haplotype blocks  or, at least, networks of SNPs in high LD , will facilitate the assembly of human genome haplotype maps [13–15] that will enormously improve, among other things, the efficiency of disease gene mapping. It seems that these blocks are mainly defined by recombination hot spots [16, 17], but haplotype blocks can also be generated by genetic drift in regions of uniform recombination if rates is low enough . We have now growing empirical knowledge about haplotype block and tagSNP diversity, but less is known about the effect of population demographic history. Though important work has been undertaken in the application of population genetics to LD mapping [19–22] and its relevance to human populations [23–25], we still have an incomplete understanding of how the combined effect of genetic drift, mutation, recombination and migration, affect LD and tagSNP patterns, although it is known that they do . Moreover, recombination is an important evolutionary process to understand how genetic diversity is generated and maintained in populations. Jointly with positive selection, recombination allows for very high rates of evolution . However, the impact of recombination is dependent on other forces, such as selection and demography. Developing tools that allow simultaneous simulation of natural selection, recombination and complex demographic patterns will be of great help in trying to better understand the trace left on the DNA by the different interacting evolutionary forces.
Simulation of biological populations under different evolutionary genetic models can be done following backward or forward strategies. Backward simulations, also called coalescent-based simulations, are computationally very efficient because they are based on the history of lineages with surviving offspring in the current population and ignore all individuals that are not ancestral to the present-day population . Hence, coalescent is a sample-based theory relevant to the study of population samples and DNA sequence data. From its beginnings, the basic coalescent has been extended in several useful ways. For example, to include structured population models [28–32], changing population size [33–35], recombination [36, 37] and selection [38–43].
On the contrary, forward simulations are less efficient because the entire population is simulated from past to present. However, the coalescent framework imposes some limitations that forward simulation does not. The first of these is the same feature that causes its efficiency, namely, the coalescent does not keep track of the complete ancestral information i.e. only takes into account ancestries that survived to form the present-day sample. Thus, if the interest is focused on the evolutionary process itself, rather than on its outcome, forward simulations should be preferred . Second, coalescent simulations are complicated by simple genetic forces such as selection, and although different evolutionary scenarios have been incorporated (see above) it is still difficult to implement models incorporating complex evolutionary situations with selection, variable population size, recombination, complex mating schemes, and so on. In fact, we can only simulate limited forms of recombination and selection under the coalescent. It is known that recombination has a major impact for detecting positive natural selection [45, 46]. Shriner et al studied the impact of recombination under a neutral model. Anisimova et al studied the recombination effect under a coalescent codon-based model i.e. the unit of change was the codon instead of the nucleotide. In the latter case, recombination was not simulated at the intracodon level. Therefore, we still ignore the importance of intracodon recombination under a given codon-based model. Moreover, coalescent methods cannot yet simulate realistic samples of complex human diseases . Indeed, when simulating non-neutral scenarios and/or complex models under the coalescent, much of its computational efficiency is lost (however, see recent work by Marjoram  and Liang ). Furthermore, the coalescent model is based on specific limiting values and relationships between some important parameters . Hence, there is increasing interest in forward population genetic simulation and new efficient tools have been recently developed [50–52]. Therefore, a program that allows the simulation forward in time, of different Markovian nucleotide or codon models of DNA mutation combined with recombination, at inter and intra codon levels, fitness-based selection and complex demographic scenarios, will be of great interest. Here I will introduce the program GenomePop that fulfills the mentioned requirements.
GenomePop uses a simple and efficient algorithm to perform forward simulation of populations and/or genomes. The basic idea considers an individual as the differences (mutations) between this individual and a reference or consensus genotype. Thus, each individual is no longer represented by its complete sequence or genotype but by the mutations it carries with respect to the consensus. A more detailed explanation of the algorithm is provided at the program web page. Taking advantage of the efficiency of this approach, GenomePop can simulate, forward in time, DNA sequences under specific Markov models. The program allows the simulation of recombination under both nucleotide and codon models of evolution, providing a way to simulate recombination at inter and intracodon levels under codon models. It also permits arbitrary migration models, simulation of SNPs, recombination hot-spots, fitness-based selection and many other features that are detailed in the program web-page. GenomePop has different output formats as GenePop for SNPs and Phylip or Nexus for DNA sequences.
Markov models of DNA mutation
Markov processes are used in molecular evolution to describe the change between nucleotides, aminoacids or codons over evolutionary time. Usually, time is measured as the number of substitutions because molecular sequence data does not allow the separate estimation of the rate and the time, but only of their product . In the context of forward simulation we are not interested in the transition after an arbitrary time t (branch length) but just in the transition from a nucleotide or codon to another, given that a mutation occurs. An advantage of this approach is that we need to compute the transition matrix just once at the beginning of the evolutionary process. Therefore, consider a given instantaneous substitution rate matrix Q, which allows for a complete definition of any Markovian substitution model , the matrix M = -qQ + I is the conditional transition matrix to go from i to j provided that a substitution occurs, where q = diagonal (1/q i ) and I is the identity matrix . Then, given an instantaneous substitution matrix Q, estimated for example using PAUP  or Hyphy  programs, we can obtain the corresponding transition matrix M that can be used to produce the necessary mutation process in a forward in time evolutionary model.
There are two basic biological models implemented in GenomePop, namely "viral" and "non-viral". The only difference that distinguishes them is just that in the viral model the initial sequences are different in each population, as the different viruses infect different individuals. Thus, the user can define a viral model indicating the percentage of sequence identity (0–100) between the sequences of the distinct populations. By default the sequence identity is zero i.e. the sequences at each population are randomly settled. In the non-viral model the initial sequence is the same for every population (identity of 100%).
DNA models, recombination and selection
GenomePop DNA models
MG94 × (JC/GTR)
Clearly, the more complex the model defined, the slower the simulation. To avoid high computation times, GenomePop incorporates a scaling option based on the fact that, under neutral models, we can scale the population size N and the time t, provided the consequent correction to the mutation (μ), migration (m) and recombination (r) rates holds the corresponding compound products Nμ, Nr, Nm, etc., constant.
Thus, the input in Figure 2 generates 100 datasets under a GTR model with substitution rates typical for HIV . Both recurrent and retromutation are allowed. The system will evolve 1 chromosome of 1 Kb under the given model over 20,000 generations. As can be seen in Figure 2, a scaling of 10 was used, which implies that both, population size and the number of generations, was divided by 10 and mutation was multiplied by the same factor. A more exhaustive explanation of the input facilities of GenomePop is provided at the program web page.
Example and validation of the Markov mutation method
For each obtained dataset from the input in Figure 2, the best-fit model of nucleotide substitution under the Akaike information criteria (AIC) was estimated with Modeltest v3.6 , using maximum likelihood (ML) estimates from PAUP* . The percentage of correct model estimation (GTR) was 97% although some datasets, about 29%, were also assigned invariable sites or rate heterogeneity among sites. The substitution pattern and equilibrium frequencies were correctly estimated.
Examples and validation of other general features
We ran this example over 200 generations and then analyze the output with the GenePop 4.0 program . As expected the SNPs were detected as independent. We then changed the value of recombination to 0 ('Rec' = 0) and then GenePop 4.0 tell us that the 10 SNPs are linked, as expected. Note the many possibilities that the program provides in the context of studying SNPs under complex evolutionary situations. We can define any number of populations under any user-defined migration model. We can set any number of SNPs with the desired linkage relationships. The SNPs can be set at distinct initial frequencies in the different populations, for example, 'SNPfreqs' at 1.0 and 0.0 defines the first population with allele 1 fixed and the second with allele 2 fixed.
Impact of recombination on estimation of positive selection
Impact of recombination on dN/dS estimation under a Jukes Cantor model.
1.02 ± 0.03
0.1 ± 0.05
1.06 ± 0.04
9.9 ± 0.56
1.01 ± 0.03
8.8 ± 0.49
0.3 ± 0.07
13.1 ± 0.77
12.7 ± 0.65
GenomePop has interesting characteristics for simulating SNPs or DNA sequences under complex models of evolution and demography. These features make it unique with respect to other simulation tools. Namely, the possibility of forward simulation under GTR mutation or GTR × MG94 codon models with intra-codon recombination, simulation of any user-defined migration pattern, diploid or haploid models, constant or variable population sizes, fitness-based selection, etc. Under the 2-allele model it allows the simulation of recombination hot-spots, the definition of different frequencies in different populations, etc. GenomePop can also manage large DNA fragments and has a scaling option to save computation time when simulating large sequences or population sizes under complex demographic and evolutionary situations. It has many other features that are detailed in the web page .
Project name: GenomePop v. 1.0
Project home page: http://webs.uvigo.es/acraaj/GenomePop.htm
Operating system(s): Windows and Linux (the source will be provided to compile for Mac)
Programming language: C++
License: GNU GPL.
I am grateful to A. Caballero, H. Quesada, S.T. Rodríguez-Ramilo and two anonymous reviewers for discussion and comments on the manuscript. I also want to thank Sergei L Kosakovsky Pond for his help with HYPHY. This work was supported by grant CPE03-004-C2 from Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and from Dirección Xeral de Investigación e Desenvolvemento from Xunta de Galicia. AC-R is currently funded by an Isidro Parga Pondal research fellowship from Xunta de Galicia (Spain).
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