Computer simulations of genetic polymorphisms complement analytical approaches for investigating complex evolutionary, epidemiological and ecological scenarios for both applied and theoretical purposes. Current uses include human evolutionary history, understanding the genetic bases of complex diseases, genetic epidemiology, conservation management, plant breeding and infection spread.
Dozens of software packages are now available. Some are highly flexible and widely applicable, whereas others address specific tasks, such as case–control studies or continuous populations on dynamic landscapes.
Some key features include the ability to: simulate thousands of markers over complex evolutionary histories; create realistic patterns of recombination; model, in detail, a species' life history and mating patterns; integrate selective forces on simple and complex traits; monitor perturbations such as bottlenecks and admixture; and simulate samples from museum specimens and ancient DNA.
We can divide the uses of simulators into probability-based prediction, making statistical inferences and validating new methods or statistics. Simulations fill various other roles: teaching of genetic concepts, planning of surveys for the collection of genetic samples and post hoc power analysis of data.
The steps to follow in implementing a simulation-based study are straightforward, but they do require key user decisions, such as choosing parameters, deciding run length, picking statistics to summarize the data and comparing models to each other or to real data.
A crucial step is deciding on an appropriate simulator and, as all simulators have weaknesses, the user must balance selective, demographic, genomic and historical complexity. We provide guidance on these aspects and explain the differences between forward and backward approaches.
In general, forward simulators can model life history (such as mating and age structure) and selection (including trait-based or sexual selection) in much more detail, whereas backward simulators are faster and do not require setting initial genetic conditions. A few simulators are currently used most frequently, but the wide range of options and capabilities that are available means that users can and should match a simulator to their study needs.
It is important to note that many users will find a simulation package that is 'ready to use' for their analysis, but some simulation projects will require programming skills to integrate the simulator into a bioinformatics pipeline. We also advise that users should carefully plan their simulations, keeping in mind potential limitations or model violations.
The future of simulators is certainly bright. Emerging areas of improvement include building increased ecological and landscape realism, connecting genetic simulators to other models, including infection spread or climate change, identifying appropriate and informative summary statistics, recreating biases from next-generation sequence data and increasing efficiency and accessibility.
Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes whose interactions cannot be analytically predicted. Simulations have traditionally been used in population genetics by a fairly small community with programming expertise, but the recent availability of dozens of sophisticated, customizable software packages for simulation now makes simulation an accessible option for researchers in many fields. The in silico genetic data produced by simulations, along with greater availability of population-genomics data, are transforming genetic epidemiology, anthropology, evolutionary and population genetics and conservation. In this Review of the state-of-the-art of simulation software, we identify applications of simulations, evaluate simulator capabilities, provide a guide for their use and summarize future directions.
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This work was supported by the European Project CONGRESS funded by the European Union under FP7. We also thank E. Anderson and two anonymous referees for very helpful suggestions, as well as all of the software developers (see the full list in the Supplementary information) for their assistance in checking the information presented in Table 1 and in Supplementary information S1,S2 (tables).
The authors declare no competing financial interests.
Basic information about simulators we examined, input/output information, links, and published journal articles for reference (PDF 228 kb)
Brief, general descriptions of programs using direct quotes from original journal article and/or program manual (PDF 121 kb)
Human-mediated supplementation of a native population with translocated or captive-bred individuals to increase population size or growth rates.
- Stepwise mutation model
A mutation model in which the allelic states produced by mutation depend on the initial state of an allele. The basic version assumes mutations between adjacent states, but other versions allow larger mutational changes. This model is commonly used to model the microsatellite mutation process.
- Infinite alleles model
A model in which each mutational event creates a new allele that is unlike any other that is currently present in the population.
- Coalescent theory
A theory describing the genealogy of chromosomes or genes. The genealogy is constructed backwards-in-time, starting with the present-day sample. Lineages coalesce until the most recent common ancestor of the sample is reached.
- Parametric bootstrap confidence intervals
These measure the accuracy of sample estimates using a bootstrapping approach where a parametric model is fitted to the data, and samples of parameter values are drawn from this fitted model.
- Population viability analysis
(PVA). A probability-based modelling approach for assessing the future potential (such as reproduction and extinction) of populations or species.
Wright's inbreeding coefficient, measuring the level of correlation between two genes drawn from an individual relative to two genes drawn from the population. Also defined as the probability that two alleles in an individual are both descended from a single allele in an ancestor.
- Summary statistics
Numerical values for summarizing the characteristics of a genetic data set; these often summarize features such as variability (number of alleles) or population differentiation (FST).
A scientific paradigm that uses probability as a means of quantifying the analyst's knowledge or uncertainty concerning the model and/or its parameters, given the data observed. Given a particular model described by a likelihood function, the approach involves choosing a prior distribution and then updating this with the information provided by the observed data.
- Most recent common ancestor
In the case of a sample of genes, this is the most recent gene from which all alleles in the sample are directly descended.
- Number of segregating sites
The number of polymorphic sites in a sample of homologous DNA sequences. It measures the degree of DNA sequence variation that is present in the sample.
- Assignment tests
A broad category of methods whose goal is to determine with a degree of confidence the population of origin of individuals using genetic data.
The random mating of individuals within a breeding population.
- Genome scan
Large-scale genotyping (thousands of markers) that is usually used to detect outliers such as regions of the genome under selection.
- Prior distributions
The probability distributions of parameter values before observing the data. They reflect the observer's knowledge about what values the model parameters might take before having seen the data.
- Posterior distributions
The conditional distributions of the parameter given the observed data. They reflect both the likelihood of the data and the prior distribution. They represent what we know about the model parameters, having observed the data.
- Bayes factors
The relative odds that the hypothesis is true before and after examining the data. Calculated as the ratio of the prior probabilities of the null hypothesis versus the alternative hypothesis over the ratio of the posterior probabilities.
- Deviance information criteria
(DIC). A method of model comparison or selection in which increased fit owing to addition of terms is balanced by a penalty for each additional term.
- Carrying capacities
The maximum population size of a species that a habitat can sustain. It is determined by availability of space and resources.
The interbreeding of individuals issued from two or more distinct populations or species.
- Wright's island model
A population-genetics model in which all populations are of equal size and contribute equally to a global migrant pool, from which each population draws an equal proportion of immigrants each generation.
- Hierarchical island model
A variation on Wright's island model in which local sets of populations are connected to each other by a relatively high migration rate and to other local sets of populations by a relatively low rate. They are well-suited to modelling species that are distributed over several continents.
- Geographic information system
(GIS). A collection of spatially referenced data, such as geographical and altitudinal coordinates of individuals.
- k allele model
A mutation model in which each allele can mutate to any of the other k – 1 possible alleles with equal probability.
- Sequential Markov coalescent
A simplified genealogical process that aims to capture the essential features of the full coalescent model with recombination while being scalable in the number of loci. Computation time is saved by only accounting for coalescence between lineages without overlapping ancestral material.
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Hoban, S., Bertorelle, G. & Gaggiotti, O. Computer simulations: tools for population and evolutionary genetics. Nat Rev Genet 13, 110–122 (2012). https://doi.org/10.1038/nrg3130
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