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Computer programs for population genetics data analysis: a survival guide

Nature Reviews Genetics volume 7, pages 745–758 (2006)Cite this article

Key Points

  • Computer programs are now essential for the analysis of large population genetics data sets that are increasingly being generated.

  • We review 24 such programs here, and list their main features, limitations and some of their underlying assumptions.

  • Several user-friendly programs provide methods to compute standard genetic-diversity indices from various types of marker, as well as to test genetic structure, linkage disequilibrium and selective neutrality within populations.

  • Several programs that use multilocus genotype information have recently focused on individuals rather than on populations, and provide ways to delineate populations, detect new immigrants and their population of origin, and estimate individual admixture coefficients.

  • New coalescent-based programs provide powerful methods to estimate demographic parameters; however, these parameters have been developed under specific evolutionary models, and the accuracy of the results also depends on the convergence of the programs.

  • Most programs are based on many well-documented assumptions that need to be integrated by their users for a sound interpretation of the results.

  • Proper genetic data analyses should start with generalist packages to uncover the basic properties of the data, and be followed by the use of specialized methodologies to address more specific questions.

  • An important limitation of the wider use of sophisticated programs is the lack of a generic population genetics format, which would allow data to be easily exchanged between programs.

Abstract

The analysis of genetic diversity within species is vital for understanding evolutionary processes at the population level and at the genomic level. A large quantity of data can now be produced at an unprecedented rate, requiring the use of dedicated computer programs to extract all embedded information. Several statistical packages have been recently developed, which offer a panel of standard and more sophisticated analyses. We describe here the functionalities, special features and assumptions of more than 20 such programs, indicate how they can interoperate, and discuss new directions that could lead to improved software and analyses.

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Figure 1: Flow chart of possible data exchange between different population genetics programs.

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Acknowledgements

We are grateful to P. Beerli for providing an illustration from Migrate's manual. We also thank him, as well as O. Gaggiotti, J. Goudet and A. Estoup, for suggestions and comments on an early version of this manuscript. We are indebted to three reviewers for their comments. We apologize to the authors of programs which, owing to space constraints, we have not been able to cover here. The work in L.E.'s laboratory is partially supported by a grant from the Swiss National Science Foundation.

Author information

Authors and Affiliations

  1. Computational and Molecular Population Genetics (CMPG) Laboratory, Zoological Institute, University of Berne, Baltzerstrasse 6, Berne, 3012, Switzerland

    Laurent Excoffier & Gerald Heckel

Authors
  1. Laurent Excoffier
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  2. Gerald Heckel
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Correspondence to Laurent Excoffier.

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Related links

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FURTHER INFORMATION

Berne's CMPG (Computational and Molecular Population Genetics) programs

Bob Griffith's GeneTree program

Bruce Rannala's programs

Computational and Molecular Population Genetics Laboratory homepage

Gil McVean's programs

Giorgio Bertorelle's programs

Ian Wilson's programs

Jerôme Goudet's programs

Jinliang Wang's programs

Jody Hey's programs

Jonathan Pritchard's programs

Kent Holsinger's programs

Louis Bernatchez's laboratory links and programs

Mark Beaumont's programs

Matthew Stephens's programs

Montgomery Slakin's programs

Montpellier's CBGP (Centre de Biologie et de Gestion des Populations) programs

Montpellier's Genome Populations Interactions Adaptation laboratory's programs

Noah Rosenberg's programs

Oxford Evolutionary Biology Group's programs

Oxford's Mathematical Genetics programs

Peter Andolfatto's programs

Rasmus Nielsen's programs

Richard Hudson's programs

Ziheng Yang's programs

Glossary

Linkage disequilibrium

The non-random association of alleles at different loci.

Gametic phase

In a diploid individual, it represents the original allelic combinations that an individual received from its parents. It is therefore a particular association of alleles at different loci on the same chromosome, which is often unknown.

Selective neutrality

Null model of evolution that assumes that all the alleles observed at given locus are functionally equivalent.

Bayesian

Inference framework, based on the work of Thomas Bayes (1702–1761), in which the posterior probability of a parameter depends explicitly on its prior probability, reflecting some previous belief about this parameter.

Short tandem repeat (or microsatellite)

A class of repetitive DNA that is made up of repeats that are 2–5 nucleotides in length. The number of these repeats is usually extremely variable in a population.

Homoplasic mutations

Mutations that lead to identical character states (identity-in-state) despite having occurred by different evolutionary processes.

Coalescent (theory)

A theory that describes the structure of the genealogy of a sample of genes from present time to their most recent common ancestor. For neutral genes, this genealogy is extremely variable but only depends on the past demography (deme sizes and immigration rates) of the population.

Maximum-likelihood estimation

Inference technique in which the estimated parameters of a model are those that maximize the probability of the data under that model.

Hardy–Weinberg equilibrium

(HWE). Fit between the observed frequencies of the different genotype categories and the frequencies that are expected under random mating in an ideal population. Departure from HWE can also be due to selection, migration or hidden population subdivision.

F-statistics

Statistics that measure the correlation between genes drawn at different levels of a (hierarchically) subdivided population. This correlation is influenced by several evolutionary forces, such as mutation and migration, but it was originally designed to measure how far populations had gone in the process of fixation owing to genetic drift.

Hierarchical analyses of genetic variance

Analysis in which genetic diversity is hierarchically organized, with subunits nested in larger units (for example, genes in diploid individuals drawn from demes belonging to a subdivided population).

Mantel test

Test designed to measure the association between the elements of two matrices, by taking into account the autocorrelation that exists between the elements of each matrix. It is often used to test for a significant association between genetic and geographical distances.

Mismatch distribution

The distribution of the number of differences (mismatches) between pairs of DNA sequences in a sample. The exact shape of this distribution is affected by the past demography of a population.

Infinite-sites model

A mutation model according to which each new mutation occurs at a site that has not mutated before. This model was originally developed for protein- and DNA-sequence evolution, and is obviously related to the infinite allele model.

Infinite-allele mutation model

A mutation model according to which each new mutation produces an allele that has not previously existed.

Summary statistics

In the current genetic context, these are descriptive statistics summarizing the pattern of genetic diversity, such as the level of heterozygosity or the number of alleles per locus.

D

A measure of linkage disequilibrium defined as the difference between the frequency of a two-locus haplotype and the product of the frequencies of its constituent alleles (Dij = pij pipj).

D′

A standardized version of D that is obtained by dividing D by its maximum possible value given the allele frequencies (D′ = D/Dmax).

Tajima's D

Statistic used in a selective neutrality test to decide whether the mean number of differences between pairs of DNA sequences is compatible with the observed number of segregating sites in a sample.

Likelihood (of a model)

The probability of the data under a given model defined by a particular set of parameter values.

Joint posterior distribution

When a model is defined by more than one parameter, it is the posterior distribution of all possible combinations of parameter values.

Effective population size

The size of a virtual, randomly mating, stationary and isolated population that would have the same amount and type of polymorphisms as the population under study.

Finite-island model

A conceptual model for gene flow under which a finite number of demes exchange migrants with each other. The spatial location of the populations is not specified, and the constituent demes are usually assumed to have the same size and to exchange migrants at the same rate.

F ST

A measure of the level of population genetic differentiation, which usually reflects the proportion of total genetic variability that is due to the net differences between populations (see F-statistics).

Balancing selection

A form of natural selection that maintains polymorphism within populations.

AFLP

Amplified fragment length polymorphism. A method for the selective PCR amplification of anonymous, dominant DNA polymorphisms using restriction enzymes and DNA linkers.

Ascertainment bias

Systematic bias introduced by the criteria used to select individuals and/or genetic markers to be analysed (for example, choosing SNPs with heterozygosity that is higher than a given threshold).

Selective sweep

Drastic reduction of the genetic diversity along a chromosomal segment as a consequence of the fixation of an advantageous mutation by selection in that region.

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Excoffier, L., Heckel, G. Computer programs for population genetics data analysis: a survival guide. Nat Rev Genet 7, 745–758 (2006). https://doi.org/10.1038/nrg1904

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