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Table 1 Commonly used software for population structure inference

From: PSMIX: an R package for population structure inference via maximum likelihood method

Software

STRUCTURE

GENELAND

BAPS/BAPS2

ADMIXMAP

PARTITION

L-POP

PSMIX

Method

Bayesian MCMC

Bayesian MCMC

Bayesian, MCMC is used when the number of populations ≥ 9

Bayesian MCMC

Bayesian MCMC

Latent class analysis, EM

Clustering analysis, EM

Features

Population structure inference

Process geo-referenced individual multilocus genetic data for population structure inference

Population structure inference. Use geographical sampling design of the individuals

Mainly for analysis of datasets that consist of trait measurements and genotype data on a sample of individuals from an admixed or stratified population

Population structure inference

Population structure inference

Population structure inference

Assumptions

HWE and LE between loci

HWE, LE between loci, and spatial distribution of sub-populations

HWE and LE between loci

Ancestry state is the same at all loci within a compound locus on any gamete. Mating is not assortative for admixture in the population from which the parental gametes were drawn

HWE and LE between loci. The underlying population genetic model is appropriate for out-crossing diploid organisms.

HWE and LE between loci

HWE and LE between loci

Input parameters

Parameters for running MCMC, parameters for ancestry model and allele frequency model, and the number of populations

In addition to genetic and spatial data, the user must provide parameters for the maximum number of populations, the way geographical information is handled and the allele frequency model

When MCMC is used, need parameters for running MCMC

Parameters for running MCMC, allele frequencies (number of population is specified here), and mating model. Disease information (outcome variable is suggested even if focus on population structure). Parameters for tests and output

Parameters for running MCMC, maximum number of populations, prior parameter for allelic diversity, and prior parameter for number of populations

Number of populations, admixture option, data format options, model options, output format options, and convergence criterion

Number of populations and convergence criterion

Output

One file for estimates and some files for plots. Main parameter estimates are inferred ancestry of individuals and estimated allele frequencies in each population

Main parameter estimates are the number of populations, population membership of each individual, maps giving the population memberships of each geographical pixel of a given size to locate genetic discontinuities between populations

Main parameter estimates are the number of populations and population membership of each individual

Individual/gamete level admixture variables. Ancestry-specific allele or haplotype frequencies. Results for association analysis and model parameters

The output file contains a list of the parameter settings followed by the sequence of observations of the Markov chain. A companion program PartitionView is provided to obtain useful information from the PARTITION output file.

Main outputs are estimates of allele frequencies, posterior class probabilities, and class-specific allele frequencies

Main parameter estimates are inferred ancestry of individuals

Advantages

Easy to use. Once number of populations is given, the estimates are accurate

Easy to use. Flexible to extend. Can work with or without spatial information. Can estimate number of populations

Easy to use. Provide good estimate for number of populations. When geographical sampling information is applicable, can improve the statistical power to detect clusters in the data

In addition to population structure inference, can perform association analysis on structured populations. Can deal with tightly linked loci using haplotypes

Easy to use. Can estimate number of populations and calculate a Bayes factor in support of a single source population against the alternative of more than one source population.

Computationally efficient

Easy to use. Computationally efficient. Flexible to extend

Limitations

Computationally intensive. Can detect number of populations but does not work well.#

Does not handle admixture. Computationally intensive, especially when "Falush" is used as allele frequency model, or number of populations needs to be estimated.

Very memory intensive. When MCMC is used, becomes relatively computationally intensive. Only provides membership partition, does not handle admixture

Difficult to use. Computationally intensive. Does not estimate number of populations *

Computationally intensive, especially when number of populations needs to be estimated

Parameter configuration is difficult to use, works OK for discrete populations but not for admixed populations. Does not estimate number of populations

Does not estimate number of populations

Platforms

Windows, Unix/Linux

Windows/Linux/Mac (R package)

Windows

Windows, Unix/Linux. R statistical package is required

Windows

Windows (DOS), Unix/Linux

Windows/Linux/Mac (R package)

References

Pritchard et al. (2000), Falush et al. (2003)

Guillot et al. (2005)

Corander et al. (2003, 2004)

McKeigue et al. (2000), Hoggart et al. (2003, 2004)

Dawson and Belkhir (2001)

Purcell and Sham (2004)

Tang et al. (2005), Liu et al. (2005)

URL

http://pritch.bsd.uchicago.edu/structure.html

http://www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/Geneland.html

http://www.rni.helsinki.fi/~mjs

http://www.lshtm.ac.uk/ncdeu/genetics/#admix

http://www.genetix.univ-montp2.fr/partition/partition.htm#analyse_+exe

http://statgen.iop.kcl.ac.uk/lpop

http://bioinformatics.med.yale.edu/PSMIX

  1. # With the findings of Evanno et al. (2005), STRUCTURE's ability to detect number of populations should be improved greatly.
  2. * Only focus on the function of population structure inference.