Table 1.

Commonly used software for population structure inference

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

^# With the findings of Evanno et al. (2005), STRUCTURE's ability to detect number of populations should be improved greatly.

* Only focus on the function of population structure inference.