Here you will find a summary of methods aiming at identifying population structure.
| Software | Type of method | Purpose | Specifics | Issues and warnings | Link | Reference |
|---|---|---|---|---|---|---|
| SPRelate | Multivariate analysis | Summarizing variance across loci and visualizing inter-individual genetic distance | Fast. Can use VCF files as an input | Requires careful interpretation (Jombard et al. 2009) | https://bioconductor.org/packages/release/bioc/html/SNPRelate.html | (Zheng et al. , 2012) |
| Eigenstrat/smartpca | Multivariate analysis | Summarizing variance across loci and visualizing inter-individual genetic distance | Fast. Can use VCF files as an input | Requires careful interpretation (Jombard et al. 2009) | https://github.com/DReichLab/EIG/tree/master/EIGENSTRAT | (Price et al. , 2006) |
| DAPC (adegenet) | Multivariate analysis/Clustering | Maximizes divergence between groups identified by PCA | Fast. Less sensitive to HWE assumptions. Claims to be more efficient than Structure | Requires careful interpretation (Jombard et al. 2009) | http://adegenet.r-forge.r-project.org/ | (Jombart et al. , 2010) |
| sPCA (adegenet) | Multivariate analysis/Clustering | Spatially explicit model to assess population structure | Spatially explicit and able to detect cryptic structure. Fast. | Does not take into account HW equilibrium or LD | http://adegenet.r-forge.r-project.org/ | (Jombart et al. , 2008) |
| BEDASSLE | Differentiation and MCMC model testing | Identifies contribution of environment and geographical distance to populations differentiation | Less biased than Mantel tests, provides tools for model testing | Uses population-level data. | https://cran.r-project.org/web/packages/BEDASSLE/index.html | (Bradburd et al. , 2013) |
| GENELAND | Clustering and characterizing admixture | Grouping individuals in spatially consistent clusters maximizing HW equilibrium | Takes into account spatial variation, supposed to detect weak structure, framed in R | Immigrant alleles are assumed to be found only in new immigrants | https://cran.r-project.org/web/packages/Geneland/ | (Guillot et al. , 2012) |
| sNMF | Clustering and characterizing admixture | Grouping individuals in clusters maximizing HW equilibrium and LD between loci | Fast (30X than ADMIXTURE) | Still slow computation time for large datasets | http://membres-timc.imag.fr/Olivier.Francois/snmf/index.htm | (Frichot et al. , 2014) |
| STRUCTURE | Clustering and characterizing admixture | Grouping individuals in clusters maximizing HW equilibrium and LD between loci | User friendly interface. Bayesian inference. | Slow for large datasets. Requires specific input format | http://pritchardlab.stanford.edu/structure.html | (Pritchard et al. , 2000) |
| FastSTRUCTURE | Clustering and characterizing admixture | Grouping individuals in clusters maximizing HW equilibrium and LD between loci | ~100X faster than Structure | Approximate inference of the original Structure model | http://rajanil.github.io/fastStructure/ | (Raj et al. , 2014) |
| ADMIXTURE | Clustering and characterizing admixture | Grouping individuals in clusters maximizing HW equilibrium and LD between loci | Maximum Likelihood, claimed to be faster than Structure. Note that it allows mixed ploidy (e.g. individuals that are haploids or diploids at a chromosome/locus depending on their sex can be analyzed jointly). | Often slower than its counterparts | https://www.genetics.ucla.edu/software/admixture/index.html | (Alexander and Novembre, 2009) |
| FineStructure/GlobeTrotter | Clustering and characterizing admixture | Chromosome painting, admixture and clustering | Estimates time since admixture, fast, specific tools for RAD-seq, set of scripts to facilitate analysis | Relies on Structure and fastStructure assumptions. Requires phased data. | http://paintmychromosomes.com/ | (Hellenthal et al. , 2014) |
| PCAdmix | Clustering and characterizing admixture | Chromosome painting | Fast, uses HMM to smooth out windows and limit noise due to low confidence ancestry | Requires a priori definition of ancestral populations and phased haplotypes | https://sites.google.com/site/pcadmix/ | (Brisbin et al. , 2012) |
| Splitstree | Phylogeny/Network | Network reconstruction and phylogenetic relationships | User friendly interface, proposes a variety of methods for networks reconstruction | Mostly descriptive | http://www.splitstree.org/ | (Huson and Bryant, 2006) |
| SNPhylo | Phylogeny | Network reconstruction and phylogenetic relationships | Complete pipeline from SNP filtering to tree reconstruction | Should be used on complex of species or divergent populations with little migration | http://chibba.pgml.uga.edu/snphylo/ | (Lee et al. , 2014) |
| RAxML | Phylogeny | Network reconstruction and phylogenetic relationships | Maximum Likelihood inference of phylogenetic relationships | Should be used on complex of species or divergent populations with little migration | http://sco.h-its.org/exelixis/web/software/raxml/index.html | (Stamatakis, 2014) |
| BEAST2 | Phylogeny | Network reconstruction and phylogenetic relationships | User friendly. Can be used to track changes in effective population sizes (Bayesian Skyline Plots). Possible to estimate divergence times | Slow for large datasets. Requires sequence data that can be produced by , e.g., Stacks for RAD-seq data | http://beast2.org/ | (Drummond and Rambaut, 2007; Bouckaert et al. , 2014) |
| PhyML | Phylogeny | Phylogenetic relationships | Maximum Likelihood inference of phylogenetic relationships. An online version is available | Should be used on complex of species or divergent populations with little migration | http://www.atgc-montpellier.fr/phyml/binaries.php | (Guindon et al. , 2010) |
| SNAPP | Phylogeny | Phylogenetic relationships | Handles SNP data | Remains slow for medium to large datasets (>1,000SNPs) | http://beast2.org/snapp/ | (Bryant et al. , 2012) |
| *BEAST | Phylogeny and species tree inference | Divergence time estimation and phylogenetic relationships | Outputs a species tree instead of concatenated gene tree. Allows for testing consistency between phylogenetic signals at different loci | Slow for large datasets. Requires sequence data. Not suited for situations where gene flow/admixture occurrs | http://beast2.org/ | (Heled and Drummond, 2010) |
| TREEMIX | Clustering and characterizing admixture | Admixture graph, infers most likely admixture events in a tree | Based on allele frequencies and can be used for pooled data. | Requires multiple runs to properly assess the likelihood of each model | https://bitbucket.org/nygcresearch/treemix/src | (Pickrell and Pritchard, 2012) |
| TWISST | Topology weighting | Chromosome painting, clustering and branching between populations | Retrieves the most likely coalescence pattern between several taxa along the genome. Can be seen as an extension of the ABBA/BABA test | Needs a priori grouping of individuals into taxa. Requires at least 4 taxa. Impractical for more than 6 taxa. Windows size must include enough SNPs to retrieve the correct topology but at the risk that regions with different histories are included | https://github.com/simonhmartin/twisst | (Martin and Van Belleghem, 2016) |
| LAMP | Pedigree, Identity by descent/state | Chromosome painting, relatedness | LAMP also allows for association and pedigree analyses | Identifies local ancestry in windows (source of noise), requires phased data | http://lamp.icsi.berkeley.edu/lamp/ | (Baran et al. , 2012) |
| PLINK | Pedigree, Identity by descent/state | Estimating inbreeding and relatedness | Allows studying identity by descent and by state. PLINK is a multi-purpose tool, facilitating data analysis within the same software | NA | http://pngu.mgh.harvard.edu/~purcell/plink/ | (Purcell et al. , 2007) |
| VCFTOOLS | Pedigree, Identity by descent/state | Estimating inbreeding and relatedness | Computes unadjusted Ajk and kinship coefficient | NA | https://vcftools.github.io/man_latest.html | (Danecek et al. , 2011) |
| KING | Pedigree, Identity by descent/state | Estimating inbreeding and relatedness, multivariate analysis | Mendelian error checking, testing family structure, highly accurate kinship coefficient, association analysis, population structure inference | Kinship coefficient also computed in VCFTOOLS | http://people.virginia.edu/~wc9c/KING/Download.htm | (Manichaikul et al. , 2010) |
| BAYPASS/Bayenv | Variance/covariance matrix | Building a population covariance matrix across population allele frequencies, similar to TREEMIX | Can handle pooled data | Matrices are mostly designed to provide a neutral model for assessing selection, but can be used to infer population structure | http://www1.montpellier.inra.fr/CBGP/software/baypass/ ; https://bitbucket.org/tguenther/bayenv2_public/src | (Günther and Coop, 2013; Gautier, 2015) |
| Arlequin | AMOVA (Analysis of MOlecular VAriance) | Characterizing hierarchical population structure | Arlequin allows for a variety of other analyses of diversity | Requires a priori assignment of individuals to populations, data formatting is required prior analysis | http://cmpg.unibe.ch/software/arlequin35/Arl35Downloads.html | (Excoffier and Lischer, 2010) |
| POPTREE2 | Genetic distance | Visualizing a matrix of pairwise differentiation statistics as a tree | Can be used for pooled datasets, several statistics can be used | Differentiation measures alone do not necessarily retrieve the actual history of populations | http://www.med.kagawa-u.ac.jp/~genomelb/takezaki/poptree2/index.html | (Takezaki et al. , 2010) |
| Stacks | Differentiation/Diversity/Phylogeny | Processing RAD-seq data and facilitate their analysis | Designed for RAD-seq data, variety of output formats for downstream analyses. Allows to retrieve DNA sequences for each locus | NA | http://catchenlab.life.illinois.edu/stacks/ | (Catchen et al. , 2011) |
| Popoolation/Popoolation2/Popoolation TE | Differentiation/Diversity | Extracting summary statistics from pooled data | Explicitely corrects for sampling bias in pooled data | Mostly limited to a few summary statistics. A pipeline dedicated to TE detection is also available | https://sourceforge.net/p/popoolation/wiki/Main/ | (Kofler, Orozco-terWengel, et al. , 2011; Kofler, Pandey, et al. , 2011) |
| POPGenome | Differentiation/Diversity/Recombination | Computing summary statistics based on AFS and LD along genomes | Accepts VCF and GFF/GFT files, efficient and fast. Tests for admixture available (ABBA BABA test). Includes basic coalescence simulations (ms and msms) | Mostly limited to summary statistics (but coalescent simulations are possible). No built-in SNP calling module | http://catchenlab.life.illinois.edu/stacks/ | (Pfeifer et al. , 2014) |
| ANGSD | Differentiation/Diversity/Recombination | Computing summary statistics based on AFS and LD along genomes | Able to process BAM files, built-in procedures for data filtering, admixture analysis | Mostly limited to summary statistics | https://github.com/ANGSD/angsd | (Korneliussen et al. , 2014) |
| Arlequin | Differentiation/Diversity/Recombination | Computing summary statistics based on AFS and LD along genomes | Can output AFS for further analysis in fastsimcoal2 | Slower than PopGenome, requires a private format | http://cmpg.unibe.ch/software/arlequin35/Arl35Downloads.html | (Excoffier and Lischer, 2010) |
| VCFTOOLS | Differentiation/Diversity/Recombination | Computing summary statistics based on AFS and LD along genomes | Fast. VCFTOOLS can also be used for SNP filtering | Less summary statistics than POPGenome | https://vcftools.github.io/man_latest.html | (Danecek et al. , 2011) |
| LDHat | Recombination | Estimating variation in recombination rates along a genome | Handles unphased and missing data, underlying model can be used for organisms such as viruses or bacteria | Limited to 300 sequences, private format, model for recombination hotspots based on human data | http://ldhat.sourceforge.net/ | (McVean et al. , 2002) |
| LDHot | Recombination | Identifying recombination hotspots | Specifically designed for detecting recombination hotspots | Requires data to be phased, working with LDHat | https://github.com/auton1/LDhot | (Myers, 2005) |
| Kimtree | Genetic distance | Estimating divergence time between populations and testing for topologies | The method is conditional on a prior topology provided by the user. It computes DIC for a given topology, allowing to test for the best one. | Times are given in diffusion time scale, and can be converted in demographic times using independent estimates of Ne. | http://www1.montpellier.inra.fr/CBGP/software/kimtree/index.html | (Gautier and Vitalis, 2013) |
| npstat | Differentiation/Diversity | Extracting summary statistics from pooled data | Explicitely corrects for sampling bias in pooled data. Allows computing tests using an outgroup (MK test, Fay and Wu's H) and characterizing coding mutations. | Mostly limited to summary statistics, but more complete than Popoolation. | https://github.com/lucaferretti/npstat | (Ferretti et al. 2013) |
| SVDQuartets | Phylogeny | Builds species trees using short non-recombining sequences | Coalescence-based. Suitable for short loci (e.g. RAD-seq and GBS) | See ASTRAL-2 and Chou et al. 2015 | http://www.stat.osu.edu/~lkubatko/software/SVDquartets/ | (Chifman and Kubatko, 2014) |
| ASTRAL-2 | Phylogeny | Builds species trees using short non-recombining sequences | Coalescence-based. Suitable for short loci (e.g. RAD-seq and GBS) | More reliable under high incomplete lineage sorting that SVDQuartets and NJst (Chou et al. 2015) | https://github.com/smirarab/ASTRAL | (Mirarab and Warnow, 2015) |
| NJst (in phybase) | Phylogeny | Builds species trees using short non-recombining sequences | Coalescence-based. Suitable for short loci (e.g. RAD-seq and GBS) | See ASTRAL-2 and Chou et al. 2015 | https://code.google.com/archive/p/phybase/downloads | (Liu and Yu, 2011) |
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