Methods to infer populations history

SoftwareClass of methodPurposeSpecificsIssues and warningsLinkReference
DsuiteABBA-BABAIdentifying past events of admixture between populationsFast, handles VCF format. Suited for low-depth sequencing (handles uncertainties on genotypes). Provides a set of summary statistics that are useful to investigate complex admixture eventsRequires an outgroup sequence. The methods cannot estimate the direction of gene flow.https://github.com/millanek/Dsuite(Malinsky et al., 2020)
RENT+Ancestral Recombination Graphs/coalescenceRetracing the whole process of recombination and coalescence along a genomeFaster than first version of ARGWeaver.Requires phased haplotypes. Specific input format. No built-in functions to extract information from genealogies.https://github.com/SajadMirzaei/RentPlus(Mirzaei and Wu, 2017)
TREEMIXClustering and characterizing admixtureAdmixture graph, infers most likely admixture events in a treeBased on allele frequencies and can be used for pooled data.Requires multiple runs to properly assess the likelihood of each modelhttps://bitbucket.org/nygcresearch/treemix/src(Pickrell and Pritchard, 2012)
G-PhoCSCoalescence/BayesianEstimating population divergence and migration parameters using a coalescent frameworkBayesian + MCMC, handles ancient samplesParameters scaled by mutation rate, no admixturehttp://compgen.cshl.edu/GPhoCS/(Gronau et al., 2011)
IMa3Coalescence/BayesianInferring parameters from an isolation with migration (IM) modelFully bayesian approach, can perform joint estimates of parameters in L-mode and test for nested models. Can estimate phylogenetic relationships and migration ratesIM model is the only one available. Discrete admixture cannot be tested. Can only use subsets of whole-genome resequencing data. Recent splits lead to overestimate migration rateshttps://github.com/jodyhey/IMa3(Hey and Nielsen, 2007)
ABLECoalescence/Composite LikelihoodModel comparison and parameters estimationUses both allele frequency spectrum and linkage disequilibrium within blocks of a pre-specified size.Relies on ms syntax. Determining the most informative size for blocks requires performing pilot runs.https://github.com/champost/ABLE(Beeravolu et al., 2016)
Stairway2Coalescence/Composite LikelihoodInferring change in Ne with timeUser-friendly. Fast. Suitable for pools or low-depth sequencing.Cannot handle migration or population splits.https://github.com/xiaoming-liu/stairway-plot-v2(Liu and Fu, 2020)
fastsimcoal2Coalescence/LikelihoodModel comparison and parameters estimationPerforms coalescent simulations, parameter estimation and model testing using a fast likelihood method. Can handle arbitrarily complex scenarios for any type of markerThe maximum-likelihood method only uses the allele frequency spectrum. Several runs (20-100) are needed to explore the likelihood space.http://cmpg.unibe.ch/software/fastsimcoal2/(Excoffier et al., 2013)
∂a∂iDiffusion approximation of the AFSModel comparison and parameters estimationRun time does not depend on the number of SNPs included, does not require coalescent simulations, handles arbitrarily complex scenarios. Fast estimation of confidence intervals around parameters estimates (Godambe method). Suitable for pools/low-depth sequencingrequires some knowledge of Python. Limited to 3 populations. Several runs (20-100) are needed to explore the likelihood space.https://bitbucket.org/gutenkunstlab/dadi(Gutenkunst et al., 2009)
momentsDiffusion approximation of the AFSModel comparison and parameters estimationBased on Python, syntax similar to ∂a∂i. Can handle selection. Can use VCF files as input.Requires some knowledge of Python. Limited to 5 populations. Several runs (20-100) are needed to explore the likelihood space.https://bitbucket.org/simongravel/moments/src/master/(Jouganous et al., 2017)
momi2Diffusion approximation of the AFSModel comparison and parameters estimationCan scale to ten populations. Can simulate and read data in the VCF format. Detailed tutorials availableDoes not handle continuous gene flowhttps://github.com/popgenmethods/momi2(Kamm et al., 2020)
KIMTreeDiffusion approximation/BayesianEstimating divergence time between populations and testing for topologies. Estimate divergence times and past effective sex-ratio along branches of a populations tree.Fast and user-friendly. R scripts to obtain plots are available. Suitable for pools/low-depth sequencing. 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.Strong selection on the sex chromosome can produce male-biased sex-ratios. 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/download.html(Clemente et al., 2018)
GADMAGenetic algorithmModel comparison and parameters estimationBased on moments and ∂a∂i. Automates the search for the best set of models explaining a given frequency spectrum.Limited to three populations at the moment.https://github.com/ctlab/GADMA(Noskova et al., 2020)
DoRISIdentity by Descent (IBD) tractTesting various demographic scenarioUses variation in IBD tracts length to test for various demographic models.IBD must be inferred first with, e.g., BEAGLE. Handles a limited set of demographic scenarios. Modification in the code is required for more complex scenarioshttps://github.com/pierpal/DoRIS(Palamara and Pe’er, 2013)
Unnamed.Identity by state (IBS) tractPredict observed patterns of Identity by state along a genome by fittingan appropriate, arbitrary complex demographic modelAllows bootstrapping and estimating confidence over parameter estimates with msSpecific input format (similar to MSMC or ARGWeaver)https://github.com/kelleyharris/Inferring-demography-from-IBS(Harris and Nielsen, 2013)
ASTRAL-2PhylogenyBuilds species trees using short non-recombining sequencesCoalescence-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)
BEAST2PhylogenyNetwork reconstruction and phylogenetic relationshipsUser friendly. Can be used to track changes in effective population sizes (Bayesian Skyline Plots). Possible to estimate divergence timesSlow for large datasets. Requires sequence data that can be produced by , e.g., Stacks for RAD-seq datahttp://beast2.org/(Drummond and Rambaut, 2007; Bouckaert et al., 2014)
IQ-Tree 2PhylogenyDivergence time estimation and phylogenetic relationshipsUser-friendly, can be run locally or on a webserver, very detailed tutorials. Fast and accurate.Still no tutorial for analyzing big data (last checked December 2020).http://www.iqtree.org/(Minh et al., 2020)
MCMCTree and MCMCTreeRPhylogenyDivergence time estimation and phylogenetic relationshipsIncluded in PAML. A R program is designed to help choosing relevant priors and interpreting results https://github.com/PuttickMacroevolution/MCMCtreeRBayesian, sensitive to priors. Requires a resolved phylogeny and an alignment. Slow for large datasets. Not suited for recent divergence and high gene flow.http://abacus.gene.ucl.ac.uk/software/paml.html (Yang, 2007; Puttick, 2019)
NJstPhylogenyBuilds species trees using short non-recombining sequencesAvailable in the R package phybase. Estimates populations/species tree from gene treesRequires splitting part of the genome into non-recombining "loci".https://github.com/bomeara/phybase/(Liu and Yu, 2010, 2011)
PHRAPLPhylogenyAdmixture graph, reticulated evolutionUses trees in the NEWICK format as an input to infer topology, migration rates, divergence times. Similar to ABC in spirit, using tree topology as a summary statistics.Cannot handle more than 16 taxa at a time, and requires subsetting larger datasetshttp://www.phrapl.org/(Jackson et al., 2017)
PhyMLPhylogenyPhylogenetic relationshipsMaximum Likelihood inference of phylogenetic relationships. An online version is availableShould be used on complex of species or divergent populations with little migration. Can be ran on genomic windows to detect introgression (with e.g. TWISST, Dsuite)http://www.atgc-montpellier.fr/phyml/binaries.php(Guindon et al., 2010)
RAxMLPhylogenyNetwork reconstruction and phylogenetic relationshipsMaximum Likelihood inference of phylogenetic relationshipsShould be used on complex of species or divergent populations with little migrationhttp://sco.h-its.org/exelixis/web/software/raxml/index.html(Stamatakis, 2014)
SNAPPPhylogenyPhylogenetic relationshipsHandles SNP dataRemains slow for medium to large datasets (>1,000SNPs)http://beast2.org/snapp/(Bryant et al., 2012)
SNPhyloPhylogenyNetwork reconstruction and phylogenetic relationshipsComplete pipeline from SNP filtering to tree reconstructionShould be used on complex of species or divergent populations with little migrationhttp://chibba.pgml.uga.edu/snphylo/(Lee et al., 2014)
SVDQuartetsPhylogenyPhylogenetic relationshipsEstimates populations/species tree from gene treesRemains slow for large datasets. Requires PAUP*.https://www.asc.ohio-state.edu/kubatko.2/software/SVDquartets/(Chifman and Kubatko, 2014)
SVDQuestPhylogenyPhylogenetic relationshipsEstimates populations/species tree from gene treesFaster than SVDQuartetshttps://github.com/pranjalv123/SVDquest(Vachaspati and Warnow, 2018)
*BEASTPhylogeny and species tree inferenceDivergence time estimation and phylogenetic relationshipsOutputs a species tree instead of concatenated gene tree. Allows for testing consistency between phylogenetic signals at different lociSlow for large datasets. Requires sequence data. Not suited for situations where gene flow/admixture is importanthttp://beast2.org/(Heled and Drummond, 2010)
SplitstreePhylogeny/NetworkNetwork reconstruction and phylogenetic relationshipsUser friendly interface, proposes a variety of methods for networks reconstructionMostly descriptivehttp://www.splitstree.org/(Huson and Bryant, 2006)
diCal2Sequentially Markovian coalescentTesting any arbitrary demographic scenarioWorks with smaller, more fragmented datasets than PSMC. Handles more complex demographic models than MSMC (including admixture).Requires phased whole genome data and a model to be definedhttps://sourceforge.net/projects/dical2/(Sheehan et al., 2013)
MSMC and MSMC-IMSequentially Markovian coalescentInferring change in Ne and migration rates with time between two populationsAllows to track population size changes in time without a priori. Allows estimating variation in cross-coalescence rate between two populationsLimited to the study of 8 diploid individuals from 2 populations at once. Requires whole genome phased data and masking regions with insufficient sequencing depthhttps://github.com/stschiff/msmc and https://github.com/wangke16/MSMC-IM(Schiffels and Durbin, 2014)
PSMCSequentially Markovian coalescentInferring change in effective population sizes (Ne) with time using a single diploid genomeAllows to track population size changes in time without a priori.Limited to one population and one diploid individual. Better used within MSMC. Requires phased whole genome data and masking regions with insufficient sequencing depthhttps://github.com/lh3/psmc(Li and Durbin, 2011)
SMC++Sequentially Markovian coalescentInferring change in Ne with time and splitting time between two populationsCan analyze hundreds of individuals at a time and does not require phasingMasking regions as in MSMC. The ancestral allele is assumed to be the reference allele by default. Assumes a clean split for populations divergence. Future versions should allow gene flow inference.https://github.com/popgenmethods/smcpp(Terhorst et al., 2016)
TWISSTTopology weightingChromosome painting, clustering and branching between populationsRetrieves the most likely coalescence pattern between several taxa along the genome. Can be seen as an extension of the ABBA/BABA testNeeds 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 includedhttps://github.com/simonhmartin/twisst(Martin and Van Belleghem, 2016)
BAYPASS/BayenvVariance/covariance matrixBuilding a population covariance matrix across population allele frequencies, similar to TREEMIXCan handle pooled dataMatrices are mostly designed to provide a neutral model for assessing selection, but can be used to infer population structurehttp://www1.montpellier.inra.fr/CBGP/software/baypass/ ; https://bitbucket.org/tguenther/bayenv2_public/src(Günther and Coop, 2013; Gautier, 2015)

References

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