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. 2019 Dec;68(1):e87.
doi: 10.1002/cpbi.87.

Population Genetic Inference With MIGRATE

Peter Beerli  1 , Somayeh Mashayekhi  1   2 , Marjan Sadeghi  1 , Marzieh Khodaei  1 , Kyle Shaw  1
Affiliations

Population Genetic Inference With MIGRATE

Peter Beerli et al. Curr Protoc Bioinformatics. 2019 Dec.

Abstract

Many evolutionary biologists collect genetic data from natural populations and then need to investigate the relationship among these populations to compare different biogeographic hypotheses. MIGRATE, a useful tool for exploring relationships between populations and comparing hypotheses, has existed since 1998. Throughout the years, it has steadily improved in both the quality of algorithms used and in the efficiency of carrying out those calculations, thus allowing for a larger number of loci to be evaluated. This efficiency has been enhanced, as MIGRATE has been developed to perform many of its calculations concurrently when running on a computer cluster. The program is based on the coalescence theory and uses Bayesian inference to estimate posterior probability densities of all the parameters of a user-specified population model. Complex models, which include migration and colonization parameters, can be specified. These models can be evaluated using marginal likelihoods, thus allowing a user to compare the merits of different hypotheses. The three presented protocols will help novice users to develop sophisticated analysis techniques useful for their research projects. © 2019 The Authors. Basic Protocol 1: First steps with MIGRATE Basic Protocol 2: Population model specification Basic Protocol 3: Prior distribution specification Basic Protocol 4: Model selection Support Protocol 1: Installing the program MIGRATE Support Protocol 2: Installation of parallel MIGRATE.

Keywords: Bayesian inference; DNA; MCMC; coalescent; divergence time; gene flow; microsatellite; population genetics.

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Figures

Figure 1
Figure 1
Default population model for three populations; all populations receive migrants from all others. Three different ways to represent the same population model are shown. The graph on the left represents each population in time, assuming gene flow (arrows) is recurrent among all of them; the middle graph represents only the interaction among populations without a time component; the matrix on the right is an adjacency matrix where the diagonal represents the population sizes and off‐diagonal represents immigration connections (more detail on this adjacency matrix in the Basic Protocol 2).
Figure 2
Figure 2
MIGRATE main menu.
Figure 3
Figure 3
Example of an output during the run of the program: the information block contains a time stamp; the prognosed time of completion; parameter acceptance ratios; and current parameter value. The Propwindow column describes the size of the proposal window in the Markov chain Monte Carlo (MCMC) run, AutoCorr is the autocorrelation among parameter values, and ESS is the effective sample size of the MCMC.
Figure 4
Figure 4
Example of histograms of the same mutation‐scaled immigration rates of three runs with different run length; the left run had set options that led to a runtime that is 100× shorter than the rightmost run. The leftmost histogram shows signs of problems.
Figure 5
Figure 5
Example models. (1) Three models: recurrent immigration, recurrent immigration after divergence, and divergence (from left to right). (2) to (5) Suggested steps to build up the adjacency matrix. The populations used for the data and these models are named Arbon (A), Berg (B), and Chur (C). In the rightmost column, A and B were pooled and considered a single population AB.
Figure 6
Figure 6
Copy of the Log marginal likelihood table as displayed in the outfile_short.pdf of the run that was carried out according to the instructions in Basic Protocol 1 using parmfile_short. A re‐run of the same data will lead to slightly different values.
Figure 7
Figure 7
The main table in the output of MIGRATE. This table was produced with Model 3 in Basic Protocol 2. The full table is available in the downloaded tutorial material at currentprotocols/basic_protocol2/example_results/outfile_model3.pdf.
See this image and copyright information in PMC

References

    1. Beerli, P. (1998). Estimation of migration rates and population sizes in geographically structured populations. In Carvalho G. (Ed.), Advances in molecular ecology, Volume 306 of NATO Science Series A: Life Sciences (pp. 39–53). Amsterdam: IOS Press.
    1. Beerli, P. (2006). Comparison of Bayesian and maximum likelihood inference of population genetic parameters. Bioinformatics, 22(3), 341–345. doi: 10.1093/bioinformatics/bti803. - DOI - PubMed
    1. Beerli, P. , & Felsenstein, J. (1999). Maximum‐likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics, 152(2), 763–773. - PMC - PubMed
    1. Beerli, P. , & Felsenstein, J. (2001). Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proceedings of the National Academy of Sciences of the United States of America, 98(8), 4563–4568. doi: 10.1073/pnas.081068098. - DOI - PMC - PubMed
    1. Beerli, P. , & Palczewski, M. (2010). Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics, 185(1), 313–326. doi: 10.1534/genetics.109.112532. - DOI - PMC - PubMed

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