Literature DB >> 25585533

MetaPopGen: an r package to simulate population genetics in large size metapopulations.

Marco Andrello1, Stéphanie Manel1.   

Abstract

Population genetics simulation models are useful tools to study the effects of demography and environmental factors on genetic variation and genetic differentiation. They allow for studying species and populations with complex life histories, spatial distribution and many other complicating factors that make analytical treatment impracticable. Most simulation models are individual-based: this poses a limitation to simulation of very large populations because of the limits in computer memory and long computation times. To overcome these limitations, we propose an intermediate approach that allows modelling of very complex demographic scenarios, which would be intractable with analytical models, and removes the limitations imposed by large population size, which affect individual-based simulation models. We implement this approach in a software package for the r environment, MetaPopGen. The innovative concept of this approach with respect to the other population genetic simulators is that it focuses on genotype numbers rather than on individuals. Genotype numbers are iterated through time by using random number generators for appropriate probabilistic distributions to reproduce the stochasticity inherent to Mendelian segregation, survival, dispersal and reproduction. Features included in the model are age structure, monoecious and dioecious (or separate sexes) life cycles, mutation, dispersal and selection. The model simulates only one locus at a time. All demographic parameters can be genotype-, sex-, age-, deme- and time-dependent. MetaPopGen is therefore indicated to study large populations and very complex demographic scenarios. We illustrate the capabilities of MetaPopGen by applying it to the case of a marine fish metapopulation in the Mediterranean Sea.
© 2015 John Wiley & Sons Ltd.

Keywords:  connectivity; dispersal; gene flow; simulation model; simulator; stochasticity

Mesh:

Year:  2015        PMID: 25585533     DOI: 10.1111/1755-0998.12371

Source DB:  PubMed          Journal:  Mol Ecol Resour        ISSN: 1755-098X            Impact factor:   7.090


  2 in total

1.  skelesim: an extensible, general framework for population genetic simulation in R.

Authors:  Christian M Parobek; Frederick I Archer; Michelle E DePrenger-Levin; Sean M Hoban; Libby Liggins; Allan E Strand
Journal:  Mol Ecol Resour       Date:  2016-11-16       Impact factor: 7.090

2.  Global gene flow releases invasive plants from environmental constraints on genetic diversity.

Authors:  Annabel L Smith; Trevor R Hodkinson; Jesus Villellas; Jane A Catford; Anna Mária Csergő; Simone P Blomberg; Elizabeth E Crone; Johan Ehrlén; Maria B Garcia; Anna-Liisa Laine; Deborah A Roach; Roberto Salguero-Gómez; Glenda M Wardle; Dylan Z Childs; Bret D Elderd; Alain Finn; Sergi Munné-Bosch; Maude E A Baudraz; Judit Bódis; Francis Q Brearley; Anna Bucharova; Christina M Caruso; Richard P Duncan; John M Dwyer; Ben Gooden; Ronny Groenteman; Liv Norunn Hamre; Aveliina Helm; Ruth Kelly; Lauri Laanisto; Michele Lonati; Joslin L Moore; Melanie Morales; Siri Lie Olsen; Meelis Pärtel; William K Petry; Satu Ramula; Pil U Rasmussen; Simone Ravetto Enri; Anna Roeder; Christiane Roscher; Marjo Saastamoinen; Ayco J M Tack; Joachim Paul Töpper; Gregory E Vose; Elizabeth M Wandrag; Astrid Wingler; Yvonne M Buckley
Journal:  Proc Natl Acad Sci U S A       Date:  2020-02-07       Impact factor: 11.205

  2 in total

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