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slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes

View ORCID ProfileMartin Petr, View ORCID ProfileBenjamin C. Haller, View ORCID ProfilePeter L. Ralph, View ORCID ProfileFernando Racimo
doi: https://doi.org/10.1101/2022.03.20.485041
Martin Petr
1Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Denmark
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  • For correspondence: mp@bodkan.net
Benjamin C. Haller
2Department of Computational Biology, Cornell University, Ithaca, NY, USA
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Peter L. Ralph
3Institute of Ecology and Evolution, University of Oregon, Eugene, OR, USA
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Fernando Racimo
1Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Denmark
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Abstract

One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called slendr (www.slendr.net), which leverages a SLiM simulation back end bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene flow events between populations using a straightforward declarative language. Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree sequence processing and analysis, slendr opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the slendr R package and demonstrate its features on several practical example workflows.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://www.slendr.net/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 21, 2022.
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slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
Martin Petr, Benjamin C. Haller, Peter L. Ralph, Fernando Racimo
bioRxiv 2022.03.20.485041; doi: https://doi.org/10.1101/2022.03.20.485041
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slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
Martin Petr, Benjamin C. Haller, Peter L. Ralph, Fernando Racimo
bioRxiv 2022.03.20.485041; doi: https://doi.org/10.1101/2022.03.20.485041

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