Population genetic simulation: Benchmarking frameworks for non-standard models of natural selection

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dc.contributor.authorJohnson, O.L.
dc.contributor.authorTobler, R.
dc.contributor.authorSchmidt, J.M.
dc.contributor.authorHuber, C.D.
dc.date.issued2024
dc.description.abstractPopulation genetic simulation has emerged as a common tool for investigating increasingly complex evolutionary and demographic models. Software capable of handling high-level model complexity has recently been developed, and the advancement of tree sequence recording now allows simulations to merge the efficiency and genealogical insight of coalescent simulations with the flexibility of forward simulations. However, frameworks utilizing these features have not yet been compared and benchmarked. Here, we evaluate various simulation workflows using the coalescent simulator msprime and the forward simulator SLiM, to assess resource efficiency and determine an optimal simulation framework. Three aspects were evaluated: (1) the burn-in, to establish an equilibrium level of neutral diversity in the population; (2) the forward simulation, in which temporally fluctuating selection is acting; and (3) the final computation of summary statistics. We provide typical memory and computation time requirements for each step. We find that the fastest framework, a combination of coalescent and forward simulation with tree sequence recording, increases simulation speed by over twenty times compared to classical forward simulations without tree sequence recording, although it does require six times more memory. Overall, using efficient simulation workflows can lead to a substantial improvement when modelling complex evolutionary scenarios-although the optimal framework ultimately depends on the available computational resources.
dc.description.statementofresponsibilityOlivia L. Johnson, Raymond Tobler, Joshua M. Schmidt, Christian D. Huber
dc.identifier.citationMolecular Ecology Resources, 2024; 24(3):e13930-1-e13930-13
dc.identifier.doi10.1111/1755-0998.13930
dc.identifier.issn1755-098X
dc.identifier.issn1755-0998
dc.identifier.orcidJohnson, O.L. [0000-0001-8029-2397]
dc.identifier.orcidTobler, R. [0000-0002-4603-1473]
dc.identifier.orcidHuber, C.D. [0000-0002-2267-2604]
dc.identifier.urihttps://hdl.handle.net/2440/142026
dc.language.isoen
dc.publisherWiley
dc.relation.granthttp://purl.org/au-research/grants/arc/DE190101069
dc.relation.granthttp://purl.org/au-research/grants/arc/DP190103606
dc.rights© 2024 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
dc.source.urihttp://dx.doi.org/10.1111/1755-0998.13930
dc.subjectfluctuating selection
dc.subjectmsprime
dc.subjectpopulation genetic simulation
dc.subjectSLiM
dc.subjecttree sequence recording
dc.subject.meshGenetics, Population
dc.subject.meshModels, Genetic
dc.subject.meshComputer Simulation
dc.subject.meshSoftware
dc.subject.meshBenchmarking
dc.subject.meshSelection, Genetic
dc.titlePopulation genetic simulation: Benchmarking frameworks for non-standard models of natural selection
dc.typeJournal article
pubs.publication-statusPublished

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