Literature DB >> 30565882

Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes.

Benjamin C Haller1, Jared Galloway2, Jerome Kelleher3, Philipp W Messer1, Peter L Ralph2.   

Abstract

There is an increasing demand for evolutionary models to incorporate relatively realistic dynamics, ranging from selection at many genomic sites to complex demography, population structure, and ecological interactions. Such models can generally be implemented as individual-based forward simulations, but the large computational overhead of these models often makes simulation of whole chromosome sequences in large populations infeasible. This situation presents an important obstacle to the field that requires conceptual advances to overcome. The recently developed tree-sequence recording method (Kelleher, Thornton, Ashander, & Ralph, 2018), which stores the genealogical history of all genomes in the simulated population, could provide such an advance. This method has several benefits: (1) it allows neutral mutations to be omitted entirely from forward-time simulations and added later, thereby dramatically improving computational efficiency; (2) it allows neutral burn-in to be constructed extremely efficiently after the fact, using "recapitation"; (3) it allows direct examination and analysis of the genealogical trees along the genome; and (4) it provides a compact representation of a population's genealogy that can be analysed in Python using the msprime package. We have implemented the tree-sequence recording method in SLiM 3 (a free, open-source evolutionary simulation software package) and extended it to allow the recording of non-neutral mutations, greatly broadening the utility of this method. To demonstrate the versatility and performance of this approach, we showcase several practical applications that would have been beyond the reach of previously existing methods, opening up new horizons for the modelling and exploration of evolutionary processes.
© 2018 John Wiley & Sons Ltd.

Entities:  

Keywords:  background selection; coalescent; genealogical history; pedigree recording; selective sweeps; tree sequences

Mesh:

Year:  2019        PMID: 30565882      PMCID: PMC6393187          DOI: 10.1111/1755-0998.12968

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


  31 in total

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Authors:  John Wakeley; Léandra King; Bobbi S Low; Sohini Ramachandran
Journal:  Genetics       Date:  2012-01-10       Impact factor: 4.562

2.  A separation-of-timescales approach to the coalescent in a continuous population.

Authors:  Jon F Wilkins
Journal:  Genetics       Date:  2004-12       Impact factor: 4.562

3.  Convergence to the island-model coalescent process in populations with restricted migration.

Authors:  Frederick A Matsen; John Wakeley
Journal:  Genetics       Date:  2005-10-11       Impact factor: 4.562

4.  SLiM: simulating evolution with selection and linkage.

Authors:  Philipp W Messer
Journal:  Genetics       Date:  2013-05-24       Impact factor: 4.562

5.  Solving the paradox of stasis: squashed stabilizing selection and the limits of detection.

Authors:  Benjamin C Haller; Andrew P Hendry
Journal:  Evolution       Date:  2013-10-16       Impact factor: 3.694

6.  Evolutionary branching in complex landscapes.

Authors:  Benjamin C Haller; Rupert Mazzucco; Ulf Dieckmann
Journal:  Am Nat       Date:  2013-08-20       Impact factor: 3.926

7.  Distortion of genealogical properties when the sample is very large.

Authors:  Anand Bhaskar; Andrew G Clark; Yun S Song
Journal:  Proc Natl Acad Sci U S A       Date:  2014-01-27       Impact factor: 11.205

8.  The founding of Mauritian endemic coffee trees by a synchronous long-distance dispersal event.

Authors:  M D Nowak; B C Haller; A D Yoder
Journal:  J Evol Biol       Date:  2014-05-03       Impact factor: 2.411

9.  Pervasive adaptive protein evolution apparent in diversity patterns around amino acid substitutions in Drosophila simulans.

Authors:  Shmuel Sattath; Eyal Elyashiv; Oren Kolodny; Yosef Rinott; Guy Sella
Journal:  PLoS Genet       Date:  2011-02-10       Impact factor: 5.917

10.  Genome-wide signals of positive selection in human evolution.

Authors:  David Enard; Philipp W Messer; Dmitri A Petrov
Journal:  Genome Res       Date:  2014-03-11       Impact factor: 9.043

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  36 in total

1.  A Fast and Simple Method for Detecting Identity-by-Descent Segments in Large-Scale Data.

Authors:  Ying Zhou; Sharon R Browning; Brian L Browning
Journal:  Am J Hum Genet       Date:  2020-03-12       Impact factor: 11.025

2.  Local PCA Shows How the Effect of Population Structure Differs Along the Genome.

Authors:  Han Li; Peter Ralph
Journal:  Genetics       Date:  2018-11-20       Impact factor: 4.562

3.  Evolutionary Modeling in SLiM 3 for Beginners.

Authors:  Benjamin C Haller; Philipp W Messer
Journal:  Mol Biol Evol       Date:  2019-05-01       Impact factor: 16.240

Review 4.  Host-parasite co-evolution and its genomic signature.

Authors:  Dieter Ebert; Peter D Fields
Journal:  Nat Rev Genet       Date:  2020-08-28       Impact factor: 53.242

5.  Background Selection Does Not Mimic the Patterns of Genetic Diversity Produced by Selective Sweeps.

Authors:  Daniel R Schrider
Journal:  Genetics       Date:  2020-08-26       Impact factor: 4.562

6.  Genetic Signatures of Evolutionary Rescue by a Selective Sweep.

Authors:  Matthew M Osmond; Graham Coop
Journal:  Genetics       Date:  2020-05-12       Impact factor: 4.562

7.  A numerical framework for genetic hitchhiking in populations of variable size.

Authors:  Eric Friedlander; Matthias Steinrücken
Journal:  Genetics       Date:  2022-03-03       Impact factor: 4.562

Review 8.  From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection.

Authors:  Hussein A Hejase; Noah Dukler; Adam Siepel
Journal:  Trends Genet       Date:  2020-01-15       Impact factor: 11.639

9.  The timing of human adaptation from Neanderthal introgression.

Authors:  Sivan Yair; Kristin M Lee; Graham Coop
Journal:  Genetics       Date:  2021-05-17       Impact factor: 4.562

10.  Haplotype-based inference of the distribution of fitness effects.

Authors:  Diego Ortega-Del Vecchyo; Kirk E Lohmueller; John Novembre
Journal:  Genetics       Date:  2022-04-04       Impact factor: 4.562

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