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Exploring Approximate Bayesian Computation for inferring recent demographic history with genomic markers in non-model species

View ORCID ProfileJoane S. Elleouet, View ORCID ProfileSally N. Aitken
doi: https://doi.org/10.1101/252650
Joane S. Elleouet
Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, 3041-2424 Main Mall, Vancouver BC V6T 1Z4, Canada
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Sally N. Aitken
Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, 3041-2424 Main Mall, Vancouver BC V6T 1Z4, Canada
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Abstract

Approximate Bayesian computation (ABC) is widely used to infer demographic history of populations and species using DNA markers. Genomic markers can now be developed for non-model species using reduced representation library (RRL) sequencing methods that select a fraction of the genome using targeted sequence capture or restriction enzymes (genotyping-by-sequencing, GBS). We explored the influence of marker number and length, knowledge of gametic phase, and tradeoffs between sample size and sequencing depth on the quality of demographic inferences performed with ABC. We focused on 2-population models of recent spatial expansion with varying numbers of unknown parameters. Performing ABC on simulated datasets with known parameter values, we found that the timing of a recent spatial expansion event could be precisely estimated in a 3-parameter model. Taking into account uncertainty in parameters such as initial population size and migration rate collectively decreased the precision of inferences dramatically. Phasing haplotypes did not improve results, regardless of sequence length. Numerous short sequences were as valuable as fewer, longer sequences, and performed best when a large sample size was sequenced at low individual depth, even when sequencing errors were added. ABC results were similar to results obtained with an alternative method based on the site frequency spectrum (SFS) when performed with unphased GBS-type markers. We conclude that unphased GBS-type datasets can be sufficient to precisely infer simple demographic models, and discuss possible improvements for the use of ABC with genomic data.

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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-NC 4.0 International license.
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Posted January 24, 2018.
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Exploring Approximate Bayesian Computation for inferring recent demographic history with genomic markers in non-model species
Joane S. Elleouet, Sally N. Aitken
bioRxiv 252650; doi: https://doi.org/10.1101/252650
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Exploring Approximate Bayesian Computation for inferring recent demographic history with genomic markers in non-model species
Joane S. Elleouet, Sally N. Aitken
bioRxiv 252650; doi: https://doi.org/10.1101/252650

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