The Library
Multi-locus data distinguishes between population growth and multiple merger coalescents
Tools
Koskela, Jere (2018) Multi-locus data distinguishes between population growth and multiple merger coalescents. Statistical Applications in Genetics and Molecular Biology, 17 (3). 20170011. doi:10.1515/sagmb-2017-0011 ISSN 1544-6115.
|
PDF
WRAP-multi-locus-data-distinguishes-between-population-growth-multiple-merger-coalescents-Koskela-2018.pdf - Accepted Version - Requires a PDF viewer. Download (7Mb) | Preview |
|
PDF
WRAP-multi-locus-data-distinguishes-population-Koskela-2017.pdf - Submitted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (7Mb) |
Official URL: https://doi.org/10.1515/sagmb-2017-0011
Abstract
We introduce a low dimensional function of the site frequency spectrum that is tailor-made for distinguishing coalescent models with multiple mergers from Kingman coalescent models with population growth, and use this function to construct a hypothesis test between these two model classes. The null and alternative sampling distributions of our statistic are intractable, but its low dimensionality renders these distributions amenable to Monte Carlo estimation. We construct kernel density estimates of the sampling distributions based on simulated data, and show that the resulting hypothesis test dramatically improves on the statistical power of a current state-of-the-art method. A key reason for this improvement is the use of multi-locus data, in particular averaging observed site frequency spectra across unlinked loci to reduce sampling variance. We also demonstrate the robustness of our method to nuisance and tuning parameters. Finally we demonstrate that the same kernel density estimates can be used to conduct parameter estimation, and argue that our method is readily generalisable for applications in model selection, parameter inference and experimental design.
Item Type: | Journal Article | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics |
|||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||
Library of Congress Subject Headings (LCSH): | Genes -- Mathematical models, Genetics, Mathematical statistics | |||||||||
Journal or Publication Title: | Statistical Applications in Genetics and Molecular Biology | |||||||||
Publisher: | Walter de Gruyter GmbH & Co. KG | |||||||||
ISSN: | 1544-6115 | |||||||||
Official Date: | 13 June 2018 | |||||||||
Dates: |
|
|||||||||
Volume: | 17 | |||||||||
Number: | 3 | |||||||||
Article Number: | 20170011 | |||||||||
DOI: | 10.1515/sagmb-2017-0011 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 20 April 2018 | |||||||||
Date of first compliant Open Access: | 15 February 2019 | |||||||||
RIOXX Funder/Project Grant: |
|
|||||||||
Related URLs: | ||||||||||
Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year