The Library
Robust model selection between population growth and multiple merger coalescents
Tools
Tools
Tools
Koskela, Jere and Wilke Berenguer, Maite (2019) Robust model selection between population growth and multiple merger coalescents. Mathematical Biosciences, 311 . pp. 1-12. doi:10.1016/j.mbs.2019.03.004 ISSN 0025-5564.
|
PDF
WRAP-roburst-model-selection-population-growth-multiple-merger-coalescents-Koskela-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (656Kb) | Preview |
Official URL: https://doi.org/10.1016/j.mbs.2019.03.004
Abstract
We study the effect of biological confounders on the model selection problem between Kingman coalescents with population growth, and Xi-coalescents involving simultaneous multiple mergers. We use a low dimensional, computationally tractable summary statistic, dubbed the singleton-tail statistic, to carry out approximate likelihood ratio tests between these model classes. The singleton-tail statistic has been shown to distinguish between them with high power in the simple setting of neutrally evolving, panmictic populations without recombination. We extend this work by showing that cryptic recombination and selection do not diminish the power of the test, but that misspecifying population structure does. Furthermore, we demonstrate that the singleton-tail statistic can also solve the more challenging model selection problem between multiple mergers due to selective sweeps, and multiple mergers due to high fecundity with moderate power of up to 30%.
| 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): | Natural selection , Population genetics, Genetic recombination | |||||||||||||||
| Journal or Publication Title: | Mathematical Biosciences | |||||||||||||||
| Publisher: | Elsevier Science Inc. | |||||||||||||||
| ISSN: | 0025-5564 | |||||||||||||||
| Official Date: | May 2019 | |||||||||||||||
| Dates: |
|
|||||||||||||||
| Volume: | 311 | |||||||||||||||
| Page Range: | pp. 1-12 | |||||||||||||||
| DOI: | 10.1016/j.mbs.2019.03.004 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| Date of first compliant deposit: | 6 March 2019 | |||||||||||||||
| Date of first compliant Open Access: | 6 March 2020 | |||||||||||||||
| 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
