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  A conditional likelihood is required to estimate the selection coefficient in ancient DNA

Valleriani, A. (2016). A conditional likelihood is required to estimate the selection coefficient in ancient DNA. Scientific Reports, 6: 31561. doi:10.1038/srep31561.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-F4E6-B Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-627A-6
Genre: Journal Article

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Valleriani, Angelo1, Author              
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1Angelo Valleriani, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society, ou_1863324              

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Free keywords: Open Access
 Abstract: Time-series of allele frequencies are a useful and unique set of data to determine the strength of natural selection on the background of genetic drift. Technically, the selection coefficient is estimated by means of a likelihood function built under the hypothesis that the available trajectory spans a sufficiently large portion of the fitness landscape. Especially for ancient DNA, however, often only one single such trajectories is available and the coverage of the fitness landscape is very limited. In fact, one single trajectory is more representative of a process conditioned both in the initial and in the final condition than of a process free to visit the available fitness landscape. Based on two models of population genetics, here we show how to build a likelihood function for the selection coefficient that takes the statistical peculiarity of single trajectories into account. We show that this conditional likelihood delivers a precise estimate of the selection coefficient also when allele frequencies are close to fixation whereas the unconditioned likelihood fails. Finally, we discuss the fact that the traditional, unconditioned likelihood always delivers an answer, which is often unfalsifiable and appears reasonable also when it is not correct.

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 Dates: 2016-08-162016
 Publication Status: Published in print
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 Identifiers: DOI: 10.1038/srep31561
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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: - Volume / Issue: 6 Sequence Number: 31561 Start / End Page: - Identifier: ISSN: 2045-2322