Literature DB >> 27495379

An approximate stationary solution for multi-allele neutral diffusion with low mutation rates.

Conrad J Burden1, Yurong Tang2.   

Abstract

We address the problem of determining the stationary distribution of the multi-allelic, neutral-evolution Wright-Fisher model in the diffusion limit. A full solution to this problem for an arbitrary K×K mutation rate matrix involves solving for the stationary solution of a forward Kolmogorov equation over a (K-1)-dimensional simplex, and remains intractable. In most practical situations mutations rates are slow on the scale of the diffusion limit and the solution is heavily concentrated on the corners and edges of the simplex. In this paper we present a practical approximate solution for slow mutation rates in the form of a set of line densities along the edges of the simplex. The method of solution relies on parameterising the general non-reversible rate matrix as the sum of a reversible part and a set of (K-1)(K-2)/2 independent terms corresponding to fluxes of probability along closed paths around faces of the simplex. The solution is potentially a first step in estimating non-reversible evolutionary rate matrices from observed allele frequency spectra.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Forward Kolmogorov equation; Multi-allele Wright–Fisher; Neutral evolution

Mesh:

Year:  2016        PMID: 27495379     DOI: 10.1016/j.tpb.2016.07.005

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  3 in total

1.  The stationary distribution of a sample from the Wright-Fisher diffusion model with general small mutation rates.

Authors:  Conrad J Burden; Robert C Griffiths
Journal:  J Math Biol       Date:  2018-11-13       Impact factor: 2.259

2.  The transition distribution of a sample from a Wright-Fisher diffusion with general small mutation rates.

Authors:  Conrad J Burden; Robert C Griffiths
Journal:  J Math Biol       Date:  2019-09-17       Impact factor: 2.259

3.  Quantifying GC-Biased Gene Conversion in Great Ape Genomes Using Polymorphism-Aware Models.

Authors:  Rui Borges; Gergely J Szöllősi; Carolin Kosiol
Journal:  Genetics       Date:  2019-05-30       Impact factor: 4.562

  3 in total

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