Literature DB >> 26556388

SpectralTDF: transition densities of diffusion processes with time-varying selection parameters, mutation rates and effective population sizes.

Matthias Steinrücken1, Ethan M Jewett2, Yun S Song3.   

Abstract

MOTIVATION: In the Wright-Fisher diffusion, the transition density function describes the time evolution of the population-wide frequency of an allele. This function has several practical applications in population genetics and computing it for biologically realistic scenarios with selection and demography is an important problem.
RESULTS: We develop an efficient method for finding a spectral representation of the transition density function for a general model where the effective population size, selection coefficients and mutation parameters vary over time in a piecewise constant manner.
AVAILABILITY AND IMPLEMENTATION: The method, called SpectralTDF, is available at https://sourceforge.net/projects/spectraltdf/ CONTACT: yss@berkeley.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Year:  2015        PMID: 26556388      PMCID: PMC4907395          DOI: 10.1093/bioinformatics/btv627

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  9 in total

1.  Stochastic processes and distribution of gene frequencies under natural selection.

Authors:  M KIMURA
Journal:  Cold Spring Harb Symp Quant Biol       Date:  1955

2.  A simple method for finding explicit analytic transition densities of diffusion processes with general diploid selection.

Authors:  Yun S Song; Matthias Steinrücken
Journal:  Genetics       Date:  2011-12-29       Impact factor: 4.562

3.  Simultaneous inference of selection and population growth from patterns of variation in the human genome.

Authors:  Scott H Williamson; Ryan Hernandez; Adi Fledel-Alon; Lan Zhu; Rasmus Nielsen; Carlos D Bustamante
Journal:  Proc Natl Acad Sci U S A       Date:  2005-05-19       Impact factor: 11.205

4.  Estimation of 2Nes from temporal allele frequency data.

Authors:  Jonathan P Bollback; Thomas L York; Rasmus Nielsen
Journal:  Genetics       Date:  2008-05       Impact factor: 4.562

Review 5.  It's about time: the temporal dynamics of phenotypic selection in the wild.

Authors:  Adam M Siepielski; Joseph D DiBattista; Stephanie M Carlson
Journal:  Ecol Lett       Date:  2009-09-08       Impact factor: 9.492

6.  Transition Densities and Sample Frequency Spectra of Diffusion Processes with Selection and Variable Population Size.

Authors:  Daniel Živković; Matthias Steinrücken; Yun S Song; Wolfgang Stephan
Journal:  Genetics       Date:  2015-04-14       Impact factor: 4.562

7.  A unified treatment of the probability of fixation when population size and the strength of selection change over time.

Authors:  D Waxman
Journal:  Genetics       Date:  2011-04-28       Impact factor: 4.562

8.  An explicit transition density expansion for a multi-allelic Wright-Fisher diffusion with general diploid selection.

Authors:  Matthias Steinrücken; Y X Rachel Wang; Yun S Song
Journal:  Theor Popul Biol       Date:  2012-11-02       Impact factor: 1.570

9.  A NOVEL SPECTRAL METHOD FOR INFERRING GENERAL DIPLOID SELECTION FROM TIME SERIES GENETIC DATA.

Authors:  Matthias Steinrücken; Anand Bhaskar; Yun S Song
Journal:  Ann Appl Stat       Date:  2014-12       Impact factor: 2.083

  9 in total
  3 in total

Review 1.  Statistical Inference in the Wright-Fisher Model Using Allele Frequency Data.

Authors:  Paula Tataru; Maria Simonsen; Thomas Bataillon; Asger Hobolth
Journal:  Syst Biol       Date:  2017-01-01       Impact factor: 15.683

2.  The Effects of Population Size Histories on Estimates of Selection Coefficients from Time-Series Genetic Data.

Authors:  Ethan M Jewett; Matthias Steinrücken; Yun S Song
Journal:  Mol Biol Evol       Date:  2016-08-22       Impact factor: 16.240

3.  Allele Age Under Non-Classical Assumptions is Clarified by an Exact Computational Markov Chain Approach.

Authors:  Bianca De Sanctis; Ivan Krukov; A P Jason de Koning
Journal:  Sci Rep       Date:  2017-09-19       Impact factor: 4.379

  3 in total

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