Literature DB >> 2607221

Genealogical-tree probabilities in the infinitely-many-site model.

R C Griffiths1.   

Abstract

This paper considers the distribution of the genealogical tree of a sample of genes in the infinitely-many-site model where the relative age ordering of the mutations (nodes in the tree) is known. A computer implementation of a recursion for the probability of such trees is discussed when the nodes are age-labeled, or not.

Mesh:

Year:  1989        PMID: 2607221     DOI: 10.1007/bf00276949

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  8 in total

1.  On the number of segregating sites in genetical models without recombination.

Authors:  G A Watterson
Journal:  Theor Popul Biol       Date:  1975-04       Impact factor: 1.570

2.  Counting genealogical trees.

Authors:  R C Griffiths
Journal:  J Math Biol       Date:  1987       Impact factor: 2.259

3.  The population genealogy of the infinitely--many neutral alleles model.

Authors:  P Donnelly; S Tavaré
Journal:  J Math Biol       Date:  1987       Impact factor: 2.259

4.  The sampling theory of neutral alleles and an urn model in population genetics.

Authors:  F M Hoppe
Journal:  J Math Biol       Date:  1987       Impact factor: 2.259

5.  The birth process with immigration, and the genealogical structure of large populations.

Authors:  S Tavaré
Journal:  J Math Biol       Date:  1987       Impact factor: 2.259

6.  Allelic frequencies given the sample's common ancestral type.

Authors:  B Beder
Journal:  Theor Popul Biol       Date:  1988-04       Impact factor: 1.570

7.  The sampling theory of selectively neutral alleles.

Authors:  W J Ewens
Journal:  Theor Popul Biol       Date:  1972-03       Impact factor: 1.570

8.  Line-of-descent and genealogical processes, and their applications in population genetics models.

Authors:  S Tavaré
Journal:  Theor Popul Biol       Date:  1984-10       Impact factor: 1.570

  8 in total
  12 in total

1.  A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation.

Authors:  A R Templeton; K A Crandall; C F Sing
Journal:  Genetics       Date:  1992-10       Impact factor: 4.562

2.  Inference from samples of DNA sequences using a two-locus model.

Authors:  Paul A Jenkins; Robert C Griffiths
Journal:  J Comput Biol       Date:  2011-01       Impact factor: 1.479

3.  Estimating substitution rates from molecular data using the coalescent.

Authors:  R Lundstrom; S Tavaré; R H Ward
Journal:  Proc Natl Acad Sci U S A       Date:  1992-07-01       Impact factor: 11.205

4.  Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics.

Authors:  Jody Hey; Rasmus Nielsen
Journal:  Proc Natl Acad Sci U S A       Date:  2007-02-14       Impact factor: 11.205

5.  Computing likelihoods for coalescents with multiple collisions in the infinitely many sites model.

Authors:  Matthias Birkner; Jochen Blath
Journal:  J Math Biol       Date:  2008-03-18       Impact factor: 2.259

6.  Incorporating experimental design and error into coalescent/mutation models of population history.

Authors:  Bjarne Knudsen; Michael M Miyamoto
Journal:  Genetics       Date:  2007-06-11       Impact factor: 4.562

7.  Inferring coalescence times from DNA sequence data.

Authors:  S Tavaré; D J Balding; R C Griffiths; P Donnelly
Journal:  Genetics       Date:  1997-02       Impact factor: 4.562

8.  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

9.  Empirical tests of some predictions from coalescent theory with applications to intraspecific phylogeny reconstruction.

Authors:  K A Crandall; A R Templeton
Journal:  Genetics       Date:  1993-07       Impact factor: 4.562

10.  Accurate and fast methods to estimate the population mutation rate from error prone sequences.

Authors:  Bjarne Knudsen; Michael M Miyamoto
Journal:  BMC Bioinformatics       Date:  2009-08-11       Impact factor: 3.169

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