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Coalescents and Genealogical Structure Under Neutrality

Coalescents and Genealogical Structure Under Neutrality One of the important recent developments in population genetics modeling is the use of so-called coalescent or genealogical methods. In considering the structure of genetic data, these methods focus primarily on the genealogical tree of the sampled genes. They are attractive for several reasons: (a) Quanti­ tative analysis of stochastic models is usually easier with genealogical methods than with traditional approaches; (b) the structure of genetic data reflects, in large part, the underlying genealogy, so that an understanding of genealogy enhances a qualitative understanding of the patterns of variation in genetic data; (c) use of the coalescent leads to extremely efficient simulation methods, and (d) it provides inference techniques for genetic data that allow,for the first time,full use of the information in that data. Our aim here is to illustrate some of the central ideas underlying genealogical methods. It is neither possible,nor perhaps helpful,to be exhaustive,and we do not attempt a complete historical account even of the areas we do discuss. Earlier reviews of the approach may be found (see 7,21,22,47,48). A notable absentee from our coverage is genealogy in models with recombination (see 12,21, 22). We have aimed for a middle course between giving free reign to http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Genetics Annual Reviews

Coalescents and Genealogical Structure Under Neutrality

Donnelly, P; Tavare, S
Annual Review of Genetics , Volume 29 (1) – Dec 1, 1995
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Publisher
Annual Reviews
Copyright
Copyright 1995 Annual Reviews. All rights reserved
Subject
Review Articles
ISSN
0066-4197
eISSN
1545-2948
DOI
10.1146/annurev.ge.29.120195.002153
pmid
8825481
Publisher site
See Article on Publisher Site

Abstract

One of the important recent developments in population genetics modeling is the use of so-called coalescent or genealogical methods. In considering the structure of genetic data, these methods focus primarily on the genealogical tree of the sampled genes. They are attractive for several reasons: (a) Quanti­ tative analysis of stochastic models is usually easier with genealogical methods than with traditional approaches; (b) the structure of genetic data reflects, in large part, the underlying genealogy, so that an understanding of genealogy enhances a qualitative understanding of the patterns of variation in genetic data; (c) use of the coalescent leads to extremely efficient simulation methods, and (d) it provides inference techniques for genetic data that allow,for the first time,full use of the information in that data. Our aim here is to illustrate some of the central ideas underlying genealogical methods. It is neither possible,nor perhaps helpful,to be exhaustive,and we do not attempt a complete historical account even of the areas we do discuss. Earlier reviews of the approach may be found (see 7,21,22,47,48). A notable absentee from our coverage is genealogy in models with recombination (see 12,21, 22). We have aimed for a middle course between giving free reign to

Journal

Annual Review of Genetics – Annual Reviews

Published: Dec 1, 1995

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