Literature DB >> 22399461

Estimating recombination rates from genetic variation in humans.

Adam Auton1, Gil McVean.   

Abstract

Recombination acts to shuffle the existing genetic variation within a population, leading to various approaches for detecting its action and estimating the rate at which it occurs. Here, we discuss the principal methodological and analytical approaches taken to understanding the distribution of recombination across the human genome. We first discuss the detection of recent crossover events in both well-characterised pedigrees and larger populations with extensive recent shared ancestry. We then describe approaches for learning about the fine-scale structure of recombination rate variation from patterns of genetic variation in unrelated individuals. Finally, we show how related approaches using individuals of admixed ancestry can provide an alternative approach to analysing recombination. Approaches differ not only in the statistical methods used, but also in the resolution of inference, the timescale over which recombination events are detected, and the extent to which inter-individual variation can be identified.

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Mesh:

Year:  2012        PMID: 22399461     DOI: 10.1007/978-1-61779-585-5_9

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  9 in total

1.  DNA methylation epigenetically silences crossover hot spots and controls chromosomal domains of meiotic recombination in Arabidopsis.

Authors:  Nataliya E Yelina; Christophe Lambing; Thomas J Hardcastle; Xiaohui Zhao; Bruno Santos; Ian R Henderson
Journal:  Genes Dev       Date:  2015-10-15       Impact factor: 11.361

2.  Recommendations for improving statistical inference in population genomics.

Authors:  Parul Johri; Charles F Aquadro; Mark Beaumont; Brian Charlesworth; Laurent Excoffier; Adam Eyre-Walker; Peter D Keightley; Michael Lynch; Gil McVean; Bret A Payseur; Susanne P Pfeifer; Wolfgang Stephan; Jeffrey D Jensen
Journal:  PLoS Biol       Date:  2022-05-31       Impact factor: 9.593

Review 3.  Meiotic recombination in mammals: localization and regulation.

Authors:  Frédéric Baudat; Yukiko Imai; Bernard de Massy
Journal:  Nat Rev Genet       Date:  2013-11       Impact factor: 53.242

4.  Recombination Rate Heterogeneity within Arabidopsis Disease Resistance Genes.

Authors:  Kyuha Choi; Carsten Reinhard; Heïdi Serra; Piotr A Ziolkowski; Charles J Underwood; Xiaohui Zhao; Thomas J Hardcastle; Nataliya E Yelina; Catherine Griffin; Matthew Jackson; Christine Mézard; Gil McVean; Gregory P Copenhaver; Ian R Henderson
Journal:  PLoS Genet       Date:  2016-07-14       Impact factor: 5.917

5.  Effects of Demographic History on the Detection of Recombination Hotspots from Linkage Disequilibrium.

Authors:  Amy L Dapper; Bret A Payseur
Journal:  Mol Biol Evol       Date:  2018-02-01       Impact factor: 16.240

6.  Development of a Targeted Multi-Disorder High-Throughput Sequencing Assay for the Effective Identification of Disease-Causing Variants.

Authors:  Maria Delio; Kunjan Patel; Alex Maslov; Robert W Marion; Thomas V McDonald; Evan M Cadoff; Aaron Golden; John M Greally; Jan Vijg; Bernice Morrow; Cristina Montagna
Journal:  PLoS One       Date:  2015-07-27       Impact factor: 3.240

7.  Recombination in the human Pseudoautosomal region PAR1.

Authors:  Anjali G Hinch; Nicolas Altemose; Nudrat Noor; Peter Donnelly; Simon R Myers
Journal:  PLoS Genet       Date:  2014-07-17       Impact factor: 5.917

8.  Identifying chromosomal subpopulations based on their recombination histories advances the study of the genetic basis of phenotypic traits.

Authors:  Carlos Ruiz-Arenas; Alejandro Cáceres; Marcos López; Dolors Pelegrí-Sisó; Josefa González; Juan R González
Journal:  Genome Res       Date:  2020-11-17       Impact factor: 9.043

9.  BREC: an R package/Shiny app for automatically identifying heterochromatin boundaries and estimating local recombination rates along chromosomes.

Authors:  Yasmine Mansour; Annie Chateau; Anna-Sophie Fiston-Lavier
Journal:  BMC Bioinformatics       Date:  2021-08-06       Impact factor: 3.307

  9 in total

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