Literature DB >> 18430934

Fraction of informative recombinations: a heuristic approach to analyze recombination rates.

J-F Lefebvre1, D Labuda.   

Abstract

In this article we present a new heuristic approach (informative recombinations, InfRec) to analyze recombination density at the sequence level. InfRec is intuitive and easy and combines previously developed methods that (i) resolve genotypes into haplotypes, (ii) estimate the minimum number of recombinations, and (iii) evaluate the fraction of informative recombinations. We tested this approach in its sliding-window version on 117 genes from the SeattleSNPs program, resequenced in 24 African-Americans (AAs) and 23 European-Americans (EAs). We obtained population recombination rate estimates (rho(obs)) of 0.85 and 0.37 kb(-1) in AAs and EAs, respectively. Coalescence simulations indicated that these values account for both the recombinations and the gene conversions in the history of the sample. The intensity of rho(obs) varied considerably along the sequence, revealing the presence of recombination hotspots. Overall, we observed approximately 80% of recombinations in one-third and approximately 50% in only 10% of the sequence. InfRec performance, tested on published simulated and additional experimental data sets, was similar to that of other hotspot detection methods. Fast, intuitive, and visual, InfRec is not constrained by sample size limitations. It facilitates understanding data and provides a simple and flexible tool to analyze recombination intensity along the sequence.

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Year:  2008        PMID: 18430934      PMCID: PMC2323797          DOI: 10.1534/genetics.107.082255

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  49 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.  Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data.

Authors:  Na Li; Matthew Stephens
Journal:  Genetics       Date:  2003-12       Impact factor: 4.562

3.  Evidence for substantial fine-scale variation in recombination rates across the human genome.

Authors:  Dana C Crawford; Tushar Bhangale; Na Li; Garrett Hellenthal; Mark J Rieder; Deborah A Nickerson; Matthew Stephens
Journal:  Nat Genet       Date:  2004-06-06       Impact factor: 38.330

Review 4.  Where the crossovers are: recombination distributions in mammals.

Authors:  Liisa Kauppi; Alec J Jeffreys; Scott Keeney
Journal:  Nat Rev Genet       Date:  2004-06       Impact factor: 53.242

5.  Insights into recombination from patterns of linkage disequilibrium in humans.

Authors:  Susan E Ptak; Kristian Voelpel; Molly Przeworski
Journal:  Genetics       Date:  2004-05       Impact factor: 4.562

6.  Estimating the rate of gene conversion on human chromosome 21.

Authors:  Badri Padhukasahasram; Paul Marjoram; Magnus Nordborg
Journal:  Am J Hum Genet       Date:  2004-07-12       Impact factor: 11.025

7.  Estimating recombination rates using three-site likelihoods.

Authors:  Jeffrey D Wall
Journal:  Genetics       Date:  2004-07       Impact factor: 4.562

8.  Application of coalescent methods to reveal fine-scale rate variation and recombination hotspots.

Authors:  Paul Fearnhead; Rosalind M Harding; Julie A Schneider; Simon Myers; Peter Donnelly
Journal:  Genetics       Date:  2004-08       Impact factor: 4.562

9.  Nonuniform recombination within the human beta-globin gene cluster.

Authors:  A Chakravarti; K H Buetow; S E Antonarakis; P G Waber; C D Boehm; H H Kazazian
Journal:  Am J Hum Genet       Date:  1984-11       Impact factor: 11.025

10.  Intense and highly localized gene conversion activity in human meiotic crossover hot spots.

Authors:  Alec J Jeffreys; Celia A May
Journal:  Nat Genet       Date:  2004-01-04       Impact factor: 38.330

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  3 in total

1.  Female-to-male breeding ratio in modern humans-an analysis based on historical recombinations.

Authors:  Damian Labuda; Jean-François Lefebvre; Philippe Nadeau; Marie-Hélène Roy-Gagnon
Journal:  Am J Hum Genet       Date:  2010-02-25       Impact factor: 11.025

2.  Genotype-based test in mapping cis-regulatory variants from allele-specific expression data.

Authors:  Jean Francois Lefebvre; Emilio Vello; Bing Ge; Stephen B Montgomery; Emmanouil T Dermitzakis; Tomi Pastinen; Damian Labuda
Journal:  PLoS One       Date:  2012-06-07       Impact factor: 3.240

3.  Haplotype allelic classes for detecting ongoing positive selection.

Authors:  Julie Hussin; Philippe Nadeau; Jean-François Lefebvre; Damian Labuda
Journal:  BMC Bioinformatics       Date:  2010-01-28       Impact factor: 3.169

  3 in total

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