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Genetics, Vol. 167, 2067-2081, August 2004, Copyright © 2004
doi:10.1534/genetics.103.021584
Application of Coalescent Methods to Reveal Fine-Scale Rate Variation and Recombination Hotspots
Paul Fearnhead*,
Rosalind M. Harding
,1,
Julie A. Schneider,2,
Simon Myers
and
Peter Donnelly,3
* Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YF, United Kingdom
Medical Research Council Molecular Hematology Unit, Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, United Kingdom
Department of Statistics, University of Oxford, Oxford OX1 3TG, United Kingdom
3 Corresponding author: Department of Statistics, University of Oxford, 1 S. Parks Rd., Oxford OX1 3TG, United Kingdom.
E-mail: donnelly{at}stats.ox.ac.uk
There has been considerable recent interest in understanding the way in which recombination rates vary over small physical distances, and the extent of recombination hotspots, in various genomes. Here we adapt, apply, and assess the power of recently developed coalescent-based approaches to estimating recombination rates from sequence polymorphism data. We apply full-likelihood estimation to study rate variation in and around a well-characterized recombination hotspot in humans, in the ß-globin gene cluster, and show that it provides similar estimates, consistent with those from sperm studies, from two populations deliberately chosen to have different demographic and selectional histories. We also demonstrate how approximate-likelihood methods can be used to detect local recombination hotspots from genomic-scale SNP data. In a simulation study based on 80 100-kb regions, these methods detect 43 out of 60 hotspots (ranging from 1 to 2 kb in size), with only two false positives out of 2000 subregions that were tested for the presence of a hotspot. Our study suggests that new computational tools for sophisticated analysis of population diversity data are valuable for hotspot detection and fine-scale mapping of local recombination rates.
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