Estimating recombination rates from single-nucleotide polymorphisms using summary statistics - PubMed (original) (raw)
Comparative Study
Estimating recombination rates from single-nucleotide polymorphisms using summary statistics
Badri Padhukasahasram et al. Genetics. 2006 Nov.
Abstract
We describe a novel method for jointly estimating crossing-over and gene-conversion rates from population genetic data using summary statistics. The performance of our method was tested on simulated data sets and compared with the composite-likelihood method of R. R. Hudson. For several realistic parameter values, the new method performed similarly to the composite-likelihood approach for estimating crossing-over rates and better when estimating gene-conversion rates. We used our method to analyze a human data set recently genotyped by Perlegen Sciences.
Figures
Figure 1.—
Recombination rate estimates for 50-kb overlapping windows across a 50-Mb region in chromosome 1. (Top) The population gene-conversion rates and (bottom) the population crossing-over rates, estimated under a model with uniform recombination for a mean conversion tract length (L) of 500 bp. The shaded bars represent confidence intervals with at least 90% of the total mass and are constructed around the estimated values at positions corresponding to the centers of the windows.
Figure 1.—
Recombination rate estimates for 50-kb overlapping windows across a 50-Mb region in chromosome 1. (Top) The population gene-conversion rates and (bottom) the population crossing-over rates, estimated under a model with uniform recombination for a mean conversion tract length (L) of 500 bp. The shaded bars represent confidence intervals with at least 90% of the total mass and are constructed around the estimated values at positions corresponding to the centers of the windows.
Figure 2.—
Comparison of recombination rates estimated from population genetic data using the summary statistics method with those obtained from human genetic maps (data from K
ong
et al. 2002 used in M
yers
et al. 2005). A scatter plot is shown of the estimates of population-scaled crossing-over rate with the deCODE pedigree-based estimates (centimorgans per megabase) for 5-Mb regions across the human genome. The correlation coefficient between these estimates is 0.9181.
Figure 3.—
Crossing-over estimates in a 3.3-Mb MHC region in human chromosome 6. The red curve shows estimates (centimorgans per megabase) obtained in M
yers
et al. (2005) using the M
c
V
ean
et al. (2004) method. The blue curve shows sliding-window population crossing-over estimates (multiplied by some constant) for overlapping 50-kb regions along chromosome 6 obtained using the summary statistics method.
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