  Single nucleotide polymorphisms (SNPs) often appear in clusters along the
length of a chromosome. This is due to variation in local coalescent times
caused by,for example, selection or recombination. Here we investigate whether
recombination alone (within a neutral model) can cause statistically
significant SNP clustering. We measure the extent of SNP clustering as the
ratio between the variance of SNPs found in bins of length $l$, and the mean
number of SNPs in such bins, $\sigma^2_l/\mu_l$. For a uniform SNP distribution
$\sigma^2_l/\mu_l=1$, for clustered SNPs $\sigma^2_l/\mu_l > 1$. Apart from the
bin length, three length scales are important when accounting for SNP
clustering: The mean distance between neighboring SNPs, $\Delta$, the mean
length of chromosome segments with constant time to the most recent common
ancestor, $\el$, and the total length of the chromosome, $L$. We show that SNP
clustering is observed if $\Delta < \el \ll L$. Moreover, if $l\ll \el \ll L$,
clustering becomes independent of the rate of recombination. We apply our
results to the analysis of SNP data sets from mice, and human chromosomes 6 and
X. Of the three data sets investigated, the human X chromosome displays the
most significant deviation from neutrality.
