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pubmed:abstractText |
Single nucleotide polymorphisms (SNPs) are distributed highly non-randomly in the human genome through a variety of processes from ascertainment biases (i.e. the preferential development of SNPs around interesting genes) to the action of mutation hotspots and natural selection. However, with more systematic SNP development, one might expect an increasing proportion of SNPs to be distributed more or less randomly. Here, I test this null hypothesis using stochastic simulations and compare this output with that of an alternative hypothesis that mutations are more likely to occur near existing SNPs, a possibility suggested both by molecular studies of meiotic mismatch repair in yeast and by data showing that SNPs cluster around heterozygous deletions. A purely Poisson process generates SNP clusters that differ from equivalent data from human chromosome 1 in both the frequency of different-sized clusters and the SNP density within each cluster, even for small clusters of just four or five SNPs, while clusters on the X chromosome differ from those on the autosomes. In contrast, modest levels of mutational non-independence generate a reasonable fit to the real data for both cluster frequency and density, and also exhibit the evolutionary transience noted for 'mutation hotspots'. Mutational non-independence therefore provides an interesting new hypothesis that appears capable of explaining the distribution of SNPs in the human genome.
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