A model for the clustered distribution of SNPs in the human genome

TL;DR

This paper introduces a phenomenological model based on preferential mutation proximity to explain the clustered distribution of SNPs in the human genome, demonstrating its effectiveness over random models across different chromosomes.

Contribution

The paper presents a new preferential mutation model that better captures SNP clustering and explains population-specific mutation patterns in the human genome.

Findings

Preferential mutation model outperforms random models in fitting SNP clustering

Model applies to both autosomes and X chromosome, explaining their differences

Parameter analysis reveals population-specific mutation probabilities

Abstract

Motivated by a non-random but clustered distribution of SNPs, we introduce a phenomenological model to account for the clustering properties of SNPs in the human genome. The phenomenological model is based on a preferential mutation to the closer proximity of existing SNPs. With the Hapmap SNP data, we empirically demonstrate that the preferential model is better for illustrating the clustered distribution of SNPs than the random model. Moreover, the model is applicable not only to autosomes but also to the X chromosome, although the X chromosome has different characteristics from autosomes. The analysis of the estimated parameters in the model can explain the pronounced population structure and the low genetic diversity of the X chromosome. In addition, correlation between the parameters reveals the population-wise difference of the mutation probability. These results support the mutational non-independence hypothesis against random mutation.