The local environment of single nucleotide polymorphisms (SNPs) contains abundant genetic information for the study of mechanisms of mutation, genome evolution, and causes of diseases. Recent studies revealed that neighboring-nucleotide biases on SNPs were strong and the genome-wide bias patterns could be represented by a small subset of the total SNPs. It remains unsolved for the estimation of the effective SNP size, the number of SNPs that are sufficient to represent the bias patterns observed from the whole SNP data.
To estimate the effective SNP size, we developed a novel statistical method, SNPKS, which considers both the statistical and biological significances. SNPKS consists of two major steps: to obtain an initial effective size by the Kolmogorov-Smirnov test (KS test) and to find an intermediate effective size by interval evaluation. The SNPKS algorithm was implemented in computer programs and applied to the real SNP data. The effective SNP size was estimated to be 38,200, 39,300, 38,000, and 38,700 in the human, chimpanzee, dog, and mouse genomes, respectively, and 39,100, 39,600, 39,200, and 42,200 in human intergenic, genic, intronic, and CpG island regions, respectively.
SNPKS is the first statistical method to estimate the effective SNP size. It runs efficiently and greatly outperforms the algorithm implemented in SNPNB. The application of SNPKS to the real SNP data revealed the similar small effective SNP size (38,000 – 42,200) in the human, chimpanzee, dog, and mouse genomes as well as in human genomic regions. The findings suggest strong influence of genetic factors across vertebrate genomes.