The accumulation of genome-wide information on single nucleotide polymorphisms in humans provides an unprecedented opportunity to detect the evolutionary forces responsible for heterogeneity of the level of genetic variability across loci. Previous studies have shown that history of recombination events has produced long haplotype blocks in the human genome, which contribute to this heterogeneity. Other factors, however, such as natural selection or the heterogeneity of mutation rates across loci, may also lead to heterogeneity of genetic variability. We compared synonymous and non-synonymous variability within human genes with their divergence from murine orthologs. We separately analyzed the non-synonymous variants predicted to damage protein structure or function and the variants predicted to be functionally benign. The predictions were based on comparative sequence analysis and, in some cases, on the analysis of protein structure. A strong correlation between non-synonymous, benign variability and non-synonymous human-mouse divergence suggests that selection played an important role in shaping the pattern of variability in coding regions of human genes. However, the lack of correlation between deleterious variability and evolutionary divergence shows that a substantial proportion of the observed non-synonymous single-nucleotide polymorphisms reduces fitness and never reaches fixation. Evolutionary and medical implications of the impact of selection on human polymorphisms are discussed.

Human single nucleotide polymorphisms (SNPs) represent the most frequent type of human population DNA variation. One of the main goals of SNP research is to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. Non-synonymous coding SNPs (nsSNPs) comprise a group of SNPs that, together with SNPs in regulatory regions, are believed to have the highest impact on phenotype. Here we present a World Wide Web server to predict the effect of an nsSNP on protein structure and function. The prediction method enabled analysis of the publicly available SNP database HGVbase, which gave rise to a dataset of nsSNPs with predicted functionality. The dataset was further used to compare the effect of various structural and functional characteristics of amino acid substitutions responsible for phenotypic display of nsSNPs. We also studied the dependence of selective pressure on the structural and functional properties of proteins. We found that in our dataset the selection pressure against deleterious SNPs depends on the molecular function of the protein, although it is insensitive to several other protein features considered. The strongest selective pressure was detected for proteins involved in transcription regulation.

Single nucleotide polymorphisms (SNPs) constitute the bulk of human genetic variation, occurring with an average density of approximately 1/1000 nucleotides of a genotype. SNPs are either neutral allelic variants or are under selection of various strengths, and the impact of SNPs on fitness remains unknown. Identification of SNPs affecting human phenotype, especially leading to risks of complex disorders, is one of the key problems of medical genetics. SNPs in protein-coding regions that cause amino acid variants (non-synonymous cSNPs) are most likely to affect phenotypes. We have developed a straightforward and reliable method based on physical and comparative considerations that estimates the impact of an amino acid replacement on the three-dimensional structure and function of the protein. We estimate that approximately 20% of common human non-synonymous SNPs damage the protein. The average minor allele frequency of such SNPs in our data set was two times lower than that of benign non-synonymous SNPs. The average human genotype carries approximately 10(3) damaging non-synonymous SNPs that together cause a substantial reduction in fitness.