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Single nucleotide polymorphisms (SNPs) are the major source of human genetic variation, and the functional subset of SNPs, predominantly in protein coding regions, contributes to phenotypic variation. However, much of the variation in coding regions may not produce any functional effects. There are two broad strategies for classifying polymorphism as functional or neutral: sequence-based methods predict functional significance based on conservation scores calculated from alignments of homologous gene sequences; structure-based methods map variations to known protein structures and predict likely effects based on properties of proteins. Several tools have been developed to classify polymorphism as functional or neutral based on these methods. It was shown that most of functional SNPs are evolutionarily deleterious. Though the utility of the tools has not yet been adequately demonstrated, they may have important applications in the area of medical genetics.

In addition to multiple, complete genome sequences, genome-wide data on biological properties of genes, such as knockout effect, expression levels, protein-protein interactions, and others, are rapidly accumulating. Numerous attempts were made by many groups to examine connections between these properties and quantitative measures of gene evolution. The questions addressed pertain to the most fundamental aspects of biology: what determines the effect of the knockout of a given gene on the phenotype (in particular, is it essential or not) and the rate of a gene’s evolution and how are the phenotypic properties and evolution connected? Many significant correlations were detected, e.g., positive correlation between the tendency of a gene to be lost during evolution and sequence evolution rate, and negative correlations between each of the above measures of evolutionary variability and expression level or the phenotypic effect of gene knockout. However, most of these correlations are relatively weak and explain a small fraction of the variation present in the data. We propose that the majority of the relationships between the phenotypic (“input”) and evolutionary (“output”) variables can be described with a single, composite variable, the genes “social status in the genomic community”, which reflects the biological role of the gene and its mode of evolution. “High-status” genes, involved in house-keeping processes, are more likely to be higher and broader expressed, to have more interaction partners, and to produce lethal or severely impaired knockout mutants. These genes also tend to evolve slower and are less prone to gene loss across various taxonomic groups. “Low-status” genes are expected to be weakly expressed, have fewer interaction partners, and exhibit narrower (and less coherent) phyletic distribution. On average, these genes evolve faster and are more often lost during evolution than high-status genes. The “gene status” notion may serve as a generator of null hypotheses regarding the connections between phenotypic and evolutionary parameters associated with genes. Any deviation from the expected pattern calls for attention—to the quality of the data, the nature of the analyzed relationship, or both.