Prediction of nonsynonymous single nucleotide polymorphisms in human disease-associated genes

Analysis of human genetic variation can shed light on the problem of the ge netic basis of complex disorders. Nonsynonymous single nucleotide polymorph isms (SNPs), which affect the amino acid sequence of proteins, are believed to be the most frequent type of variation associated with the respective d isease phenotype. Complete enumeration of nonsynonymous SNPs in the candida te genes will enable further association studies on panels of affected and unaffected individuals. Experimental detection of SNPs requires implementat ion of expensive technologies and is still far from being routine. Alternat ively, SNPs can be identified by computational analysis of a publicly avail able expressed sequence tag (EST) database following experimental verificat ion. We performed in silico analysis of amino acid variation for 471 of pro teins with a documented history of experimental variation studies and with confirmed association with human diseases. This allowed us to evaluate the level of completeness of the current knowledge of nonsynonymous SNPs in wel l studied, medically relevant genes and to estimate the proportion of new v ariants which can be added with the help of computer-aided mining in EST da tabases. Our results suggest that approx. 50% of frequent nonsynonymous var iants are already stored in public databases. Computational methods based o n the scan of an EST database can add significantly to the current knowledg e, but they are greatly limited by the size of EST databases and the nonuni form coverage of genes by ESTs. Nevertheless, a considerable number of new candidate nonsynonymous SNPs in genes of medical interest were found by EST screening procedure.