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Genome bioinformatic analysis of nonsynonymous SNPs

DOI: 10.1186/1471-2105-8-301

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Abstract:

In the present report we have compared and contrasted structure- and sequence-based methods of prediction to over 5500 genes carrying nearly 24,000 nsSNPs, by employing an automatic comparative modelling procedure to build models for the genes. The nsSNP information came from two sources, the OMIM database which are rare (minor allele frequency, MAF, < 0.01) and are known to cause penetrant, monogenic diseases. Secondly, nsSNP information came from dbSNP125, for which the vast majority of nsSNPs, mostly MAF > 0.05, have no known link to a disease. For over 40% of the nsSNPs, structure-based methods predicted which of these sequence changes are likely to either disrupt the structure of the protein or interfere with the function or interactions of the protein. For the remaining 60%, we generated sequence-based predictions.We show that, in general, the prediction tools are able distinguish disease causing mutations from those mutations which are thought to have a neutral affect. We give examples of mutations in genes that are predicted to be deleterious and may have a role in disease. Contrary to previous reports, we also show that rare mutations are consistently predicted to be deleterious as often as commonly occurring nsSNPs.The recent sequencing of the human genome has provided a wealth of information detailing several million genetic variations between individuals. This offers new opportunities for identifying the genetic predisposition to and understanding the causes of common diseases. It has been estimated that 90% of genetic variations in humans are due to single nucleotide polymorphisms (SNPs) [1], most of which have minor allele frequencies exceeding 0.05 and will provide a significant proportion of common causal variants that will be mapped and identified in the future. Through the HapMap project over 4 million of these have been genotyped in a common panel of DNA samples, not only validating the SNP and estimating its allele frequency in the general popula

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