Open Access Highly Accessed Methodology article

Genome bioinformatic analysis of nonsynonymous SNPs

David F Burke1*, Catherine L Worth1, Eva-Maria Priego1, Tammy Cheng1, Luc J Smink2, John A Todd2 and Tom L Blundell1

Author Affiliations

1 Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK

2 Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 2XY, UK

For all author emails, please log on.

BMC Bioinformatics 2007, 8:301  doi:10.1186/1471-2105-8-301

Published: 20 August 2007

Abstract

Background

Genome-wide association studies of common diseases for common, low penetrance causal variants are underway. A proportion of these will alter protein sequences, the most common of which is the non-synonymous single nucleotide polymorphism (nsSNP). It would be an advantage if the functional effects of an nsSNP on protein structure and function could be predicted, both for the final identification process of a causal variant in a disease-associated chromosome region, and in further functional analyses of the nsSNP and its disease-associated protein.

Results

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.

Conclusion

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.