Genome bioinformatic analysis of nonsynonymous SNPs

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.

Figure 1

Distribution of fold recognition results. Distribution of the number of HOMSTRAD families predicted by FUGUE for a gene.

Figure 2

Model of IL21R/IL21 complex. Ribbon representation of the model of IL21R in complex with its ligand, IL21. The residue which is mutated, Arg191, is show is ball-and-stick representation.

Figure 3

Model of TCF7 in complex with DNA. Ribbon representation of the model of TCF7 in complex with DNA. The residue which is mutated, Trp336, is show is ball-and-stick representation. The tryptophan residue interacts with the major groove of the DNA.

Figure 4

Distribution of predicted functional residues. Pie chart showing percentage of mutations affecting predicted functional residues for OMIM and dbSNP

Figure 5

Distribution of predicted structurally deleterious nsSNPs. Pie chart showing percentage of structurally deleterious predictions for OMIM and dbSNP datasets.