SNPs3D: candidate gene and SNP selection for association studies

Abstract

Background: The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and database that provides and integrates as much information as possible on disease/gene relationships at the molecular level.

Description: The resource http://www.SNPs3D.org has three primary modules. One module identifies which genes are candidates for involvement in a specified disease. A second module provides information about the relationships between sets of candidate genes. The third module analyzes the likely impact of non-synonymous SNPs on protein function. Disease/candidate gene relationships and gene-gene relationships are derived from the literature using simple but effective text profiling. SNP/protein function relationships are derived by two methods, one using principles of protein structure and stability, the other based on sequence conservation. Entries for each gene include a number of links to other data, such as expression profiles, pathway context, mouse knockout information and papers. Gene-gene interactions are presented in an interactive graphical interface, providing rapid access to the underlying information, as well as convenient navigation through the network. Use of the resource is illustrated with aspects of the inflammatory response and hypertension.

Conclusion: The combination of SNP impact analysis, a knowledge based network of gene relationships and candidate genes, and access to a wide range of data and literature allow a user to quickly assimilate available information, and so develop models of gene-pathway-disease interaction.

Figure 1

Database Schema. The Blue blocks represent individual modules, which may be single or multiple MySql tables.

Figure 2

Example interface page of candidate SNPs for inflammation related disease. Two support vector machine (SVM) models, based on sequence profiles [25] and structural stability [24] are used to analyze SNPs in candidate genes for inflammation. SNPs are classified as deleterious (negative SVM score) or not to protein function in vivo. SNP population frequency information is extracted from the NCBI dbSNP database.

Figure 3

Log-log plot of linkage scores in the gene-gene KnowledgeNet. Scores follow an approximately power law distribution, with a few very high scoring relationships (up to a value of 300), and many relatively weak ones.

Figure 4

Distribution of the number of links to each gene in the gene-gene KnowledgeNet. Blue bars show the distribution for all genes with at least one link (15,799) and red, the distribution for 1669 linked HGMD monogenic disease genes. The tail is truncated – the highest linkage is 493, for TP53. Genes with no interactions above the threshold score of 0.5 are not included.

Figure 5

Distribution of the number of candidate genes for a set of 76 diseases. The curve shows the distribution using a disease-gene linkage threshold of 0.05. Cancers and common human diseases tend to have many candidate genes, but monogenic diseases typically have more than one candidate as well.

Figure 6

Graphical Interface for the KnowledgeNet of candidate genes for hypertension. The four larger ovals circle the clusters of genes in each of the primary blood pressure regulation pathways. Oval symbols are used for genes involved in monogenic disease, rectangular symbols for the rest. Red indicates that one or more population SNPs are classified as harmful at the molecular level. Italic red text indicates that one or more population SNPs with population frequency information are predicted to be deleterious. The length and color of the edges represent the strength of the link between pairs of genes. Red edges link genes sharing the same abstracts. Short edges link genes sharing a large number of biological keywords. Subsets of nodes can be highlighted by a number of criteria, such as membership of the same KEGG pathway, or homology, or SNP frequency.

Figure 7

Simplified view of the four primary candidate pathways involved in hypertension. A: renin-angiotensin pathway; B: regulation by endothelin (EDN1); C: regulation by natruretic peptide (NPPA, NPPB, NPPC); D: the bradykinin-killikrien pathway.