Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation.

Using the relative expression levels of two SNP alleles of a gene in the same sample is an effective approach for identifying cis-acting regulatory SNPs (rSNPs). In the current study, we established a process for systematic screening for cis-acting rSNPs using experimental detection of AI as an initial approach. We selected 160 expressed candidate genes that are involved in cancer and anticancer drug resistance for analysis of AI in a panel of cell lines that represent different types of cancers and have been well characterized for their response patterns against anticancer drugs. Of these genes, 60 contained heterozygous SNPs in their coding regions, and 41 of the genes displayed imbalanced expression of the two cSNP alleles. Genes that displayed AI were subjected to bioinformatics-assisted identification of rSNPs that alter the strength of transcription factor binding. rSNPs in 15 genes were subjected to electrophoretic mobility shift assay, and in eight of these genes (APC, BCL2, CCND2, MLH1, PARP1, SLIT2, YES1, XRCC1) we identified differential protein binding from a nuclear extract between the SNP alleles. The screening process allowed us to zoom in from 160 candidate genes to eight genes that may contain functional rSNPs in their promoter regions.