The genetics of human phenotype variation and especially, the genetic basis of human complex diseases could be understood by knowing the functions of Single Nucleotide Polymorphisms (SNPs). The main goal of this work is to predict the deleterious non-synonymous SNPs (nsSNPs), so that the number of SNPs screened for association with disease can be reduced to that most likely alters gene function. In this work by using computational tools, we have analyzed the SNPs that can alter the expression and function of cancerous genes involved in colon cancer. To explore possible relationships between genetic mutation and phenotypic variation, different computational algorithm tools like Sorting Intolerant from Tolerant (evolutionary-based approach), Polymorphism Phenotyping (structure-based approach), PupaSuite, UTRScan and FASTSNP were used for prioritization of high-risk SNPs in coding region (exonic nonsynonymous SNPs) and non-coding regions (intronic and exonic 5′ and 3′-untranslated region (UTR) SNPs). We developed semi-quantitative relative ranking strategy (non availability of 3D structure) that can be adapted to a priori SNP selection or post hoc evaluation of variants identified in whole genome scans or within haplotype blocks associated with disease. Lastly, we analyzed haplotype tagging SNPs (htSNPs) in the coding and untranslated regions of all the genes by selecting the force tag SNPs selection using iHAP analysis. The computational architecture proposed in this review is based on integrating relevant biomedical information sources to provide a systematic analysis of complex diseases. We have shown a "real world" application of interesting existing bioinformatics tools for SNP analysis in colon cancer. © 2010 International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg.