Applications of computational tools to predict functional SNPs effects in human ErbB genes

Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetic basis of human complex diseases such as cancer. However, identifying functional SNPs among the huge number of available SNPs is challenging. In this study, we analyzed the genetic variations that can alter the expression and function of ErbB proteins using different computational tools. For noncoding SNP, we found that one SNP located in 59UTR of ErbB1 gene might change protein expression level and two SNPS located in regulatory regions might affect transcriptional regulation of Erbb1 and Erbb4. For coding SNPs we predicted that 25 nonsynonymous SNPs (most of them in ErbB1 gene) might disrupt the protein function among which 22 might alter protein structure. Prediction regarding the potential effect of the SNPs showed that 13 of them located within the tyrosine kinase or the ligand binding domain are likely to be associated with cancer.