A portion of SNPs is considered to contribute to complex disease development [7, 10–12]. SNPs in or around the candidate genes might be directly linked to a disease; however, not all SNPs are supposed to affect gene expression and function, so selection of those with potential effects is keenly debated . Several studies have developed tools and/or systematically analysed nsSNPs to identify those that affect gene function based on evolutionary conservation or structural parameters [16–18, 33]. PolyPhen is one such web-based tool utilised to select the nsSNPs that are likely to affect protein function. In short, the PolyPhen predictions are based on protein alignments, structural parameters or sequence annotations. The sensitivity of PolyPhen has been reported to be approximately 82 per cent .
In this study, we hypothesised that the systematic analysis of candidate genes that are expressed in the affected tissue is likely to improve and enrich the identification of disease-susceptibility alleles. Accordingly, using a bioinformatics-based strategy, we identified the functional nsSNPs from a large number of genes related to the carcinogenesis-related pathways (DNA repair, cell cycle, signal transduction, etc), which are expressed in breast tissue. We propose that these potentially functional nsSNPs can result in abnormalities at the protein level, which are likely to affect the development, metabolism and homeostasis of the breast tissue, and thus can contribute to breast cancer susceptibility.
The genes with functional nsSNPs identified in this study were from a variety of carcinogenesis-related cellular pathways. According to this information, possible biological roles for these nsSNPs may be suggested. For example, nsSNPs from angiogenesis- and metastasis-related proteins may have roles in tumour growth and the development of metastatic tumours [34, 35]. Additionally, DNA repair nsSNPs may lead to the accumulation of somatic mutations and thus can participate in cancer initiation and promotion [34–36]. Furthermore, together with the DNA repair nsSNPs, the nsSNPs from the pharmacology genes may also be good candidates for the studies targeting the efficacy, differential response and adverse effect of chemo-/radiotherapy in breast cancer [37–39]. The majority of the nsSNPs were from the genes related to immunological responses (74.6 per cent), which can both suppress and promote tumorigenesis . It is likely that the larger number of the functional nsSNPs in immune system-related genes is a reflection of the large number of immunology genes in the breast tissue-expressed gene set (60 per cent).
A considerable number of genes with functional nsSNPs have been previously linked to breast cancer aetiology: ADM,ADRB2,APOE,CHGA,CSF1,CYP1B1,DAG1,ENG,EPHX1,ERBB2,F2R,MMP9,MUC4,NFATC1,NOTCH4,PLAU,PLAUR,PTGS2 and VCAM1 . Therefore, we propose that the nsSNPs in Table 2 are excellent candidates as genetic factors involved in breast cancer initiation, promotion or progression. Additionally, some of these nsSNPs may be critical for breast cancer treatment outcome.
When the distribution of the commonly occurring functional nsSNPs was analysed, differences in the major alleles and the allele frequencies across human populations were observed. For example, 15 commonly occurring nsSNPs were found in all populations, whereas another set of 15 nsSNPs was specific to particular population(s). These differences might be reflections of either the age of the allele, founder effects or the dissimilar selective pressures acting on different populations [58, 59]. Most importantly, the data also indicate that a common nsSNP with a potential biological consequence in our set was equally likely to be either prevalent across different human populations or limited to some populations. Clearly, the latter prompted us to conclude that the population-specific functional nsSNPs may contribute to the genetic predisposition in individuals with a specific background. In this regard, this conclusion is consistent with previous studies in which genetic variations with significantly different allelic frequencies among populations were found to be associated with specific disease or differential drug responses [60–65]. This information may be particularly helpful to researchers in determining which nsSNPs may be relevant to utilise in specific population-based studies. In addition, although further analyses are required, it is tempting to speculate that these nsSNPs may be a part of the potential variability of the molecular basis of breast cancer predisposition and drug response among different human populations.
Data integration from several databases forms the basis of our strategy to determine functional SNPs of breast tissue-expressed genes. The quality and the quantity of the genomic data within individual databases influence the comprehensiveness of the combined data. The functional SNP list presented in this study is a result of data integration from three databases -- namely, TissueInfo, Ensembl, and dbSNP . The non-matching data fields (eg transcript identifiers) between TissueInfo, Ensembl and dbSNP have been the main source of missing data. For example, although BRCA1 was known to have a potentially functional SNP (predicted previously), this information has not been captured because of non-matching transcript identifier information for BRCA1 in the databases. Thus, incompatibility of data in different databases has been a rate-limiting factor for the bioinformatics-based strategies presented here. The improvement of the quality and the quantity of genomic data in the databases will prove beneficial for researching complex questions. Also, the genes presented in this paper are based on the expressed sequence tag information, which may lead to an under-representation of rarely expressed genes [29, 66]. Data integration using other tissue expression databases is likely to enrich the quality of the data produced. Nevertheless, although it is possible that the SNPs presented here may not represent the most comprehensive list, the SNPs identified using the proposed strategy represent a valuable resource for studying the genetic predisposition to breast cancer.