RESUMO
Single nucleotide polymorphisms (SNPs) are tentatively critical with regard to disease predisposition, but coding synonymous SNPs (sSNPs) are generally considered "neutral". Nevertheless, sSNPs in serine/arginine-rich (SR) and splice-site (SS) exonic splicing enhancers (ESEs) or in exonic CpG methylation targets, could be decisive for splicing, particularly in aging-related conditions, where mis-splicing is frequently observed. We presently identified 33 genes T2D-related and 28 related to neurodegenerative diseases, by investigating the impact of the corresponding coding sSNPs on splicing and using gene ontology data and computational tools. Potentially critical (prominent) sSNPs comply with the following criteria: changing the splicing potential of prominent SR-ESEs or of significant SS-ESEs by >1.5 units (Δscore), or formation/deletion of ESEs with maximum splicing score. We also noted the formation/disruption of CpGs (tentative methylation sites of epigenetic sSNPs). All disease association studies involving sSNPs are also reported. Only 21/670 coding SNPs, mostly epigenetic, reported in 33 T2D-related genes, were found to be prominent coding synonymous. No prominent sSNPs have been recorded in three key T2D-related genes (GCGR, PPARGC1A, IGF1). Similarly, 20/366 coding synonymous were identified in ND related genes, mostly epigenetic. Meta-analysis showed that 17 of the above prominent sSNPs were previously investigated in association with various pathological conditions. Three out of four sSNPs (all epigenetic) were associated with T2D and one with NDs (branch site sSNP). Five were associated with other or related pathological conditions. None of the four sSNPs introducing new ESEs was found to be disease-associated. sSNPs introducing smaller Δscore changes (<1.5) in key proteins (INSR, IRS1, DISC1) were also correlated to pathological conditions. This data reveals that genetic variation in splicing-regulatory and particularly CpG sites might be related to disease predisposition and that in-silico analysis is useful for identifying sSNPs, which might be falsely identified as silent or synonymous.
