Predicting variations in splicing induced by disease associated SNPs

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Predicting variations in splicing induced by disease associated SNPs

We have predicted number of changes in gene splicing patterns induced by the polymorphic variations previously associated with predisposition to the breast cancer and Alzheimer’s as shown in [see Additional File 2 Tables S1 and S2]. Some of the predicted changes are quite dramatic, but in general SNPs cause milder effect compared to the effect of mutations [see Section Predicting aberrant splicing isoforms] where annotated SSs routinely disappear or mutations create new cryptic exons. Number of polymorphic variants, potentially alternating composition of gene isoforms, was predicted for the disease associated and randomly selected groups of control SNPs are shown in Table 2.
Here we tried to rank the polymorphic variations according to their possible destabilizing effect on splicing. We reported [see Additional File 2 Tables S1 and S2] polymorphic variations that change annotated exon score more than 2%, which according to [41] could cause increased exon skipping or retention compared to the reference exon. According to Table 2 number of such events induced by the disease associated SNPs is at least 3.5 times higher compared to control SNP groups, which suggests active role of the disease associated SNPs in modulation of predicted exonic strengths. These variations could indicate consequently different splicing commitment patterns for the affected exonic isoforms.
Another class of events is the score change for the exons overlapping with the annotated exon, which according to [7-8, 41] could disrupt mRNA inclusion patterns for alternatively used exon isoforms sharing an annotated boundary. According to Table 2 number of such events induced by the disease associated SNPs is also significantly increased compared to control SNP groups, which suggests destabilizing role for many of such changes listed in [see Additional File 2 Tables S1 and S2]. Similar splicing destabilizing effect could be achieved by simply removing or creating additional exon isoforms sharing a SS with an annotated exon and the number of such predicted events induced by the disease associated SNPs is also substantially increased compared to controls. On the contrary, the number of polymorphic variations associated with creation of new cryptic SSs or pseudo exon deletion is approximately the same for the disease associated and control group of SNPs, which suggests insignificant effect on splicing for these classes of events.

Conclusions

Using the set of previously predicted cis-acting elements we were able to construct a splicing simulator capable of predicting exon score changes induced by mutations and polymorphic variants thus elucidating possible mechanism behind such variants leading to disorders caused by aberrant splicing.
Our tool performs favorably, compared to other splicing prediction methods, in context of genes linked to ASD. SpliceScan II provided more accurate prediction of aberrant splicing events, as documented in DBASS5 [7] and DBASS3 [8], compared to existing methods. Although the performance of our tool predicting the effect of mutations triggering an aberrant splicing is high compared to other methods, it could not be used as a general ab initio gene structural annotation method since the number of false positive predicted exons is high, as could be seen in Figure 2, though the fraction of reported false positives is comparable to what reported by other similar methods. Therefore, the most informative use of our method would evolve screening of polymorphic variants for possible splicing alternations in the context of known reference human gene structures. To accomplish this task we have created companion Autism Candidate Gene Map (ACGMAP) database (http://www.meddean.luc.edu/node/375) that contains such structures and known alternative splicing variants for candidate ASD genes.
The reason the SpliceScan II is less specific (especially for higher sensitivity values) than previous SpliceScan tool [23] is in the nature of classification problem we address with new method. As could be seen in Figure 4, the SpliceScan uses simple probabilistic model of scoring putative 3’ SS, where confidence of the putative 3’ SS raises since two strong complement 5’SSs are located downstream.
However, according to [7] 3’SS would unlikely to form exon with any of the 5’ SSs located downstream in such a way since physiologically feasible 5’SS normally avoid strong competitors nearby. Indeed, SpliceScan scoring for both putative 5’SSs would be mediocre due to a conflict associated with their closeness. However, this observation does not help to predict which exonic isoform would be activated.
To resolve this logical difficulty SpliceScan II makes all possible pairs of putative 3’SS and 5’SS located no further than 400 nt apart to predict possible SSs utilization. The number of pseudo exons formed this way outnumber the real exons by at least on order of magnitude [18], which turns in a harder classification task than simple SSs classification. For the weak SSs the number of putative exons to be classified is in excess of the number of weak SSs flanking them, which translates to a lower specificity compared to simpler SSs scoring methods.

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