Mechanism and Modeling of Human Disease-associated Near-exon Intronic Variants That Perturb RNA Splicing

It is estimated that 10-30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, systematic diagnosis or prediction for splicing variants is yet to be established, especially for the near-exon intronic splice region. The major challenge lies in the redundant and ill-defined branch sites and other splicing motifs therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants overlapped with branch sites and collected 5,884 variants across the 5’ splice region. We found that strong splice sites and exonic features preserve splicing from intronic sequence variation. While the splicing altering mechanism of the 3’ intronic variants is complex, that of the 5’ is mainly splice site destruction. Statistical learning combined with these molecular features allows precise prediction for altered splicing from an intronic variant. This statistical model provides identity and ranking of biological features that determine splicing, which serves as transferable knowledge, and out-performs the benchmarking predictive tool. Moreover, we demonstrated that intronic splicing variants may associate with disease risks in human population. Our study elucidates the mechanism of splicing response of intronic variants, which classify disease-associated splicing variants for the promise of precision medicine.

High-Throughput Sequencing to Identify Mutations Associated with Retinal Dystrophies

Retinal dystrophies (RD) are clinically and genetically heterogenous disorders showing mutations in over 270 disease-associated genes. Several millions of people worldwide are affected with different types of RD. Studying the relevance of disease-associated sequence alterations will assist in understanding disorders and may lead to the development of therapeutic approaches. Here, we established a whole exome sequencing (WES) pipeline to rapidly identify disease-associated mutations in patients. Sanger sequencing was applied to identify deep-intronic variants and to verify the co-segregation of WES results within families. We analyzed 26 unrelated patients with different syndromic and non-syndromic clinical manifestations of RD. All patients underwent ophthalmic examinations. We identified nine novel disease-associated sequence variants among 37 variants identified in total. The sequence variants located to 17 different genes. Interestingly, two cases presenting with Stargardt disease carried deep-intronic variants in ABCA4. We have classified 21 variants as pathogenic variants, 4 as benign/likely benign variants, and 12 as variants of uncertain significance. This study highlights the importance of WES-based mutation analyses in RD patients supporting clinical decisions, broadly based genetic diagnosis and support genetic counselling. It is essential for any genetic therapy to expand the mutation spectrum, understand the genes’ function, and correlate phenotypes with genotypes.

Role of Splicing Regulatory Elements and In Silico Tools Usage in the Identification of Deep Intronic Splicing Variants in Hereditary Breast/Ovarian Cancer Genes

The contribution of deep intronic splice-altering variants to hereditary breast and ovarian cancer (HBOC) is unknown. Current computational in silico tools to predict spliceogenic variants leading to pseudoexons have limited efficiency. We assessed the performance of the SpliceAI tool combined with ESRseq scores to identify spliceogenic deep intronic variants by affecting cryptic sites or splicing regulatory elements (SREs) using literature and experimental datasets. Our results with 233 published deep intronic variants showed that SpliceAI, with a 0.05 threshold, predicts spliceogenic deep intronic variants affecting cryptic splice sites, but is less effective in detecting those affecting SREs. Next, we characterized the SRE profiles using ESRseq, showing that pseudoexons are significantly enriched in SRE-enhancers compared to adjacent intronic regions. Although the combination of SpliceAI with ESRseq scores (considering ∆ESRseq and SRE landscape) showed higher sensitivity, the global performance did not improve because of the higher number of false positives. The combination of both tools was tested in a tumor RNA dataset with 207 intronic variants disrupting splicing, showing a sensitivity of 86%. Following the pipeline, five spliceogenic deep intronic variants were experimentally identified from 33 variants in HBOC genes. Overall, our results provide a framework to detect deep intronic variants disrupting splicing.