Conclusions
In this paper, we have presented functional evidence for spliceogenic variants that are generally overlooked in clinical genetic testing and/or reporting, including: variants that affect SREs, abrogate BP sites, or activate pseudoexons. Bioinformatic analysis considering variant effects at the mRNA level may help prioritize likely functional variants currently annotated as (likely) benign or VUS for additional functional and clinical analyses. Further, clinical diagnostic laboratories may need to consider expanding their sequencing coverage and/or variant annotation to include BP window and deep intronic regions to detect additional pathogenic intronic variants, particularly when strongly indicated by patient presentation. However, improving the low performance of current predictors is a challenge due to the limited size of experimentally validated training data. Clearly, experimental studies that assess variants outside of the donor and acceptor splice site motifs for splicing mechanisms are needed to further calibrate algorithms, and to improve prediction of variant effect. We have shown that results from a published large-scale saturation genome editing experiment can be used to map SREs, to assess the performance of bioinformatic predictors, and inform development of a prioritization workflow to detect variants that impact SREs. As more such data become available, we anticipate that the expansion of training datasets will lead to improvements in approaches to predict variant effect/s. Such advances will be critical to improve the sensitivity and specificity of bioinformatic prediction of variant effect/s on SREs, BP sites, and pseudoexon usage, and thereby improve assessment of variant pathogenicity.