![]() To address this, the field increasingly relies on automated approaches to prioritize causal variants. Given millions of variants in a human genome and myriad molecular processes through which each variant might act, pinpointing the genetic changes causal for a specific phenotype down to a set or single variant remains difficult. However, splicing is just one of many biological processes that can be impacted by genetic variants, with others including protein function, distal and proximal regulation of cell type-specific transcription, transcript stability, and DNA replication. Variants disrupting splicing are established contributors to rare genetic disease and more generally variants modulating splicing substantially contribute to phenotypic variation with respect to common traits and disease risk. The dynamics of both canonical and alternative splicing can be influenced or disrupted by genomic sequence variation. Exons with high psi values are associated with stronger conservation and depletion of loss-of-function variation. For each exon or exon segment, the quantity “percent spliced-in” (psi) is defined as the relative fraction of transcripts this segment is included in. Various studies show that more than 90% of genes with multiple exons undergo alternative splicing, i.e., not all exons are included in every transcript. At some genes, multiple acceptor or donor sites compete, such that multiple different alternative transcripts can be formed from one gene, i.e., alternative splicing. The 3′-donor site binds to the acceptor and connects the two exons, thereby releasing the intron. While variations of this process have been described, the principal mechanism of RNA splicing is that the branchpoint located in the spliced intron binds to the 5′-donor site (relative to the intron), forming a lariat intermediate. One of the key steps involved in the regulation of eukaryotic gene expression is RNA splicing, the transformation of transcribed pre-mRNA into translatable mRNA through the removal of intronic sequences. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction. While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion cadd.gs.), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. ![]() Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. To address this, various methods aim to predict variant effects on splicing. ![]() Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins.
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