ArticleGenome medicine2021
CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores.
Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 426 papers, 2 of them syntheses that pooled it.
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Who cites it
426 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- From Pathogenicity to Mechanism: A Variant Interpretation Framework for Monogenic Epilepsy.Clinical genetics · 2026Article
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- Splice-site variants in neurology: from molecular mechanisms to clinical interpretation - a focused review.Neurogenetics · 2026Review
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- The Global Parkinson's Disease Genetics (GP2) Genome Browser.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Uncovering phenotypic expansion in AXIN2-related disorders through precision animal modeling.Genetics in medicine : official journal of the American College of Medical Genetics · 2026Article
- Prediction of human missense variant effects from functional evidence.Nature genetics · 2026Article
- Common and rare genetic variant associations with cognitive performance across development in British birth cohorts.Nature human behaviour · 2026Article
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- Mild form of aromatic L-amino acid decarboxylase deficiency.BMJ case reports · 2026Article
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- Exome-wide association study reveals common and rare coding variants shaping chronic pain in 327,642 UK biobank participants.Genome medicine · 2026Article
- Atlas of glomerular disease-specific genetic effects on blood transcriptome.medRxiv : the preprint server for health sciences · 2026Article
366 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
backgroundSplicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies.
methodsIt has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants.
resultsWe integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu ), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. 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.
conclusionsWhile 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. 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.
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