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S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing

Nature Genetics (2019) | Download Citation

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Abstract

Exome analysis of patients with a likely monogenic disease does not identify a causal variant in over half of cases. Splice-disrupting mutations make up the second largest class of known disease-causing mutations. Each individual (singleton) exome harbors over 500 rare variants of unknown significance (VUS) in the splicing region. The existing relevant pathogenicity prediction tools tackle all non-coding variants as one amorphic class and/or are not calibrated for the high sensitivity required for clinical use. Here we calibrate seven such tools and devise a novel tool called Splicing Clinically Applicable Pathogenicity prediction (S-CAP) that is over twice as powerful as all previous tools, removing 41% of patient VUS at 95% sensitivity. We show that S-CAP does this by using its own features and not via meta-prediction over previous tools, and that splicing pathogenicity prediction is distinct from predicting molecular splicing changes. S-CAP is an important step on the path to deriving non-coding causal diagnoses.

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Code availability

The S-CAP code is publiclally available at Bitbucket (see URLs) for non-commercial purposes.

Data availability

S-CAP scores for all rare variants in the predefined splicing region in the human genome, along with the source code repository, training and test set variant IDs, and final trained models for the S-CAP classifier, will be made available via the S-CAP website on publication.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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          Acknowledgements

          We thank R. Dror for support, as well as A. Wenger, M. Berger, J. Birgmeier and all members of the Bejerano laboratory for useful discussions, feedback, and advice. P.D.S. and D.N.C. receive financial support from Qiagen through a license agreement with Cardiff University. This work was funded in part by a Stanford Graduate Fellowship and a Computational and Evolutionary Genomics Fellowship to K.A.J., a Stanford Graduate Fellowship to J.M.P., and Stanford Pediatrics Department, DARPA, a Packard Foundation and Microsoft Faculty Fellowships to G.B.

          Author information

          1. These authors contributed equally: Karthik A. Jagadeesh, Joseph M. Paggi.

          Affiliations

          1. Department of Computer Science, Stanford University, Stanford, CA, USA

            • Karthik A. Jagadeesh
            • , Joseph M. Paggi
            •  & Gill Bejerano

          2. Department of Biology, Stanford University, Stanford, CA, USA

            • James S. Ye

          3. Institute of Medical Genetics, Cardiff University, Cardiff, UK

            • Peter D. Stenson
            •  & David N. Cooper

          4. Department of Pediatrics, Stanford University, Stanford, CA, USA

            • Jonathan A. Bernstein
            •  & Gill Bejerano

          5. Department of Developmental Biology, Stanford University, Stanford, CA, USA

            • Gill Bejerano

          6. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

            • Gill Bejerano

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          Contributions

          K.A.J., J.M.P., and G.B. designed the study. K.A.J., J.M.P., and J.S.Y. developed the features, trained the model, and evaluated the results. P.D.S. and D.N.C. curated the HGMD data. J.A.B. provided the patient exome cases feedback. K.A.J., J.M.P., and G.B. wrote the manuscript. All authors reviewed the manuscript.

          Competing interests

          The authors declare no competing interests.

          Corresponding author

          Correspondence to Gill Bejerano.

          Integrated supplementary information

          1. Supplementary Figure 1 Framework for training and evaluating six pathogenicity models.

            The splicing region is split into 6 independent regions as defined in Fig. 1c, and a separate model is trained for variants residing in each region. Given a set of variants to be scored, we calculate the S-CAP score for each variant by using the corresponding model associated with the region where the variant is found.

          2. Supplementary Figure 2 Overall precision-recall curve per region for splicing-pathogenicity classification.

            The size of training and testing data for each model is specified in Table 3. Precision measures the number of correctly classified pathogenic variants over the total number of variants classified as pathogenic. This measure reflects the class imbalance as there are over two orders of magnitude more benign variants that pathogenic variants. S-CAP improves on existing metrics over the precision-recall curve in all splicing regions.

          3. Supplementary Figure 3 Performance of S-CAP compared with MutPred Splice.

            MutPred Splice is a computational method for predicting the pathogenicity of exonic synonymous variants. MutPred Splice was trained by its authors using a subset of the pathogenic data used to train/test S-CAP. As a result, we need to independently test MutPred Splice on a set of variants that was not used in its training. This test set comprises 78 rare synonymous variants from HGMD added to the database in 2013 or later and 10,000 rare synonymous variants present in healthy controls in ExAC. On this set S-CAP improves on MutPred Splice by 5.7% when comparing the overall AUC. S-CAP performs especially well in the high sensitivity domain and improves on the MutPred Splice hsr-AUC by 204%.

          4. Supplementary Figure 4 S-CAP performance in the high-sensitivity region.

            The size of training and testing data for each model is specified in Table 3. The hsr-AUC curve is formed by subsetting the overall AUC to just the region where pathogenic variants are correctly classified over 95% of the time. An hsr-AUC curve is calculated for each of the regions as defined in Fig. 1c. S-CAP improves on the next best method’s hsr-AUC by 185% in the 3′ intronic region (a), 40.7% in the 3′ core sites (b), 224% in the exonic region (c), 31.6% in the 5′ core sites (d), 72.7% in the 5′ extended region (e) and 120% in the 5′ intronic region (f). None of the existing methods consistently outperforms the others.

          5. Supplementary Figure 5 S-CAP performance in 3′-core and 5′-core regions without zygosity feature.

            The size of training and testing data for each model is specified in Table 3 in the rows corresponding to 3′ Core and 5′ Core regions. Our model without the zygosity feature outperforms existing metrics in the 3′ core and 5′ core regions by up to 23.4% in AUC and by up to 36% in the hsrAUC. The performance in 3′ core region is slightly better than in the 5′ core region. Incorporating the variant zygosity feature boosts classification performance by 5.4% (Fig. 2, Supplementary Fig. 4).

          6. Supplementary Figure 6 Performance on recessive and dominant classes.

            The size of training data for the 3′ Core and 5′ Core models are specified in Table 3. The 3′ Core Dominant and Recessive models are evaluated over 787 and 131 pathogenic variants and 8,421 and 91 benign variants respectively. The 5′ Core Dominant and Recessive models are evaluated over 938 and 218 pathogenic variants and 9,085 and 103 benign variants respectively. The distribution of the underlying features is dramatically different for dominant and recessive variants. This results in a big difference in performance when classifying recessive and benign variants in the core splice site regions. S-CAP achieves an AUC (a) of 0.818 on dominant tagged variants and (b) of 0.862 on recessive tagged variants in the 3′ core region. There is a similar performance difference in the 5′ core region where S-CAP achieves an AUC (c) of 0.809 on dominant tagged variants (d) and of 0.875 on recessive tagged variants. In the high sensitivity region, S-CAP achieves an hsr-AUC (e, f) of 0.270 on dominant tagged variants and of 0.491 on recessive tagged variants in the 3′ core region (g, h) and of 0.229 on dominant tagged variants and of 0.510 of recessive tagged variants in the 5′ core region. S-CAP AUC and hsr-AUC are consistently better than those of all other tools, and no existing method consistently outperforms the others.

          7. Supplementary Figure 7 S-CAP maintains a 41% decrease in VUS over 1000 Genomes superpopulations.

            Each boxplot is defined so the centerline is at the median, the bottom of the box marks the 25th %-ile, the top of the box marks the 75th %-ile and the whiskers stretch to denote 1.5 times the interquartile range (IQR). S-CAP performance in reducing VUS is consistent across 5 different super populations from the 1000 genomes project despite European bias in the gnomAD control database. EUR, European (n = 503). AFR, African (n = 661). SAS, South Asian (n = 489). AMR, Ad Mixed American (n = 347). EAS, East Asian (n = 504).

          8. Supplementary Figure 8 S-CAP test-set performance resembles performance during cross-validation.

            The cross validation performance is measured over benign and pathogenic training variants as described in column 1 in Table 3. The cross validation performance is measured as the mean performance over all 5 folds. We find the performance is similar to between the cross validation and the test set reported performance in all 6 regions.

          Supplementary information

          1. Supplementary Information

            Supplementary Figures 1–8, Supplementary Tables 1–5 and Supplementary Note

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          DOI

          https://doi.org/10.1038/s41588-019-0348-4