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. 2025 Mar;144(2-3):243-251.
doi: 10.1007/s00439-023-02624-3. Epub 2024 Jan 3.

Predicting the impact of rare variants on RNA splicing in CAGI6

Jenny Lord  1 , Carolina Jaramillo Oquendo  1 , Htoo A Wai  1 , Andrew G L Douglas  1   2 , David J Bunyan  1   3 , Yaqiong Wang  4 , Zhiqiang Hu  5 , Zishuo Zeng  6 , Daniel Danis  7 , Panagiotis Katsonis  8 , Amanda Williams  8 , Olivier Lichtarge  8 , Yuchen Chang  9   10 , Richard D Bagnall  9   10 , Stephen M Mount  11 , Brynja Matthiasardottir  12   13 , Chiaofeng Lin  14 , Thomas van Overeem Hansen  15   16 , Raphael Leman  17   18 , Alexandra Martins  19 , Claude Houdayer  19   20 , Sophie Krieger  17   18 , Constantina Bakolitsa  5 , Yisu Peng  21 , Akash Kamandula  21 , Predrag Radivojac  21 , Diana Baralle  22   23
Affiliations

Predicting the impact of rare variants on RNA splicing in CAGI6

Jenny Lord et al. Hum Genet. 2025 Mar.

Abstract

Variants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variant's impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here, we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. Several challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

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Conflict of interest statement

Declarations. Competing interests: The authors have no relevant financial or non-financial interests to disclose. On behalf of all authors, the corresponding author states that there is no conflict of interest. Ethics approval: Informed consent was provided for all patients for splicing studies to be conducted. Patients were recruited from Wessex Regional Genetics Laboratory in Salisbury (52 variants) or the Splicing and Disease research study (12 variants) at the University of Southampton, ethically approved by the Health Research Authority (IRAS Project ID 49685, REC 11/SC/0269) and by the University of Southampton (ERGO ID 23056).

Figures

Fig. 1
Fig. 1
Schematic diagram showing locations of the 56 challenge variants in relation to their nearest splice site, with colour indicating whether (yellow) or not (green) each variant was determined experimentally to impact splicing
Fig. 2
Fig. 2
Receiver operating characteristic (ROC) curves of model performance based on prediction scores. For Area Under Curve (AUC), see Table 2
Fig. 3
Fig. 3
Proportion of variants correctly predicted by each method in the different regions (near acceptor, near donor, exonic and intronic distant)
Fig. 4
Fig. 4
Variants across the splicing region coloured by the number of prediction methods (out of the 12 challenge entrants) that correctly predicted the splicing outcome
See this image and copyright information in PMC

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