TY - JOUR

T1 - Predicting the impact of rare variants on RNA splicing in CAGI6

AU - Lord, Jenny

AU - Oquendo, Carolina Jaramillo

AU - Wai, Htoo A.

AU - Douglas, Andrew G.L.

AU - Bunyan, David J.

AU - Wang, Yaqiong

AU - Hu, Zhiqiang

AU - Zeng, Zishuo

AU - Danis, Daniel

AU - Katsonis, Panagiotis

AU - Williams, Amanda

AU - Lichtarge, Olivier

AU - Chang, Yuchen

AU - Bagnall, Richard D.

AU - Mount, Stephen M.

AU - Matthiasardottir, Brynja

AU - Lin, Chiaofeng

AU - Hansen, Thomas van Overeem

AU - Leman, Raphael

AU - Martins, Alexandra

AU - Houdayer, Claude

AU - Krieger, Sophie

AU - Bakolitsa, Constantina

AU - Peng, Yisu

AU - Kamandula, Akash

AU - Radivojac, Predrag

AU - Baralle, Diana

N1 - Publisher Copyright: © 2024, The Author(s).

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

U2 - 10.1007/s00439-023-02624-3

DO - 10.1007/s00439-023-02624-3

M3 - Journal article

C2 - 38170232

AN - SCOPUS:85181257092

JO - Human Genetics

JF - Human Genetics

SN - 0340-6717

ER -