Predicting the impact of rare variants on RNA splicing in CAGI6
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Predicting the impact of rare variants on RNA splicing in CAGI6. / Lord, Jenny; Oquendo, Carolina Jaramillo; Wai, Htoo A.; Douglas, Andrew G.L.; Bunyan, David J.; Wang, Yaqiong; Hu, Zhiqiang; Zeng, Zishuo; Danis, Daniel; Katsonis, Panagiotis; Williams, Amanda; Lichtarge, Olivier; Chang, Yuchen; Bagnall, Richard D.; Mount, Stephen M.; Matthiasardottir, Brynja; Lin, Chiaofeng; Hansen, Thomas van Overeem; Leman, Raphael; Martins, Alexandra; Houdayer, Claude; Krieger, Sophie; Bakolitsa, Constantina; Peng, Yisu; Kamandula, Akash; Radivojac, Predrag; Baralle, Diana.
I: Human Genetics, 2024.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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 -
ID: 382438193