CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs)

The Genetics of Neurodevelopmental Disorders Lab in Padua provided a new intellectual disability (ID) Panel challenge for computational methods to predict patient phenotypes and their causal variants in the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6). Eight research teams submitted a total of 30 models to predict phenotypes based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. Here, we assess the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and their causal variants. We also evaluated predictions for possible genetic causes in patients without a clear genetic diagnosis. Like the previous ID Panel challenge in CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (Pathogenic/Likely Pathogenic, Variants of Uncertain Significance and Risk Factors) were provided. The phenotypic traits and variant data of 150 patients from the CAGI5 ID Panel Challenge were provided as training set for predictors. The CAGI6 challenge confirms CAGI5 results that predicting phenotypes from gene panel data is highly challenging, with AUC values close to random, and no method able to predict relevant variants with both high accuracy and precision. However, a significant improvement is noted for the best method, with recall increasing from 66% to 82%. Several groups also successfully predicted difficult-to-detect variants, emphasizing the importance of variants initially excluded by the Padua NDD Lab.

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Publication Details

Journal
Human Genetics
Published
2025-01-09
DOI
https://doi.org/10.1007/s00439-024-02722-w
Citations
2
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
3.63

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article

CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs)

Damiano Piovesan, Shaowen Zhu, Azza Althagafi, Olivier Lichtarge et al.
2 citations
Human Genetics
Genomics and Rare Diseases
3.63
article

CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs)

Damiano Piovesan, Shaowen Zhu, Azza Althagafi, Olivier Lichtarge, Panagiotis Katsonis, Yexian Zhang, Emanuela Leonardi, Maria Cristina Aspromonte, Giulia Babbi, Robert Hoehndorf, Pier Luigi Martelli, Xian Su, Uma Sunderam, Rita Casadio, Samuele Bovo, Sumyyah Toonsi, Sean D. Mooney, Silvio C. E. Tosatto, Alessandra Murgia, Maxat Kulmanov, Vikas Pejaver, Wuwei Tan, Wesley Surento, Qi Li, Yang Shen, Amanda M. Williams, Alessio Del Conte, Maggie Haitian Wang, Rajgopal Srinivasan
article en
2 citations

Abstract

The Genetics of Neurodevelopmental Disorders Lab in Padua provided a new intellectual disability (ID) Panel challenge for computational methods to predict patient phenotypes and their causal variants in the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6). Eight research teams submitted a total of 30 models to predict phenotypes based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. Here, we assess the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and their causal variants. We also evaluated predictions for possible genetic causes in patients without a clear genetic diagnosis. Like the previous ID Panel challenge in CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (Pathogenic/Likely Pathogenic, Variants of Uncertain Significance and Risk Factors) were provided. The phenotypic traits and variant data of 150 patients from the CAGI5 ID Panel Challenge were provided as training set for predictors. The CAGI6 challenge confirms CAGI5 results that predicting phenotypes from gene panel data is highly challenging, with AUC values close to random, and no method able to predict relevant variants with both high accuracy and precision. However, a significant improvement is noted for the best method, with recall increasing from 66% to 82%. Several groups also successfully predicted difficult-to-detect variants, emphasizing the importance of variants initially excluded by the Padua NDD Lab.

Human GeneticsVol. 144(2-3)
University of Padua (IT), Chinese University of Hong Kong (HK), Taif University (SA), Baylor College of Medicine (US), University of Washington Medical Center (US), City University of Hong Kong, Shenzhen Research Institute (CN), Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IT), National Research Council (IT), Genomic Health (United States) (US), Tata Consultancy Services (India) (IN), King Abdullah University of Science and Technology (SA), University of Hong Kong (HK), Texas A&M University (US), University of Bologna (IT), Icahn School of Medicine at Mount Sinai (US)
European Commission, Università degli Studi di Padova, National Institutes of Health, National Institute of General Medical Sciences
Quality Education
Openalex Percentile: Top 10%
Genomics and Rare Diseases
3.63
DOIOpen Access PDFOpenAlex
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