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[Preprint]. 2023 Aug 2:rs.3.rs-3209168.
doi: 10.21203/rs.3.rs-3209168/v1.

CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs)

Maria Cristina Aspromonte  1 , Alessio Del Conte  1 , Shaowen Zhu  2 , Wuwei Tan  2 , Yang Shen  2 , Yexian Zhang  3 , Qi Li  3 , Maggie Haitian Wang  3 , Giulia Babbi  4 , Samuele Bovo  5 , Pier Luigi Martelli  4 , Rita Casadio  4 , Azza Althagafi  6 , Sumyyah Toonsi  6 , Maxat Kulmanov  6 , Robert Hoehndorf  6 , Panagiotis Katsonis  7 , Amanda Williams  7 , Olivier Lichtarge  7 , Su Xian  8 , Wesley Surento  8 , Vikas Pejaver  9 , Sean D Mooney  8 , Uma Sunderam  10 , Rajgopal Srinivasan  10 , Alessandra Murgia  11 , Damiano Piovesan  1 , Silvio C E Tosatto  1 , Emanuela Leonardi  1
Affiliations

CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs)

Maria Cristina Aspromonte et al. Res Sq. .

Update in

Abstract

In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, 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. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.

Keywords: CAGI; Neurodevelopmental Disorders; gene-panel; phenotype prediction; variants interpretation.

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Conflict of interest statement

Competing Interests The authors declare no competing interests.

Figures

Figure 1
Figure 1. Performance of the eight groups matching the specific phenotype in 415 patients.
Colors represent the proportion and number of groups which correctly predicted the phenotype.
Figure 2
Figure 2. Overall performance for each submission on phenotype prediction.
(A) Each cell represents the mean AUC values of the ROC for the 1000 bootstrap iterations. The color scale ranges from dark (+1, perfect performance) to white (0, bad performance). White means random performance. (B) Each cell represents MCC values. The color scale ranges from green (+1, perfect correlation) to red (−1, negative correlation). White means no better than random prediction. AUC, area under ROC curve; MCC, Matthew correlation coefficient.
Figure 3
Figure 3. Distribution of the ROC curves for all seven clinical traits.
The best performant submission for each phenotype, based on the AUC value, is shown.
Figure 4
Figure 4. Performance of the eight groups matching the phenotype in 217 patients carrying only disease causing variants.
Colors represent the proportion and number of groups which correctly predicted the phenotype in patients carrying Disease Causing
Figure 5
Figure 5. Predicted variants distribution.
Category “Experimental” is the amount of variants which were identified and classified by the Padua NDD lab. Each bar represents the amount of variants and types predicted by each submission. NDD, neurodevelopmental disorder.
Figure 6
Figure 6. Performance of the eight groups predicting the correct variants.
The amount of variants was calculated for each category (DC, LP, CF). Colors indicate the proportion and number of groups which correctly predicted those variants
See this image and copyright information in PMC

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