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

Author:

Aspromonte Maria Cristina1,Conte Alessio Del1,Zhu Shaowen2,Tan Wuwei2,Shen Yang2,Zhang Yexian3,Li Qi3,Wang Maggie Haitian3,Babbi Giulia4,Bovo Samuele5,Martelli Pier Luigi4,Casadio Rita4,Althagafi Azza6,Toonsi Sumyyah6,Kulmanov Maxat6,Hoehndorf Robert6,Katsonis Panagiotis7,Williams Amanda7,Lichtarge Olivier7,Xian Su8,Surento Wesley8,Pejaver Vikas9,Mooney Sean D.8,Sunderam Uma10,Sriniva Rajgopal10,Murgia Alessandra11,Piovesan Damiano1,Tosatto Silvio C. E.1,Leonardi Emanuela1

Affiliation:

1. Department of Biomedical Sciences, University of Padova

2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843

3. CUHK Shenzhen Research Institute, Shenzhen

4. Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna

5. Department of Agricultural and Food Sciences, University of Bologna

6. Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23

7. Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030

8. Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195

9. Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029

10. Innovation Labs, Tata Consultancy Services, Hyderabad

11. Department of Women's and Children's Health, University of Padova

Abstract

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.

Publisher

Research Square Platform LLC

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