CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases
| Autor: | Zhiqiang Hu, Jesse M. Hunter, Olivier Lichtarge, Sean D. Mooney, Aashish N. Adhikari, Steven E. Brenner, Rita Casadio, Yizhou Yin, Lipika R. Pal, Uma Sunderam, Panagiotis Katsonis, Predrag Radivojac, Thomas Joseph, Giulia Babbi, Naveen Sivadasan, Constantina Bakolitsa, Vangala G. Saipradeep, Laura Kasak, John Moult, Julian Gough, M. Stephen Meyn, Pier Luigi Martelli, Jennifer Poitras, Rupa A Udani, Jan Zaucha, Rafael F. Guerrero, Yuxiang Jiang, Aditya Rao, Sujatha Kotte, Kunal Kundu |
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| Přispěvatelé: | Kasak L., Hunter J.M., Udani R., Bakolitsa C., Hu Z., Adhikari A.N., Babbi G., Casadio R., Gough J., Guerrero R.F., Jiang Y., Joseph T., Katsonis P., Kotte S., Kundu K., Lichtarge O., Martelli P.L., Mooney S.D., Moult J., Pal L.R., Poitras J., Radivojac P., Rao A., Sivadasan N., Sunderam U., Saipradeep V.G., Yin Y., Zaucha J., Brenner S.E., Meyn M.S. |
| Rok vydání: | 2019 |
| Předmět: |
Male
Adolescent In silico Genomic data Computational biology Biology Undiagnosed Diseases Genome Article 03 medical and health sciences Databases Genetic SickKid pediatric rare disease Genetics Humans Computer Simulation Genetic Predisposition to Disease Child Gene Genetics (clinical) 030304 developmental biology Disease gene 0303 health sciences Whole Genome Sequencing variant interpretation 030305 genetics & heredity Computational Biology Genetic Variation Pathogenicity Phenotype ddc phenotype prediction Child Preschool New disease CAGI Female whole-genome sequencing data |
| Zdroj: | Hum Mutat |
| ISSN: | 1098-1004 1059-7794 |
| Popis: | Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes. |
| Databáze: | OpenAIRE |
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