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Hum Mutat. 2019 Sep;40(9):1330-1345. doi: 10.1002/humu.23823. Epub 2019 Jul 3.

Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge.

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Department of Biomedical Sciences, University of Padua, Padua, Italy.
Department of Information Engineering, University of Padua, Padua, Italy.
Department of Woman and Child Health, University of Padua, Padua, Italy.
Fondazione Istituto di Ricerca Pediatrica (IRP), Città della Speranza, Padova, Italy.
Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts.
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.
Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland.
enGenome srl, via Ferrata 5, Pavia, Italy.
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
Department of Plant and Microbial Biology, University of California, Berkeley, California.
Institute of Neuroscience, National Research Council (CNR), Padua, Italy.


The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.


community challenge; critical assessment; genetic testing; phenotype prediction; variant interpretation


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