Volume 40, Issue 9 pp. 1330-1345
SPECIAL ARTICLE
Full Access

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

Marco Carraro

Marco Carraro

Department of Biomedical Sciences, University of Padua, Padua, Italy

These authors contributed equally to this study.

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Alexander Miguel Monzon

Alexander Miguel Monzon

Department of Biomedical Sciences, University of Padua, Padua, Italy

These authors contributed equally to this study.

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Luigi Chiricosta

Luigi Chiricosta

Department of Biomedical Sciences, University of Padua, Padua, Italy

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Francesco Reggiani

Francesco Reggiani

Department of Biomedical Sciences, University of Padua, Padua, Italy

Department of Information Engineering, University of Padua, Padua, Italy

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Maria Cristina Aspromonte

Maria Cristina Aspromonte

Department of Woman and Child Health, University of Padua, Padua, Italy

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Mariagrazia Bellini

Mariagrazia Bellini

Department of Woman and Child Health, University of Padua, Padua, Italy

Fondazione Istituto di Ricerca Pediatrica (IRP), Città della Speranza, Padova, Italy

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Kymberleigh Pagel

Kymberleigh Pagel

Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts

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Yuxiang Jiang

Yuxiang Jiang

Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts

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Predrag Radivojac

Predrag Radivojac

Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts

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Kunal Kundu

Kunal Kundu

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

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Lipika R. Pal

Lipika R. Pal

Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland

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Yizhou Yin

Yizhou Yin

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

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Ivan Limongelli

Ivan Limongelli

enGenome srl, via Ferrata 5, Pavia, Italy

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Gaia Andreoletti

Gaia Andreoletti

Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland

Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland

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John Moult

John Moult

Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland

Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland

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Stephen J. Wilson

Stephen J. Wilson

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas

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Panagiotis Katsonis

Panagiotis Katsonis

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas

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Olivier Lichtarge

Olivier Lichtarge

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas

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Jingqi Chen

Jingqi Chen

Department of Plant and Microbial Biology, University of California, Berkeley, California

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Yaqiong Wang

Yaqiong Wang

Department of Plant and Microbial Biology, University of California, Berkeley, California

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Zhiqiang Hu

Zhiqiang Hu

Department of Plant and Microbial Biology, University of California, Berkeley, California

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Steven E. Brenner

Steven E. Brenner

Department of Plant and Microbial Biology, University of California, Berkeley, California

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Carlo Ferrari

Carlo Ferrari

Department of Information Engineering, University of Padua, Padua, Italy

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Alessandra Murgia

Alessandra Murgia

Department of Woman and Child Health, University of Padua, Padua, Italy

Fondazione Istituto di Ricerca Pediatrica (IRP), Città della Speranza, Padova, Italy

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Silvio C.E. Tosatto

Corresponding Author

Silvio C.E. Tosatto

Department of Biomedical Sciences, University of Padua, Padua, Italy

Institute of Neuroscience, National Research Council (CNR), Padua, Italy

These authors contributed equally to this study.

Correspondence Silvio Tosatto, Department of Biomedical Sciences, University of Padua. Viale G. Colombo 3, 35131, Padua, Italy. Email: [email protected]

Emanuela Leonardi, Department of Woman and Child Health, University of Padua, Padua. Corso Stati Uniti, 4, 35127, Padua, Italy. Email: [email protected]

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Emanuela Leonardi

Corresponding Author

Emanuela Leonardi

Department of Woman and Child Health, University of Padua, Padua, Italy

Fondazione Istituto di Ricerca Pediatrica (IRP), Città della Speranza, Padova, Italy

These authors contributed equally to this study.

Correspondence Silvio Tosatto, Department of Biomedical Sciences, University of Padua. Viale G. Colombo 3, 35131, Padua, Italy. Email: [email protected]

Emanuela Leonardi, Department of Woman and Child Health, University of Padua, Padua. Corso Stati Uniti, 4, 35127, Padua, Italy. Email: [email protected]

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First published: 30 May 2019
Citations: 12

Abstract

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.

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