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Genet Med. 2019 Jun 5. doi: 10.1038/s41436-019-0566-2. [Epub ahead of print]

PEDIA: prioritization of exome data by image analysis.

Author information

1
Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany.
2
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Medical Genetics and Human Genetics, Berlin, Germany.
3
Berlin Institute of Health (BIH), Berlin, Germany.
4
Centro de Cáncer de Mama, Tecnológico de Monterrey, Monterrey, Mexico.
5
FDNA Inc., Boston, MA, USA.
6
Rigshospitalet, Department of Neurology, Copenhagen, Denmark.
7
Unidad de Investigación Médica en Medicina Reproductiva, Mexico City, Mexico.
8
Centre for Medical Ethics, Faculty of Medicine and the Norwegian Research Center for Computers and Law, Faculty of Law, University of Oslo, Oslo, Norway.
9
CeGaT GmbH, Tübingen, Germany.
10
Faculty of Medicine, Department of Medical Genetics, University of Oslo, Blindern, Oslo, Norway.
11
Developmental Neurology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
12
Department of Human Genetics, University Hospital of Bonn, Bonn, Germany.
13
Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
14
Department of Human Genetics, KU Leuven, Leuven, Belgium.
15
Department of Human Genetics, University of Hamburg, Hamburg, Germany.
16
Manchester Centre for Genomic Medicine, St Mary's Hospital, Central Manchester University Hospitals NHS Foundation Trust Manchester Academic Health Sciences Centre, Manchester, United Kingdom.
17
School of Biological Sciences, Division of Evolution and Genomic Sciences, University of Manchester, Manchester, United Kingdom.
18
Division of Human Genetics, Medical University of Innsbruck, Innsbruck, Austria.
19
Center for Genomics and Transcriptomics, Eberhard Karls University of Tübingen, Tübingen, Germany.
20
A. I. duPont Hospital for Children, Wilmington, DE, USA.
21
National Research and Applied Medicine Centre 'Mother and Child'', Minsk, Belarus.
22
Lineagen, Salt Lake City, Utah, USA.
23
Clinical Genetics Unit, AUSL-IRCCS Reggio Emilia, Reggio Emilia, Italy.
24
Center for Chronically Sick Children (Sozialpädiatrisches Zentrum, SPZ), Charité - Universitätsmedizin Berlin, Berlin, Germany.
25
GeneTalk, Bonn, Germany.
26
University Hospital Magdeburg, Magdeburg, Germany.
27
Children's Hospital of Los Angeles, Los Angeles, CA, USA.
28
Department of Pediatrics, Medical University of Sofia, Sofia, Bulgaria.
29
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, NeuroCure Clinical Research Center, Berlin, Germany.
30
Research Institute of Medical Genetics of Russian Academy of Medical Sciences, Moscow, Russian Federation.
31
Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Woodbury, New York, USA.
32
Institute of Human Genetics, University of Bonn, Bonn, Germany.
33
Hospital General Universitario De Valencia, Valencia, Spain.
34
Hospital General De Requena, Servicio Pediatría, Spain.
35
University Hospital Leipzig, Leipzig, Germany.
36
Hospital Universitario Miguel Servet, Zaragoza, Spain.
37
Azienda Ospedaliera Universitaria Senese, Siena, Italy.
38
Department of Pediatrics and Neurology, University of Colorado School of Medicine, Colorado, Aurora, USA.
39
Clinical Institute of Medical Genetics, University Medical Centre Ljubljana, Ljubljana, Slovenia.
40
The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel-Hashomer, Israel.
41
Center for Human Genetics, University Hospital, Université Catholique de Louvain, Brussels, Belgium.
42
Odense University Hospital, Odense, Denmark.
43
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
44
School of Medicine, University of Leeds, Leeds, United Kingdom.
45
Genomic Bioinformatics Laboratory, Department of Molecular Biology, Ariel University, Ariel, Israel.
46
Center for Prenatal Diagnosis and Human Genetics, Berlin, Germany.
47
Cliniques universitaires Saint Luc UCL, Bruxelles, Belgium.
48
Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg FAU, Erlangen, Erlangen, Germany.
49
Hopital Robert Debré, Paris, France.
50
Center for Human Genetics and Laboratory Diagnostics Dr. Klein, Dr. Rost and Colleagues, Martinsried, Germany.
51
Poznañ University of Medical Sciences, Poznañ, Poland.
52
University Medical Center Göttingen, Göttingen, Germany.
53
Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany. pkrawitz@uni-bonn.de.

Abstract

PURPOSE:

Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.

METHODS:

Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.

RESULTS:

The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene.

CONCLUSION:

Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.

KEYWORDS:

computer vision; deep learning; dysmorphology; exome diagnostics; variant prioritization

PMID:
31164752
DOI:
10.1038/s41436-019-0566-2

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