PEDIA: prioritization of exome data by image analysis

Hsieh TC, Mensah MA, Pantel JT, Aguilar D, Bar O, Bayat A, Becerra-Solano L, Bentzen HB, Biskup S, Borisov O, Braaten O, Ciaccio C, Coutelier M, Cremer K, Danyel M, Daschkey S, Eden HD, Devriendt K, Wilson S, Douzgou S, Đukić D, Ehmke N, Fauth C, Fischer-Zirnsak B, Fleischer N, Gabriel H, Graul-Neumann L, Gripp KW, Gurovich Y, Gusina A, Haddad N, Hajjir N, Hanani Y, Hertzberg J, Hoertnagel K, Howell J, Ivanovski I, Kaindl A, Kamphans T, Kamphausen S, Karimov C, Kathom H, Keryan A, Knaus A, Köhler S, Kornak U, Lavrov A, Leitheiser M, Lyon GJ, Mangold E, Reina PM, Carrascal AM, Mitter D, Herrador LM, Nadav G, Nöthen M, Orrico A, Ott CE, Park K, Peterlin B, Pölsler L, Raas-Rothschild A, Randolph L, Revencu N, Fagerberg CR, Robinson PN, Rosnev S, Rudnik S, Rudolf G, Schatz U, Schossig A, Schubach M, Shanoon O, Sheridan E, Smirin-Yosef P, Spielmann M, Suk EK, Sznajer Y, Thiel C, Thiel G, Verloes A, Vrecar I, Wahl D, Weber I, Winter K, Wiśniewska M, Wollnik B, Yeung MW, Zhao M, Zhu N, Zschocke J, Mundlos S, Horn D, Krawitz PM (2019)

Publication Type: Journal article

Publication year: 2019

Journal

DOI: 10.1038/s41436-019-0566-2

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.

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Involved external institutions

Universitätsklinikum Bonn Germany (DE) Rheinische Friedrich-Wilhelms-Universität Bonn Germany (DE) Foundation of the Carlo Besta Neurological Institute (IRCCS) Italy (IT) Charité - Universitätsmedizin Berlin Germany (DE) UIM Unidad de Investigación Médica en Epidemiología Clínica Mexico (MX) University of Oslo Norway (NO) Tecnológico de Monterrey (ITESM) Mexico (MX) FDNA Inc. United States (USA) (US) Heinrich-Heine-Universität Düsseldorf Germany (DE) Rigshospitalet Denmark (DK) CeGaT GmbH Germany (DE) University of Leeds United Kingdom (GB) Medizinische Universität Innsbruck Austria (AT) Lineagen, Inc. United States (USA) (US) Azienda Unità Sanitaria Locale die Reggio Emilia Italy (IT) Universitätsklinikum Magdeburg A.ö.R. Germany (DE) Université Catholique de Louvain (UCL) Belgium (BE) Children's Hospital Los Angeles United States (USA) (US) Hospital de Requena Spain (ES) Hospital General Universitari de València Spain (ES) Universitätsklinikum Leipzig Germany (DE) Ljubljana University Medical Centre (Ljubljana UMC) / Univerzitetni klinični center Ljubljana Slovenia (SI) Ariel University Israel (IL) Poznan University of Medical Sciences / Uniwersytet Medyczny im. Karola Marcinkowskiego w Poznaniu Poland (PL) Nemours/Alfred I. duPont Hospital for Children United States (USA) (US) Jackson Laboratory for Genomic Medicine United States (USA) (US) Universitätsklinikum Göttingen Germany (DE) Center for Prenatal Diagnosis and Human Genetics Germany (DE) Cliniques universitaires Saint-Luc (CHU St-Luc) Belgium (BE) Berliner Institut für Gesundheitsforschung (BIH) Germany (DE) Odense Universitetshospital (OUH) Denmark (DK) Cold Spring Harbor Laboratory United States (USA) (US) Gertner Institute Israel (IL) Russian Academy of Medical Sciences Russian Federation (RU) Medical University Sofia / Медицински университет Bulgaria (BG) Hôpital Universitaire Robert-Debré France (FR) Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven Belgium (BE) Center for Human Genetics and Laboratory Diagnostics (AHC) Germany (DE) GeneTalk GmbH Germany (DE) Universitätsklinikum Hamburg-Eppendorf (UKE) Germany (DE) State Republican Scientific and Practical Center "Mother and Child" / ДУ Рэспубліканскі навукова-практычны цэнтр "Маці і дзіця" Belarus (BY) Eberhard Karls Universität Tübingen Germany (DE) University of Colorado Anschutz Medical Campus United States (USA) (US) Azienda ospedaliero-universitaria Senese Italy (IT) Hospital Universitario Miguel Servet Spain (ES) Health Innovation Manchester United Kingdom (GB)

How to cite

APA:

Hsieh, T.C., Mensah, M.A., Pantel, J.T., Aguilar, D., Bar, O., Bayat, A.,... Krawitz, P.M. (2019). PEDIA: prioritization of exome data by image analysis. Genetics in Medicine. https://dx.doi.org/10.1038/s41436-019-0566-2

MLA:

Hsieh, Tzung Chien, et al. "PEDIA: prioritization of exome data by image analysis." Genetics in Medicine (2019).

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