PEDIA: prioritization of exome data by image analysis

Tzung Chien Hsieh, Martin A. Mensah, Jean T. Pantel, Dione Aguilar, Omri Bar, Allan Bayat, Luis Becerra-Solano, Heidi B. Bentzen, Saskia Biskup, Oleg Borisov, Oivind Braaten, Claudia Ciaccio, Marie Coutelier, Kirsten Cremer, Magdalena Danyel, Svenja Daschkey, Hilda David Eden, Koenraad Devriendt, Sandra Wilson, Sofia DouzgouDejan Đukić, Nadja Ehmke, Christine Fauth, Björn Fischer-Zirnsak, Nicole Fleischer, Heinz Gabriel, Luitgard Graul-Neumann, Karen W. Gripp, Yaron Gurovich, Asya Gusina, Nechama Haddad, Nurulhuda Hajjir, Yair Hanani, Jakob Hertzberg, Konstanze Hoertnagel, Janelle Howell, Ivan Ivanovski, Angela Kaindl, Tom Kamphans, Susanne Kamphausen, Catherine Karimov, Hadil Kathom, Anna Keryan, Alexej Knaus, Sebastian Köhler, Uwe Kornak, Alexander Lavrov, Maximilian Leitheiser, Gholson J. Lyon, Elisabeth Mangold, Purificación Marín Reina, Antonio Martinez Carrascal, Diana Mitter, Laura Morlan Herrador, Guy Nadav, Markus Nöthen, Alfredo Orrico, Claus Eric Ott, Kristen Park, Borut Peterlin, Laura Pölsler, Annick Raas-Rothschild, Linda Randolph, Nicole Revencu, Christina Ringmann Fagerberg, Peter Nick Robinson, Stanislav Rosnev, Sabine Rudnik, Gorazd Rudolf, Ulrich Schatz, Anna Schossig, Max Schubach, Or Shanoon, Eamonn Sheridan, Pola Smirin-Yosef, Malte Spielmann, Eun Kyung Suk, Yves Sznajer, Christian T. Thiel, Gundula Thiel, Alain Verloes, Irena Vrecar, Dagmar Wahl, Ingrid Weber, Korina Winter, Marzena Wiśniewska, Bernd Wollnik, Ming W. Yeung, Max Zhao, Na Zhu, Johannes Zschocke, Stefan Mundlos, Denise Horn, Peter M. Krawitz

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)2807-2814
Number of pages8
JournalGenetics in Medicine
Volume21
Issue number12
DOIs
StatePublished - 1 Dec 2019
Externally publishedYes

Keywords

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

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