PEDIA: prioritization of exome data by image analysis

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.

Metadata

Item Type:	Article
Authors/Creators:	Hsieh, T-C 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, C-E 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 https://orcid.org/0000-0002-7237-6280 Smirin-Yosef, P Spielmann, M Suk, E-K Sznajer, Y Thiel, CT 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
Copyright, Publisher and Additional Information:	© The Author(s) 2019. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
Keywords:	deep learning; computer vision; dysmorphology; variant prioritization; exome diagnostics
Dates:	Accepted: 23 May 2019 Published (online): 5 June 2019 Published: December 2019
Institution:	The University of Leeds
Depositing User:	Symplectic Publications
Date Deposited:	17 Feb 2020 13:55
Last Modified:	17 Feb 2020 13:55
Status:	Published
Publisher:	BMC
Identification Number:	10.1038/s41436-019-0566-2
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:157204

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PEDIA: prioritization of exome data by image analysis

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