PEDIA: prioritization of exome data by image analysis

Hsieh, T. C., Mensah, M. A., Pantel, J. T., Aguilar, D., Bar, O., Bayat, A., Becerra-Solano, L., Bentzen, H. B., Biskup, S., Borisov, O., Braaten, O., Ciaccio, C., Coutelier, M., Cremer, K., Danyel, M., Daschkey, S., Eden, H. D., Devriendt, K., Wilson, S., Douzgou, S., Dukic, D., Ehmke, N., Fauth, C., Fischer-Zirnsak, B., Fleischer, N., Gabriel, H., Graul-Neumann, L., Gripp, K. W., 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., Kohler, S., Kornak, U., Lavrov, A., Leitheiser, M., Lyon, G. J., Mangold, E., Reina, P. M., Carrascal, A. M., Mitter, D., Herrador, L. M., Nadav, G., Nothen, M., Orrico, A., Ott, C. E., Park, K., Peterlin, B., Polsler, L., Raas-Rothschild, A., Randolph, L., Revencu, N., Fagerberg, C. R., Robinson, P. N., Rosnev, S., Rudnik, S., Rudolf, G., Schatz, U., Schossig, A., Schubach, M., Shanoon, O., Sheridan, E., Smirin-Yosef, P., Spielmann, M., Suk, E. K., Sznajer, Y., Thiel, C. T., Thiel, G., Verloes, A., Vrecar, I., Wahl, D., Weber, I., Winter, K., Wisniewska, M., Wollnik, B., Yeung, M. W., Zhao, M., Zhu, N., Zschocke, J., Mundlos, S., Horn, D., Krawitz, P. M. (June 2019) PEDIA: prioritization of exome data by image analysis. Genet Med. ISSN 1098-3600

URL: https://www.ncbi.nlm.nih.gov/pubmed/31164752
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.

Item Type: Paper
Additional Information: Genetics in medicine : official journal of the American College of Medical Genetics
Uncontrolled Keywords: computer vision deep learning dysmorphology exome diagnostics variant prioritization
Subjects: Investigative techniques and equipment > optical devices
Investigative techniques and equipment > imaging
Investigative techniques and equipment > whole exome sequencing
Investigative techniques and equipment > assays > whole exome sequencing
CSHL Authors:
Communities: CSHL labs > Lyon lab
Depositing User: Matthew Dunn
Date: 5 June 2019
Date Deposited: 19 Jul 2019 16:16
Last Modified: 19 Jul 2019 16:16
Related URLs:
URI: http://repository.cshl.edu/id/eprint/38064

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