Document Type
Article
Publication Date
12-2019
Keywords
JGM
JAX Source
Genet Med 2019 Dec; 21(12):2807-2814
Volume
21
Issue
12
First Page
2807
Last Page
2814
ISSN
1530-0366
PMID
31164752
DOI
https://doi.org/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.
Recommended Citation
Hsieh T,
Mensah M,
Pantel J,
Aguilar D,
Bar O,
Bayat A,
Becerra-Solano L,
Bentzen H,
Biskup S,
Borisov O,
Braaten O,
Ciaccio C,
Coutelier M,
Cremer K,
Danyel M,
Daschkey S,
Eden H,
Devriendt K,
Wilson S,
Douzgou S,
Đukić D,
Ehmke N,
Fauth C,
Fischer-Zirnsak B,
Fleischer N,
Gabriel H,
Graul-Neumann L,
Gripp K,
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 G,
Mangold E,
Reina P,
Carrascal A,
Mitter D,
Herrador L,
Nadav G,
Nöthen M,
Orrico A,
Ott C,
Park K,
Peterlin B,
Pölsler L,
Raas-Rothschild A,
Randolph L,
Revencu N,
Fagerberg C,
Robinson P,
Rosnev S,
Rudnik S,
Rudolf G,
Schatz U,
Schossig A,
Schubach M,
Shanoon O,
Sheridan E,
Smirin-Yosef P,
Spielmann M,
Suk E,
Sznajer Y,
Thiel C,
Thiel G,
Verloes A,
Vrecar I,
Wahl D,
Weber I,
Winter K,
Wiśniewska M,
Wollnik B,
Yeung M,
Zhao M,
Zhu N,
Zschocke J,
Mundlos S,
Horn D,
Krawitz P.
PEDIA: prioritization of exome data by image analysis. Genet Med 2019 Dec; 21(12):2807-2814
Comments
This open access article is licensed under a Creative Commons Attribution 4.0 International License.