PhenoScore quantifies phenotypic variation for rare genetic diseases by 
combining facial analysis with other clinical features using a machine-learning 
framework

Dingemans, Alexander J. M.; Hinne, Max; Truijen, Kim M. G.; Goltstein, Lia; Van Reeuwijk, Jeroen; De Leeuw, Nicole; Schuurs-Hoeijmakers, Janneke; Pfundt, Rolph; Diets, Illja J.; Den Hoed, Joery; De Boer, Elke; Coenen-Van der Spek, Jet; Jansen, Sandra; Van Bon, Bregje W.; Jonis, Noraly; Ockeloen, Charlotte W.; Vulto-van Silfhout, Anneke T.; Kleefstra, Tjitske; Koolen, David A.; Campeau, Philippe M.; Palmer, Elizabeth E.; Van Esch, Hilde; Lyon, Gholson J.; Alkuraya, Fowzan S.; Rauch, Anita; Marom, Ronit; Baralle, Diana; Van der Sluijs, Pleuntje J.; Santen, Gijs W. E.; Kooy, R. Frank; Van Gerven, Marcel A. J.; Vissers, Lisenka E. L. M.; De Vries, Bert B. A.

doi:10.1038/s41588-023-01469-w

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PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

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Den Hoed, Joery
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

Vissers, Lisenka E. L. M.
Radboud University Medical Center;
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

De Vries, Bert B. A.
Radboud University Medical Center;
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

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Citation

Dingemans, A. J. M., Hinne, M., Truijen, K. M. G., Goltstein, L., Van Reeuwijk, J., De Leeuw, N., et al. (2023). PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework. Nature Genetics, 55, 1598-1607. doi:10.1038/s41588-023-01469-w.

Cite as: https://hdl.handle.net/21.11116/0000-000D-91D4-F

Abstract

Several molecular and phenotypic algorithms exist that establish genotype–phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore’s ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype–phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.