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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

Nature Genetics volume 55, pages 1598–1607 (2023)Cite this article

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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.

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Fig. 1: Overview of PhenoScore.
Fig. 2: PhenoScore for KdVS.
Fig. 3: Generalization of PhenoScore to 40 syndromes.
Fig. 4: Number of individuals needed for training.
Fig. 5: Genotype–phenotype correlations and subgroup detection.

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Data availability

The used dataset in this study is not publicly available due to both IRB and General Data Protection Regulation (EU GDPR) restrictions because the data might be (partially) traceable. However, access to the data may be requested from the data availability committee by contacting the corresponding authors via e-mail with a research proposal, who will respond within 14 d.

Code availability

The code of PhenoScore version 1.0.0 created during this study is freely available at https://github.com/ldingemans/PhenoScore ref. 83, to enable anyone to apply PhenoScore to their own dataset. Included in PhenoScore are the following two examples: the data for the SATB1 subgroups (positive example) and random data (negative example).

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Acknowledgements

We are grateful to all families and clinicians who agreed to participate and provide clinical and genotypic information. R.F.K. acknowledges financial support from the Research Fund of the University of Antwerp (Methusalem-OEC grant GENOMED). The work of G.J.L. is supported by New York State Office for People with Developmental Disabilities (OPWDD) and NIH NIGMS R35-GM-133408. E.E.P. is supported by a National Health and Medical Research Council Investigator Grant (award 2021/GNT2008166). Furthermore, we are grateful to the Dutch Organization for Health Research and Development—ZON-MW grants 912-12-109 (to B.B.A.d.V. and L.E.L.M.V.), Donders Junior researcher grant 2019 (to B.B.A.d.V. and L.E.L.M.V.) and Aspasia grant 015.014.066 (to L.E.L.M.V.). The aims of this study contribute to the Solve-RD project (to L.E.L.M.V.), which has received funding from the European Unions Horizon 2020 research and innovation program under grant agreement 779257. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Lisenka E. L. M. Vissers, Bert B. A. de Vries.

Authors and Affiliations

  1. Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands

    Alexander J. M. Dingemans, Kim M. G. Truijen, Lia Goltstein, Jeroen van Reeuwijk, Nicole de Leeuw, Janneke Schuurs-Hoeijmakers, Rolph Pfundt, Illja J. Diets, Elke de Boer, Jet Coenen-van der Spek, Bregje W. van Bon, Noraly Jonis, Charlotte W. Ockeloen, Anneke T. Vulto-van Silfhout, Tjitske Kleefstra, David A. Koolen, Lisenka E. L. M. Vissers & Bert B. A. de Vries

  2. Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands

    Alexander J. M. Dingemans, Max Hinne & Marcel A. J. van Gerven

  3. Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands

    Joery den Hoed

  4. Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

    Sandra Jansen

  5. Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada

    Philippe M. Campeau

  6. Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia

    Elizabeth E. Palmer

  7. Sydney Children’s Hospitals Network, Sydney, New South Wales, Australia

    Elizabeth E. Palmer

  8. Center for Human Genetics, University Hospitals Leuven, University of Leuven, Leuven, Belgium

    Hilde Van Esch

  9. Department of Human Genetics and George A. Jervis Clinic, Institute for Basic Research in Developmental Disabilities (IBR), Staten Island, NY, USA

    Gholson J. Lyon

  10. Biology PhD Program, The Graduate Center, The City University of New York, New York City, NY, USA

    Gholson J. Lyon

  11. Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia

    Fowzan S. Alkuraya

  12. Institute of Medical Genetics, University of Zürich, Zürich, Switzerland

    Anita Rauch

  13. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Ronit Marom

  14. Faculty of Medicine, University of Southampton, Southampton, UK

    Diana Baralle

  15. Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands

    Pleuntje J. van der Sluijs & Gijs W. E. Santen

  16. Department of Medical Genetics, University of Antwerp, Antwerp, Belgium

    R. Frank Kooy

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  1. Alexander J. M. Dingemans
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  28. Pleuntje J. van der Sluijs
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  31. Marcel A. J. van Gerven
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  32. Lisenka E. L. M. Vissers
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  33. Bert B. A. de Vries
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Contributions

A.J.M.D., M.H., L.E.L.M.V., B.B.A.d.V. and M.A.J.v.G. designed the study. A.J.M.D., K.M.G.T., L.G., J.v.R., N.d.L., J.S.H., R.P., I.J.D., E.d.B., J.d.H., J.v.d.S., S.J., B.W.v.B., N.J., E.E.P., P.M.C., A.T.V.v.S., T.K., D.A.K., F.K., H.V.E., G.J.L., F.S.A., A.R., R.M., D.B., P.J.v.d.S., G.S., L.E.L.M.V. and B.B.A.d.V. collected and curated the data. A.J.M.D. and M.H. performed the formal analyses. L.E.L.M.V. and B.B.A.d.V. acquired the funding. A.J.M.D. and M.H. completed the modeling and investigations. A.J.M.D. developed the software. A.J.M.D., M.H., L.E.L.M.V., B.B.A.d.V. and M.A.J.v.G. wrote the original draft. All authors reviewed and edited the final manuscript.

Corresponding authors

Correspondence to Lisenka E. L. M. Vissers or Bert B. A. de Vries.

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Extended data

Extended Data Fig. 1 Benchmarking PhenoScore.

The predictive accuracies of LIRICAL, Phenomizer and PhenoScore [118-120] for every included genetic syndrome are displayed here, except for ACTL6A, since the associated phenotype has no OMIM number and therefore Phenomizer and LIRICAL do not include it in its predictions. For PhenoScore and LIRICAL, to calculate the accuracy, a cut-off value of 0.5 for the predictions was used, while for Phenomizer in this case, 0.05 was chosen. For almost every investigated syndrome, PhenoScore outperforms Phenomizer and LIRICAL.

Extended Data Fig. 2 AUC curves of PhenoScore per genetic syndrome.

The receiver operating characteristic curve of all 40 genetic syndromes included in this study.

Extended Data Fig. 3 UMAP plots of facial feature vectors.

The Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP3) plot for the VGGFace2 vectors of all included genetic syndromes, and for the extra systematic confounder analysis for which the individuals with Koolen-de Vries syndrome seen at other centers were compared to individuals seen at our outpatient clinic. For all plots (except the KANSL1 internal/external plot), the feature vectors of all sampled controls during five iterations and the feature vectors of the included patients were provided as input to UMAP. The classes are not separable in this projected space, which provides evidence that the classification is not based on a systematic confounder.

Extended Data Table 1 List of publications for data collection
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Extended Data Table 2 Performance of PhenoScore with other syndromes as control
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Extended Data Table 3 Benchmarking PhenoScore
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Extended Data Table 4 Subgroup analyses
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Extended Data Table 5 Classifying variants of uncertain significance
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Extended Data Table 6 PhenoScore with phenotypically similar individuals
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Extended Data Table 7 Systematic confounder analysis using Koolen-de Vries syndrome
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Dingemans, A.J.M., Hinne, M., Truijen, K.M.G. et al. PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework. Nat Genet 55, 1598–1607 (2023). https://doi.org/10.1038/s41588-023-01469-w

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