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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


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.

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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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Medical Genetics
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Uncontrolled Keywords:Genetics, Genetics (clinical), Clinical genetics, Genetics research, Neurodevelopmental disorders, Software
Language:English
Date:1 September 2023
Deposited On:11 Aug 2023 07:09
Last Modified:29 Apr 2024 01:39
Publisher:Nature Publishing Group
ISSN:1061-4036
Additional Information: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).
OA Status:Closed
Publisher DOI:https://doi.org/10.1038/s41588-023-01469-w
PubMed ID:37550531