PhenoScore: AI-based phenomics to quantify rare disease and genetic variation

While both molecular and phenotypic data are essential when interpreting genetic variants, prediction scores (CADD, PolyPhen, and SIFT) have focused on molecular details to evaluate pathogenicity — omitting phenotypic features. To unlock the full potential of phenotypic data, we developed PhenoScore: an open source, artificial intelligence-based phenomics framework. PhenoScore combines facial recognition technology with Human Phenotype Ontology (HPO) data analysis to quantify phenotypic similarity at both the level of individual patients as well as of cohorts. We prove PhenoScore’s ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 25 out of 26 investigated genetic syndromes against clinical features observed in individuals with other neurodevelopmental disorders. Moreover, PhenoScore was able to provide objective clinical evidence for two distinct ADNP -related phenotypes, that had already been established functionally, but not yet phenotypically. Hence, PhenoScore will not only be of use to unbiasedly quantify phenotypes to assist genomic variant interpretation at the individual level, such as for reclassifying variants of unknown clinical significance, but is also of importance for detailed genotype-phenotype studies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We are grateful to the Dutch Organisation for Health Research and Development: ZONMW grants 411 912-12-109 (to B.B.A.d.V. and L.E.L.M.V.), Donders Junior researcher grant 2019 (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 SolveRD project (to L.E.L.M.V.), which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 779257. R.F.K acknowledges financial support of 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. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of the Radboud University Medical Center gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors