Filamin-A-interacting protein 1 (FILIP1) is a structural protein that is involved in neuronal and muscle function and integrity and interacts with FLNa and FLNc. Pathogenic variants in filamin-encoding genes have been linked to neurological disorders (FLNA) and muscle diseases characterized by myofibrillar perturbations (FLNC), but human diseases associated with FILIP1 variants have not yet been described. Here, we report on five patients from four unrelated consanguineous families with homozygous FILIP1 variants (two nonsense and two missense). Functional studies indicated altered stability of the FILIP1 protein carrying the p.[Pro1133Leu] variant. Patients exhibit a broad spectrum of neurological symptoms including brain malformations, neurodevelopmental delay, muscle weakness and pathology and dysmorphic features. Electron and immunofluorescence microscopy on the muscle biopsy derived from the patient harbouring the homozygous p.[Pro1133Leu] missense variant revealed core-like zones of myofibrillar disintegration, autophagic vacuoles and accumulation of FLNc. Proteomic studies on the fibroblasts derived from the same patient showed dysregulation of a variety of proteins including FLNc and alpha-B-crystallin, a finding (confirmed by immunofluorescence) which is in line with the manifestation of symptoms associated with the syndromic phenotype of FILIP1opathy. The combined findings of this study show that the loss of functional FILIP1 leads to a recessive disorder characterized by neurological and muscular manifestations as well as dysmorphic features accompanied by perturbed proteostasis and myopathology.
The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.
The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.