GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

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 We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders.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 This includes previously unpublished reports for more than 40% of the African patients.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.</p

Transcriptome sequencing identifies a noncoding, deep intronic variant in CLCN7 causing autosomal recessive osteopetrosis

Background: Over half of children with rare genetic diseases remain undiagnosed despite maximal clinical evaluation and DNA-based genetic testing. As part of an Undiagnosed Diseases Program applying transcriptome (RNA) sequencing to identify the causes of these unsolved cases, we studied a child with severe infantile osteopetrosis leading to cranial nerve palsies, bone deformities, and bone marrow failure, for whom whole-genome sequencing was nondiagnostic. Methods: We performed transcriptome (RNA) sequencing of whole blood followed by analysis of aberrant transcript isoforms and osteoclast functional studies. Results: We identified a pathogenic deep intronic variant in CLCN7 creating an unexpected, frameshifting pseudoexon causing complete loss of function. Functional studies, including osteoclastogenesis and bone resorption assays, confirmed normal osteoclast differentiation but loss of osteoclast function. Conclusion: This is the first report of a pathogenic deep intronic variant in CLCN7, and our approach provides a model for systematic identification of noncoding variants causing osteopetrosis—a disease for which molecular-genetic diagnosis can be pivotal for potentially curative hematopoietic stem cell transplantation. Our work illustrates that cryptic splice variants may elude DNA-only sequencing and supports broad first-line use of transcriptome sequencing for children with undiagnosed diseases

DataSheet1_Vici syndrome in Israel: Clinical and molecular insights.docx

Introduction: Vici Syndrome is a rare, severe, neurodevelopmental/neurodegenerative disorder with multi-systemic manifestations presenting in infancy. It is mainly characterized by global developmental delay, seizures, agenesis of the corpus callosum, hair and skin hypopigmentation, bilateral cataract, and varying degrees of immunodeficiency, among other features. Vici Syndrome is caused by biallelic pathogenic variants in EPG5, resulting in impaired autophagy. Thus far, the condition has been reported in less than a hundred individuals.Objective and Methods: We aimed to characterize the clinical and molecular findings in individuals harboring biallelic EPG5 variants, recruited from four medical centers in Israel. Furthermore, we aimed to utilize a machine learning-based tool to assess facial features of Vici syndrome.Results: Eleven cases of Vici Syndrome from five unrelated families, one of which was diagnosed prenatally with subsequent termination of pregnancy, were recruited. A total of five disease causing variants were detected in EPG5: two novel: c.2554-5A>G and c.1461delC; and 3 previously reported: c.3447G>A, c.5993C>G, and c.1007A>G, the latter previously identified in several patients of Ashkenazi-Jewish (AJ) descent. Amongst 140,491 individuals screened by the Dor Yeshorim Program, we show that the c.1007A>G variant has an overall carrier frequency of 0.45% (1 in 224) among AJ individuals. Finally, based on two-dimensional facial photographs of individuals with Vici syndrome (n = 19), a composite facial mask was created using the DeepGestalt algorithm, illustrating facial features typical of this disorder.Conclusion: We report on ten children and one fetus from five unrelated families, affected with Vici syndrome, and describe prenatal and postnatal characteristics. Our findings contribute to the current knowledge regarding the molecular basis and phenotypic features of this rare syndrome. Additionally, the deep learning-based facial gestalt adds to the clinician’s diagnostic toolbox and may aid in facilitating identification of affected individuals.</p

Table1_Vici syndrome in Israel: Clinical and molecular insights.docx

Introduction: Vici Syndrome is a rare, severe, neurodevelopmental/neurodegenerative disorder with multi-systemic manifestations presenting in infancy. It is mainly characterized by global developmental delay, seizures, agenesis of the corpus callosum, hair and skin hypopigmentation, bilateral cataract, and varying degrees of immunodeficiency, among other features. Vici Syndrome is caused by biallelic pathogenic variants in EPG5, resulting in impaired autophagy. Thus far, the condition has been reported in less than a hundred individuals.Objective and Methods: We aimed to characterize the clinical and molecular findings in individuals harboring biallelic EPG5 variants, recruited from four medical centers in Israel. Furthermore, we aimed to utilize a machine learning-based tool to assess facial features of Vici syndrome.Results: Eleven cases of Vici Syndrome from five unrelated families, one of which was diagnosed prenatally with subsequent termination of pregnancy, were recruited. A total of five disease causing variants were detected in EPG5: two novel: c.2554-5A>G and c.1461delC; and 3 previously reported: c.3447G>A, c.5993C>G, and c.1007A>G, the latter previously identified in several patients of Ashkenazi-Jewish (AJ) descent. Amongst 140,491 individuals screened by the Dor Yeshorim Program, we show that the c.1007A>G variant has an overall carrier frequency of 0.45% (1 in 224) among AJ individuals. Finally, based on two-dimensional facial photographs of individuals with Vici syndrome (n = 19), a composite facial mask was created using the DeepGestalt algorithm, illustrating facial features typical of this disorder.Conclusion: We report on ten children and one fetus from five unrelated families, affected with Vici syndrome, and describe prenatal and postnatal characteristics. Our findings contribute to the current knowledge regarding the molecular basis and phenotypic features of this rare syndrome. Additionally, the deep learning-based facial gestalt adds to the clinician’s diagnostic toolbox and may aid in facilitating identification of affected individuals.</p

Image1_Vici syndrome in Israel: Clinical and molecular insights.PNG

Introduction: Vici Syndrome is a rare, severe, neurodevelopmental/neurodegenerative disorder with multi-systemic manifestations presenting in infancy. It is mainly characterized by global developmental delay, seizures, agenesis of the corpus callosum, hair and skin hypopigmentation, bilateral cataract, and varying degrees of immunodeficiency, among other features. Vici Syndrome is caused by biallelic pathogenic variants in EPG5, resulting in impaired autophagy. Thus far, the condition has been reported in less than a hundred individuals.Objective and Methods: We aimed to characterize the clinical and molecular findings in individuals harboring biallelic EPG5 variants, recruited from four medical centers in Israel. Furthermore, we aimed to utilize a machine learning-based tool to assess facial features of Vici syndrome.Results: Eleven cases of Vici Syndrome from five unrelated families, one of which was diagnosed prenatally with subsequent termination of pregnancy, were recruited. A total of five disease causing variants were detected in EPG5: two novel: c.2554-5A>G and c.1461delC; and 3 previously reported: c.3447G>A, c.5993C>G, and c.1007A>G, the latter previously identified in several patients of Ashkenazi-Jewish (AJ) descent. Amongst 140,491 individuals screened by the Dor Yeshorim Program, we show that the c.1007A>G variant has an overall carrier frequency of 0.45% (1 in 224) among AJ individuals. Finally, based on two-dimensional facial photographs of individuals with Vici syndrome (n = 19), a composite facial mask was created using the DeepGestalt algorithm, illustrating facial features typical of this disorder.Conclusion: We report on ten children and one fetus from five unrelated families, affected with Vici syndrome, and describe prenatal and postnatal characteristics. Our findings contribute to the current knowledge regarding the molecular basis and phenotypic features of this rare syndrome. Additionally, the deep learning-based facial gestalt adds to the clinician’s diagnostic toolbox and may aid in facilitating identification of affected individuals.</p

An HNRNPK-specific DNA methylation signature makes sense of missense variants and expands the phenotypic spectrum of Au-Kline syndrome.

Au-Kline syndrome (AKS) is a neurodevelopmental disorder associated with multiple malformations and a characteristic facial gestalt. The first individuals ascertained carried de novo loss-of-function (LoF) variants in HNRNPK. Here, we report 32 individuals with AKS (26 previously unpublished), including 13 with de novo missense variants. We propose new clinical diagnostic criteria for AKS that differentiate it from the clinically overlapping Kabuki syndrome and describe a significant phenotypic expansion to include individuals with missense variants who present with subtle facial features and few or no malformations. Many gene-specific DNA methylation (DNAm) signatures have been identified for neurodevelopmental syndromes. Because HNRNPK has roles in chromatin and epigenetic regulation, we hypothesized that pathogenic variants in HNRNPK may be associated with a specific DNAm signature. Here, we report a unique DNAm signature for AKS due to LoF HNRNPK variants, distinct from controls and Kabuki syndrome. This DNAm signature is also identified in some individuals with de novo HNRNPK missense variants, confirming their pathogenicity and the phenotypic expansion of AKS to include more subtle phenotypes. Furthermore, we report that some individuals with missense variants have an "intermediate" DNAm signature that parallels their milder clinical presentation, suggesting the presence of an epi-genotype phenotype correlation. In summary, the AKS DNAm signature may help elucidate the underlying pathophysiology of AKS. This DNAm signature also effectively supported clinical syndrome delineation and is a valuable aid for variant interpretation in individuals where a clinical diagnosis of AKS is unclear, particularly for mild presentations

An HNRNPK-specific DNA methylation signature makes sense of missense variants and expands the phenotypic spectrum of Au-Kline syndrome

Abstract Au-Kline syndrome (AKS) is a neurodevelopmental disorder associated with multiple malformations and a characteristic facial gestalt. The first individuals ascertained carried de novo loss-of-function (LoF) variants in HNRNPK. Here, we report 32 individuals with AKS (26 previously unpublished), including 13 with de novo missense variants. We propose new clinical diagnostic criteria for AKS that differentiate it from the clinically overlapping Kabuki syndrome and describe a significant phenotypic expansion to include individuals with missense variants who present with subtle facial features and few or no malformations. Many gene-specific DNA methylation (DNAm) signatures have been identified for neurodevelopmental syndromes. Because HNRNPK has roles in chromatin and epigenetic regulation, we hypothesized that pathogenic variants in HNRNPK may be associated with a specific DNAm signature. Here, we report a unique DNAm signature for AKS due to LoF HNRNPK variants, distinct from controls and Kabuki syndrome. This DNAm signature is also identified in some individuals with de novo HNRNPK missense variants, confirming their pathogenicity and the phenotypic expansion of AKS to include more subtle phenotypes. Furthermore, we report that some individuals with missense variants have an “intermediate” DNAm signature that parallels their milder clinical presentation, suggesting the presence of an epi-genotype phenotype correlation. In summary, the AKS DNAm signature may help elucidate the underlying pathophysiology of AKS. This DNAm signature also effectively supported clinical syndrome delineation and is a valuable aid for variant interpretation in individuals where a clinical diagnosis of AKS is unclear, particularly for mild presentations