Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Abstract The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729–0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians

Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt

International audienceKabuki syndrome (KS) is a rare genetic disorder caused by mutations in two major genes, KMT2D and KDM6A, that are responsible for Kabuki syndrome 1 (KS1, OMIM147920) and Kabuki syndrome 2 (KS2, OMIM300867), respectively. We lack a description of clinical signs to distinguish KS1 and KS2. We used facial morphology analysis to detect any facial morphological differences between the two KS types. We used a facial-recognition algorithm to explore any facial morphologic differences between the two types of KS. We compared several image series of KS1 and KS2 individuals, then compared images of those of Caucasian origin only (12 individuals for each gene) because this was the main ethnicity in this series. We also collected 32 images from the literature to amass a large series. We externally validated results obtained by the algorithm with evaluations by trained clinical geneticists using the same set of pictures. Use of the algorithm revealed a statistically significant difference between each group for our series of images, demonstrating a different facial morphotype between KS1 and KS2 individuals (mean area under the receiver operating characteristic curve = 0.85 [p = 0.027] between KS1 and KS2). The algorithm was better at discriminating between the two types of KS with images from our series than those from the literature (p = 0.0007). Clinical geneticists trained to distinguished KS1 and KS2 significantly recognised a unique facial morphotype, which validated algorithm findings (p = 1.6e-11). Our deep-neural-network-driven facial-recognition algorithm can reveal specific composite gestalt images for KS1 and KS2 individuals

DNA methylation episignature in Gabriele-de Vries syndrome

International audiencePURPOSE: Gabriele-de Vries syndrome (GADEVS) is a rare genetic disorder characterized by developmental delay and/or intellectual disability, hypotonia, feeding difficulties, and distinct facial features. To refine the phenotype and to better understand the molecular basis of the syndrome, we analyzed clinical data and performed genome-wide DNA methylation analysis of a series of individuals carrying a YY1 variant. METHODS: Clinical data were collected for 13 individuals not yet reported through an international call for collaboration. DNA was collected for 11 of these individuals and 2 previously reported individuals in an attempt to delineate a specific DNA methylation signature in GADEVS. RESULTS: Phenotype in most individuals overlapped with the previously described features. We described 1 individual with atypical phenotype, heterozygous for a missense variant in a domain usually not involved in individuals with YY1 pathogenic missense variations. We also described a specific peripheral blood DNA methylation profile associated with YY1 variants. CONCLUSION: We reported a distinct DNA methylation episignature in GADEVS. We expanded the clinical profile of GADEVS to include thin/sparse hair and cryptorchidism. We also highlighted the utility of DNA methylation episignature analysis for classification of variants of unknown clinical significance

CDK13-related disorder: Report of a series of 18 previously unpublished individuals and description of an epigenetic signature

Purpose: Rare genetic variants in CDK13 are responsible for CDK13-related disorder (CDK13-RD), with main clinical features being developmental delay or intellectual disability, facial features, behavioral problems, congenital heart defect, and seizures. In this paper, we report 18 novel individuals with CDK13-RD and provide characterization of genome-wide DNA methylation. Methods: We obtained clinical phenotype and neuropsychological data for 18 and 10 individuals, respectively, and compared this series with the literature. We also compared peripheral blood DNA methylation profiles in individuals with CDK13-RD, controls, and other neurodevelopmental disorders episignatures. Finally, we developed a support vector machine–based classifier distinguishing CDK13-RD and non–CDK13-RD samples. Results: We reported health and developmental parameters, clinical data, and neuropsychological profile of individuals with CDK13-RD. Genome-wide differential methylation analysis revealed a global hypomethylated profile in individuals with CDK13-RD in a highly sensitive and specific model that could aid in reclassifying variants of uncertain significance. Conclusion: We describe the novel features such as anxiety disorder, cryptorchidism, and disrupted sleep in CDK13-RD. We define a CDK13-RD DNA methylation episignature as a diagnostic tool and a defining functional feature of the evolving clinical presentation of this disorder. We also show overlap of the CDK13 DNA methylation profile in an individual with a functionally and clinically related CCNK-related disorder

Further delineation of the rare GDACCF (global developmental delay, absent or hypoplastic corpus callosum, dysmorphic facies syndrome): genotype and phenotype of 22 patients with ZNF148 mutations.

BACKGROUND Pathogenic variants in the zinc finger protein coding genes are rare causes of intellectual disability and congenital malformations. Mutations in the ZNF148 gene causing GDACCF syndrome (global developmental delay, absent or hypoplastic corpus callosum, dysmorphic facies; MIM #617260) have been reported in five individuals so far. METHODS As a result of an international collaboration using GeneMatcher Phenome Central Repository and personal communications, here we describe the clinical and molecular genetic characteristics of 22 previously unreported individuals. RESULTS The core clinical phenotype is characterised by developmental delay particularly in the domain of speech development, postnatal growth retardation, microcephaly and facial dysmorphism. Corpus callosum abnormalities appear less frequently than suggested by previous observations. The identified mutations concerned nonsense or frameshift variants that were mainly located in the last exon of the ZNF148 gene. Heterozygous deletion including the entire ZNF148 gene was found in only one case. Most mutations occurred de novo, but were inherited from an affected parent in two families. CONCLUSION The GDACCF syndrome is clinically diverse, and a genotype-first approach, that is, exome sequencing is recommended for establishing a genetic diagnosis rather than a phenotype-first approach. However, the syndrome may be suspected based on some recurrent, recognisable features. Corpus callosum anomalies were not as constant as previously suggested, we therefore recommend to replace the term 'GDACCF syndrome' with 'ZNF148-related neurodevelopmental disorder'