The future role of facial image analysis in ACMG classification guidelines

Epigenomic and phenotypic characterization of DEGCAGS syndrome

Developmental Delay with Gastrointestinal, Cardiovascular, Genitourinary, and Skeletal Abnormalities syndrome (DEGCAGS, MIM #619488) is caused by biallelic, loss-of-function (LoF) ZNF699 variants, and is characterized by variable neurodevelopmental disability, discordant organ anomalies among full siblings and infant mortality. ZNF699 encodes a KRAB zinc finger protein of unknown function. We aimed to investigate the genotype-phenotype spectrum of DEGCAGS and the possibility of a diagnostic DNA methylation episignature, to facilitate the diagnosis of a highly variable condition lacking pathognomonic clinical findings. We collected data on 30 affected individuals (12 new). GestaltMatcher analyzed fifty-three facial photographs from five individuals. In nine individuals, methylation profiling of blood-DNA was performed, and a classification model was constructed to differentiate DEGCAGS from controls. We expand the ZNF699-related molecular spectrum and show that biallelic, LoF, ZNF699 variants cause unique clinical findings with age-related presentation and a similar facial gestalt. We also identified a robust episignature for DEGCAGS syndrome. DEGCAGS syndrome is a clinically variable recessive syndrome even among siblings with a distinct methylation episignature which can be used as a screening, diagnostic and classification tool for ZNF699 variants. Analysis of differentially methylated regions suggested an effect on genes potentially implicated in the syndrome's pathogenesis.

Next-generation phenotyping in Nigerian children with Cornelia de Lange syndrome

Next-generation phenotyping (NGP) can be used to compute the similarity of dysmorphic patients to known syndromic diseases. So far, the technology has been evaluated in variant prioritization and classification, providing evidence for pathogenicity if the phenotype matched with other patients with a confirmed molecular diagnosis. In a Nigerian cohort of individuals with facial dysmorphism, we used the NGP tool GestaltMatcher to screen portraits prior to genetic testing and subjected individuals with high similarity scores to exome sequencing (ES). Here, we report on two individuals with global developmental delay, pulmonary artery stenosis, and genital and limb malformations for whom GestaltMatcher yielded Cornelia de Lange syndrome (CdLS) as the top hit. ES revealed a known pathogenic nonsense variant, NM_133433.4: c.598C>T; p.(Gln200*), as well as a novel frameshift variant c.7948dup; p.(Ile2650Asnfs*11) in NIPBL. Our results suggest that NGP can be used as a screening tool and thresholds could be defined for achieving high diagnostic yields in ES. Training the artificial intelligence (AI) with additional cases of the same ethnicity might further increase the positive predictive value of GestaltMatcher.

RNA analysis and computer-aided facial phenotyping help to classify a novel TRIO splice site variant

Pathogenic variants in TRIO, encoding the guanine nucleotide exchange factor, are associated with two distinct neurodevelopmental delay phenotypes: gain-of-function missense mutations within the spectrin repeats are causative for a severe developmental delay with macrocephaly (MIM: 618825), whereas loss-of-function missense variants in the GEF1 domain and truncating variants throughout the gene lead to a milder developmental delay and microcephaly (MIM: 617061). In three affected family members with mild intellectual disability/NDD and microcephaly, we detected a novel heterozygous TRIO variant at the last coding base of exon 31 (NM_007118.4:c.4716G>A). RNA analysis from patient-derived lymphoblastoid cells confirmed aberrant splicing resulting in the skipping of exon 31 (r.4615_4716del), leading to an in-frame deletion in the first Pleckstrin homology subdomain of the GEF1 domain: p.(Thr1539_Lys1572del). To test for a distinct gestalt, facial characteristics of the family members and 41 previously published TRIO cases were systematically evaluated via GestaltMatcher. Computational analysis of the facial gestalt suggests a distinguishable facial TRIO-phenotype not outlined in the existing literature.

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.

Current genetic diagnostics in inborn errors of immunity

New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.

Computational facial analysis for rare Mendelian disorders

With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next-generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state-of-the-art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra-rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.