REEV: review, evaluate and explain variants.

In the era of high throughput sequencing, special software is required for the clinical evaluation of genetic variants. We developed REEV (Review, Evaluate and Explain Variants), a user-friendly platform for clinicians and researchers in the field of rare disease genetics. Supporting data was aggregated from public data sources. We compared REEV with seven other tools for clinical variant evaluation. REEV (semi-)automatically fills individual ACMG criteria facilitating variant interpretation. REEV can store disease and phenotype data related to a case to use these for phenotype similarity measures. Users can create public permanent links for individual variants that can be saved as browser bookmarks and shared. REEV may help in the fast diagnostic assessment of genetic variants in a clinical as well as in a research context. REEV (https://reev.bihealth.org/) is free and open to all users and there is no login requirement.

Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study.

BACKGROUND: While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. OBJECTIVE: We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. METHODS: Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. RESULTS: DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score's levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. CONCLUSIONS: If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this 'supervised' approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes.

GLI3 variants causing isolated polysyndactyly are not restricted to the protein's C-terminal third.

Loss of function variants of GLI3 are associated with a variety of forms of polysyndactyly: Pallister-Hall syndrome (PHS), Greig-Cephalopolysyndactyly syndrome (GCPS), and isolated polysyndactyly (IPD). Variants affecting the N-terminal and C-terminal thirds of the GLI3 protein have been associated with GCPS, those within the central third with PHS. Cases of IPD have been attributed to variants affecting the C-terminal third of the GLI3 protein. In this study, we further investigate these genotype-phenotype correlations. Sequencing of GLI3 was performed in patients with clinical findings suggestive of a GLI3-associated syndrome. Additionally, we searched the literature for reported cases of either manifestation with mutations in the GLI3 gene. Here, we report 48 novel cases from 16 families with polysyndactyly in whom we found causative variants in GLI3 and a review on 314 previously reported GLI3 variants. No differences in location of variants causing either GCPS or IPD were found. Review of published data confirmed the association of PHS and variants affecting the GLI3 protein's central third. We conclude that the observed manifestations of GLI3 variants as GCPS or IPD display different phenotypic severities of the same disorder and propose a binary division of GLI3-associated disorders in either PHS or GCPS/polysyndactyly.

Complete lung agenesis caused by complex genomic rearrangements with neo-TAD formation at the SHH locus.

During human organogenesis, lung development is a timely and tightly regulated developmental process under the control of a large number of signaling molecules. Understanding how genetic variants can disturb normal lung development causing different lung malformations is a major goal for dissecting molecular mechanisms during embryogenesis. Here, through exome sequencing (ES), array CGH, genome sequencing (GS) and Hi-C, we aimed at elucidating the molecular basis of bilateral isolated lung agenesis in three fetuses born to a non-consanguineous family. We detected a complex genomic rearrangement containing duplicated, triplicated and deleted fragments involving the SHH locus in fetuses presenting complete agenesis of both lungs and near-complete agenesis of the trachea, diagnosed by ultrasound screening and confirmed at autopsy following termination. The rearrangement did not include SHH itself, but several regulatory elements for lung development, such as MACS1, a major SHH lung enhancer, and the neighboring genes MNX1 and NOM1. The rearrangement incorporated parts of two topologically associating domains (TADs) including their boundaries. Hi-C of cells from one of the affected fetuses showed the formation of two novel TADs each containing SHH enhancers and the MNX1 and NOM1 genes. Hi-C together with GS indicate that the new 3D conformation is likely causative for this condition by an inappropriate activation of MNX1 included in the neo-TADs by MACS1 enhancer, further highlighting the importance of the 3D chromatin conformation in human disease.

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.

BACKGROUND: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt's quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. OBJECTIVE: The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning-based framework for the automated differentiation of DeepGestalt's output on such images. METHODS: Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. RESULTS: We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt's high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt's syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt's top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt's result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). CONCLUSIONS: DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt's results and may help enhance it and similar computer-aided facial phenotyping tools.

Variable pulmonary manifestations in Chitayat syndrome: Six additional affected individuals.

Hand hyperphalangism leading to shortened index fingers with ulnar deviation, hallux valgus, mild facial dysmorphism and respiratory compromise requiring assisted ventilation are the key features of Chitayat syndrome. This condition results from the recurrent heterozygous missense variant NM_006494.2:c.266A>G; p.(Tyr89Cys) in ERF on chromosome 19q13.2, encoding the ETS2 repressor factor (ERF) protein. The pathomechanism of Chitayat syndrome is unknown. To date, seven individuals with Chitayat syndrome and the recurrent pathogenic ERF variant have been reported in the literature. Here, we describe six additional individuals, among them only one presenting with a history of assisted ventilation, and the remaining presenting with variable pulmonary phenotypes, including one individual without any obvious pulmonary manifestations. Our findings widen the phenotype spectrum caused by the recurrent pathogenic variant in ERF, underline Chitayat syndrome as a cause of isolated skeletal malformations and therefore contribute to the improvement of diagnostic strategies in individuals with hand hyperphalangism.

Hi-C Identifies Complex Genomic Rearrangements and TAD-Shuffling in Developmental Diseases.

Genome-wide analysis methods, such as array comparative genomic hybridization (CGH) and whole-genome sequencing (WGS), have greatly advanced the identification of structural variants (SVs) in the human genome. However, even with standard high-throughput sequencing techniques, complex rearrangements with multiple breakpoints are often difficult to resolve, and predicting their effects on gene expression and phenotype remains a challenge. Here, we address these problems by using high-throughput chromosome conformation capture (Hi-C) generated from cultured cells of nine individuals with developmental disorders (DDs). Three individuals had previously been identified as harboring duplications at the SOX9 locus and six had been identified with translocations. Hi-C resolved the positions of the duplications and was instructive in interpreting their distinct pathogenic effects, including the formation of new topologically associating domains (neo-TADs). Hi-C was very sensitive in detecting translocations, and it revealed previously unrecognized complex rearrangements at the breakpoints. In several cases, we observed the formation of fused-TADs promoting ectopic enhancer-promoter interactions that were likely to be involved in the disease pathology. In summary, we show that Hi-C is a sensible method for the detection of complex SVs in a clinical setting. The results help interpret the possible pathogenic effects of the SVs in individuals with DDs.