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.

Clinical characterization and mutation spectrum of patients with hypertriglyceridemia in a German outpatient clinic.

BACKGROUND: Severe hypertriglyceridemia (HTG) has predominantly multifactorial causes (MCS). Yet a small subset of patients have the monogenetic form (FCS). It remains a challenge to distinguish patients clinically, since decompensated MCS might mimic FCS´s severity. Aim of the current study was to determine clinical criteria that could sufficiently distinguish both forms as well as to apply the FCS score proposed by Moulin and colleagues. METHODS: We retrospectively studied 72 patients who presented with severe HTG in our clinic during a time span of seven years and received genetic testing. We classified genetic variants (ACMG-criteria), followed by genetic categorization into MCS or FCS. Clinical data were gathered from the medical records and the FCS score was calculated for each patient. RESULTS: Molecular genetic screening revealed eight FCS patients and 64 MCS patients. Altogether, we found 13 pathogenic variants of which four have not been described before. The FCS patients showed a significantly higher median triglyceride level compared to the MCS. The FCS score yielded a sensitivity of 75% and a specificity of 93.7% in our cohort, and significantly differentiated between the FCS and MCS group (p<0.001). CONCLUSIONS: In our cohort we identified several variables that significantly differentiated FCS from MCS. The FCS score performed similar to the original study by Moulin, thereby further validating the discriminatory power of the FCS score in an independent cohort.

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.

Biallelic known and novel DCDC2 variants in cholestatic liver disease: Phenotype-genotype observations in four children.

Neonatal sclerosing cholangitis (NSC) is associated with progressing biliary fibrosis that often requires liver transplantation in childhood. Several recent studies have identified variants in DCDC2, encoding doublecortin domain-containing protein 2 (DCDC2), expressed in primary cilia, that accompany syndromic disease and NSC. We report four patients with hepatobiliary disease associated with two novel homozygous or compound heterozygous variants in DCDC2. Three patients with protein-truncating variants in DCDC2, expressing no DCDC2, presented with the originally described severe hepatic phenotype in infancy. One patient with a novel homozygous DCDC2 missense variant shows a markedly milder phenotype only manifest in childhood and with retained DCDC2 expression. Concomitant nephronophthisis is present in three patients and learning disability in two. This report widens the phenotypic spectrum of DCDC2-associated hepatobiliary disease. Testing for DCDC2 expression and DCDC2 variants should be included in the evaluation of cholangiopathy of unknown aetiology in childhood as well as in infancy.

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.

PEDIA: prioritization of exome data by image analysis.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.

Autosomal dominant Robinow syndrome associated with a novel DVL3 splice mutation.

Robinow syndrome is a clinically and genetically heterogeneous disorder characterized by mesomelic limb shortening, distinctive facial features, and variable oral, cardiac, vertebral, and urogenital malformations. We identified the novel de novo splice acceptor mutation c.1715-2A > C in DVL3 in a 15-year-old female patient with typical features of Robinow syndrome. By studying DVL3 transcripts in this patient, we confirmed expression of both wild-type and mutant alleles. Mutant DVL3 mRNAs were found to harbor a deletion of four nucleotides at the beginning of exon 15 and encode a protein product with a distinct -1 reading-frame C-terminus. The data demonstrate that mutant DVL3 proteins associated with Robinow syndrome show truncation of the C-terminus and share 83 novel amino acid residues before the stop codon confirming highly specific DVL3 alterations to be associated with this syndrome. The phenotype of the Robinow syndrome-affected female reported here is typical as she shows mesomelia and mild hand anomalies as well as characteristic facial anomalies. She also exhibited a supraumbilical midline abdominal raphe which has not been observed in other patients with Robinow syndrome. In contrast to the clinical data of four previously reported individuals with DVL3-related Robinow syndrome, short stature was not present in this individual at the age of 15 years. These findings expand the clinical spectrum of Robinow syndrome associated with DVL3 mutations. To date, comparison of clinical data of DVL3 mutation-positive individuals with those of patients with genetically different forms did not allow delineation of gene-specific phenotypes.

Patient with a novel syndrome with multiple benign hepatic lesions and extrahepatic neoplasms.

Simultaneous occurrence of benign hepatic lesions of different types is a sporadic phenomenon. To the best of our knowledge, we report the first clinical case of a syndrome with simultaneous manifestations of three different entities of benign liver tumors (hepatocellular adenoma, focal nodular hyperplasia and hemangioma) with a novel mutation detected in the liver adenoma and in the presence of a number of further extrahepatic organ neoplasms. Furthermore, we describe for the first time the presence of liver epithelial cells of hepatocytic phenotype expressing cytokeratin 7 (CK7) at the border of the adenoma. These findings may be important for explaining pathogenesis of benign as well as malignant tumors based on genetic and histopathological features.

Three-Dimensional Histological Characterization of the Placental Vasculature Using Light Sheet Microscopy.

The placenta is the first embryonic organ, representing the connection between the embryo and the mother, and is therefore necessary for the embryo's growth and survival. To meet the ever-growing need for nutrient and gas exchange, the maternal spiral arteries undergo extensive remodeling, thus increasing the uteroplacental blood flow by 16-fold. However, the insufficient remodeling of the spiral arteries can lead to severe pregnancy-associated disorders, including but not limited to pre-eclampsia. Insufficient endovascular trophoblast invasion plays a key role in the manifestation of pre-eclampsia; however, the underlying processes are complex and still unknown. Classical histopathology is based on two-dimensional section microscopy, which lacks a volumetric representation of the vascular remodeling process. To further characterize the uteroplacental vascularization, a detailed, non-destructive, and subcellular visualization is beneficial. In this study, we use light sheet microscopy for optical sectioning, thus establishing a method to obtain a three-dimensional visualization of the vascular system in the placenta. By introducing a volumetric visualization method of the placenta, we could establish a powerful tool to deeply investigate the heterogeneity of the spiral arteries during the remodeling process, evaluate the state-of-the-art treatment options, effects on vascularization, and, ultimately, reveal new insights into the underlying pathology of pre-eclampsia.

Broadening the phenotypic and molecular spectrum of FINCA syndrome: Biallelic NHLRC2 variants in 15 novel individuals.

FINCA syndrome [MIM: 618278] is an autosomal recessive multisystem disorder characterized by fibrosis, neurodegeneration and cerebral angiomatosis. To date, 13 patients from nine families with biallelic NHLRC2 variants have been published. In all of them, the recurrent missense variant p.(Asp148Tyr) was detected on at least one allele. Common manifestations included lung or muscle fibrosis, respiratory distress, developmental delay, neuromuscular symptoms and seizures often followed by early death due to rapid disease progression.Here, we present 15 individuals from 12 families with an overlapping phenotype associated with nine novel NHLRC2 variants identified by exome analysis. All patients described here presented with moderate to severe global developmental delay and variable disease progression. Seizures, truncal hypotonia and movement disorders were frequently observed. Notably, we also present the first eight cases in which the recurrent p.(Asp148Tyr) variant was not detected in either homozygous or compound heterozygous state.We cloned and expressed all novel and most previously published non-truncating variants in HEK293-cells. From the results of these functional studies, we propose a potential genotype-phenotype correlation, with a greater reduction in protein expression being associated with a more severe phenotype.Taken together, our findings broaden the known phenotypic and molecular spectrum and emphasize that NHLRC2-related disease should be considered in patients presenting with intellectual disability, movement disorders, neuroregression and epilepsy with or without pulmonary involvement.

Case report: KPTN gene-related syndrome associated with a spectrum of neurodevelopmental anomalies including severe epilepsy.

Biallelic variants in the kaptin gene KPTN were identified recently in individuals with a novel syndrome referred to as autosomal recessive intellectual developmental disorder 41 (MRT41). MRT41 is characterized by developmental delay, predominantly in language development, behavioral abnormalities, and epilepsy. Only about 15 affected individuals have been described in the literature, all with primary or secondary macrocephaly. Using exome sequencing, we identified three different biallelic variants in KPTN in five affected individuals from three unrelated families. In total, two KPTN variants were already reported as a loss of function variants. A novel splice site variant in KPTN was detected in two unrelated families of this study. The core phenotype with neurodevelopment delay was present in all patients. However, macrocephaly was not present in at least one patient. In total, two patients exhibited developmental and epileptic encephalopathies with generalized tonic-clonic seizures that were drug-resistant in one of them. Thus, we further delineate the KPTN-related syndrome, especially emphasizing the severity of epilepsy phenotypes and difficulties in treatment in patients of our cohort.

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.

CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph.

Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype-genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.

Familial Xp11.22 microdeletion including SHROOM4 and CLCN5 is associated with intellectual disability, short stature, microcephaly and Dent disease: a case report.

BACKGROUND: Two interstitial microdeletions Xp11.22 including the CLCN5 and SHROOM4 genes were recently reported in a male individual affected with Dent disease, short stature, psychomotor delay and minor facial anomalies. Dent disease, characterized by a specific renal phenotype, is caused by truncating mutations of CLCN5 in the majority of affected cases. CASE PRESENTATION: Here, we present clinical and molecular findings in a male patient with clinical signs of Dent disease, developmental delay, short stature, microcephaly, and facial dysmorphism. Using molecular karyotyping we identified a hemizygous interstitial microdeletion Xp11.23p.11.22 of about 700 kb, which was inherited from his asymptomatic mother. Among the six deleted genes is CLCN5, which explains the renal phenotype in our patient. SHROOM4, which is partially deleted in this patient, is involved in neuronal development and was shown to be associated with X-linked intellectual disability. This is a candidate gene, the loss of which is thought to be associated with his further clinical manifestations. To rule out mutations in other genes related to intellectual disability, whole exome sequencing was performed. No other pathogenic variants that could explain the phenotypic features, were found. CONCLUSION: We compared the clinical findings of the patient presented here with the reported case with an Xp11.22 microdeletion including CLCN5 and SHROOM4 and re-defined the phenotypic spectrum associated with this microdeletion.

Differentiation of MISSLA and Fanconi anaemia by computer-aided image analysis and presentation of two novel MISSLA siblings.

Variants in DONSON were recently identified as the cause of microcephaly, short stature, and limb abnormalities syndrome (MISSLA). The clinical spectra of MISSLA and Fanconi anaemia (FA) strongly overlap. For that reason, some MISSLA patients have been clinically diagnosed with FA. Here, we present the clinical data of siblings with MISSLA featuring a novel DONSON variant and summarize the current literature on MISSLA. Additionally, we perform computer-aided image analysis using the DeepGestalt technology to test how distinct the facial features of MISSLA and FA patients are. We show that MISSLA has a specific facial gestalt. Notably, we find that also FA patients feature facial characteristics recognizable by computer-aided image analysis. We conclude that computer-assisted image analysis improves diagnostic precision in both MISSLA and FA.

Evaluation of the role of STAP1 in Familial Hypercholesterolemia.

Familial hypercholesterolemia (FH) is characterised by elevated serum levels of low-density lipoprotein cholesterol (LDL-C) and a substantial risk for cardiovascular disease. The autosomal-dominant FH is mostly caused by mutations in LDLR (low density lipoprotein receptor), APOB (apolipoprotein B), and PCSK9 (proprotein convertase subtilisin/kexin). Recently, STAP1 has been suggested as a fourth causative gene. We analyzed STAP1 in 75 hypercholesterolemic patients from Berlin, Germany, who are negative for mutations in canonical FH genes. In 10 patients with negative family history, we additionally screened for disease causing variants in LDLRAP1 (low density lipoprotein receptor adaptor protein 1), associated with autosomal-recessive hypercholesterolemia. We identified one STAP1 variant predicted to be disease causing. To evaluate association of serum lipid levels and STAP1 carrier status, we analyzed 20 individuals from a population based cohort, the Cooperative Health Research in South Tyrol (CHRIS) study, carrying rare STAP1 variants. Out of the same cohort we randomly selected 100 non-carriers as control. In the Berlin FH cohort STAP1 variants were rare. In the CHRIS cohort, we obtained no statistically significant differences between carriers and non-carriers of STAP1 variants with respect to lipid traits. Until such an association has been verified in more individuals with genetic variants in STAP1, we cannot estimate whether STAP1 generally is a causative gene for FH.