Role of CAMK2D in neurodevelopment and associated conditions.

The calcium/calmodulin-dependent protein kinase type 2 (CAMK2) family consists of four different isozymes, encoded by four different genes-CAMK2A, CAMK2B, CAMK2G, and CAMK2D-of which the first three have been associated recently with neurodevelopmental disorders. CAMK2D is one of the major CAMK2 proteins expressed in the heart and has been associated with cardiac anomalies. Although this CAMK2 isoform is also known to be one of the major CAMK2 subtypes expressed during early brain development, it has never been linked with neurodevelopmental disorders until now. Here we show that CAMK2D plays an important role in neurodevelopment not only in mice but also in humans. We identified eight individuals harboring heterozygous variants in CAMK2D who display symptoms of intellectual disability, delayed speech, behavioral problems, and dilated cardiomyopathy. The majority of the variants tested lead to a gain of function (GoF), which appears to cause both neurological problems and dilated cardiomyopathy. In contrast, loss-of-function (LoF) variants appear to induce only neurological symptoms. Together, we describe a cohort of individuals with neurodevelopmental disorders and cardiac anomalies, harboring pathogenic variants in CAMK2D, confirming an important role for the CAMK2D isozyme in both heart and brain function.

Delineation of the adult phenotype of Coffin-Siris syndrome in 35 individuals.

Coffin-Siris syndrome (CSS) is a rare multisystemic autosomal dominant disorder. Since 2012, alterations in genes of the SWI/SNF complex were identified as the molecular basis of CSS, studying largely pediatric cohorts. Therefore, there is a lack of information on the phenotype in adulthood, particularly on the clinical outcome in adulthood and associated risks. In an international collaborative effort, data from 35 individuals ≥ 18 years with a molecularly ascertained CSS diagnosis (variants in ARID1B, ARID2, SMARCA4, SMARCB1, SMARCC2, SMARCE1, SOX11, BICRA) using a comprehensive questionnaire was collected. Our results indicate that overweight and obesity are frequent in adults with CSS. Visual impairment, scoliosis, and behavioral anomalies are more prevalent than in published pediatric or mixed cohorts. Cognitive outcomes range from profound intellectual disability (ID) to low normal IQ, with most individuals having moderate ID. The present study describes the first exclusively adult cohort of CSS individuals. We were able to delineate some features of CSS that develop over time and have therefore been underrepresented in previously reported largely pediatric cohorts, and provide recommendations for follow-up.

Understanding recessive disease risk in multi-ethnic populations with different degrees of consanguinity.

Population medical genetics aims at translating clinically relevant findings from recent studies of large cohorts into healthcare for individuals. Genetic counseling concerning reproductive risks and options is still mainly based on family history, and consanguinity is viewed to increase the risk for recessive diseases regardless of the demographics. However, in an increasingly multi-ethnic society with diverse approaches to partner selection, healthcare professionals should also sharpen their intuition for the influence of different mating schemes in non-equilibrium dynamics. We, therefore, revisited the so-called out-of-Africa model and studied in forward simulations with discrete and not overlapping generations the effect of inbreeding on the average number of recessive lethals in the genome. We were able to reproduce in both frameworks the drop in the incidence of recessive disorders, which is a transient phenomenon during and after the growth phase of a population, and therefore showed their equivalence. With the simulation frameworks, we also provide the means to study and visualize the effect of different kin sizes and mating schemes on these parameters for educational purposes.

Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.

BACKGROUND: Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias. OBJECTIVE: We present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias. MATERIALS AND METHODS: We trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test-retest precision of our model ensemble on longitudinal data. RESULTS: The mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test-retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert. CONCLUSION: We demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.

A CRISPR-Cas9-engineered mouse model for GPI-anchor deficiency mirrors human phenotypes and exhibits hippocampal synaptic dysfunctions.

Pathogenic germline mutations in PIGV lead to glycosylphosphatidylinositol biosynthesis deficiency (GPIBD). Individuals with pathogenic biallelic mutations in genes of the glycosylphosphatidylinositol (GPI)-anchor pathway exhibit cognitive impairments, motor delay, and often epilepsy. Thus far, the pathophysiology underlying the disease remains unclear, and suitable rodent models that mirror all symptoms observed in human patients have not been available. Therefore, we used CRISPR-Cas9 to introduce the most prevalent hypomorphic missense mutation in European patients, Pigv:c.1022C > A (p.A341E), at a site that is conserved in mice. Mirroring the human pathology, mutant Pigv341E mice exhibited deficits in motor coordination, cognitive impairments, and alterations in sociability and sleep patterns, as well as increased seizure susceptibility. Furthermore, immunohistochemistry revealed reduced synaptophysin immunoreactivity in Pigv341E mice, and electrophysiology recordings showed decreased hippocampal synaptic transmission that could underlie impaired memory formation. In single-cell RNA sequencing, Pigv341E-hippocampal cells exhibited changes in gene expression, most prominently in a subtype of microglia and subicular neurons. A significant reduction in Abl1 transcript levels in several cell clusters suggested a link to the signaling pathway of GPI-anchored ephrins. We also observed elevated levels of Hdc transcripts, which might affect histamine metabolism with consequences for circadian rhythm. This mouse model will not only open the doors to further investigation into the pathophysiology of GPIBD, but will also deepen our understanding of the role of GPI-anchor-related pathways in brain development.

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.

Biallelic variants in PIGN cause Fryns syndrome, multiple congenital anomalies-hypotonia-seizures syndrome, and neurologic phenotypes: A genotype-phenotype correlation study.

PURPOSE: Biallelic PIGN variants have been described in Fryns syndrome, multiple congenital anomalies-hypotonia-seizure syndrome (MCAHS), and neurologic phenotypes. The full spectrum of clinical manifestations in relation to the genotypes is yet to be reported. METHODS: Genotype and phenotype data were collated and analyzed for 61 biallelic PIGN cases: 21 new and 40 previously published cases. Functional analysis was performed for 2 recurrent variants (c.2679C>G p.Ser893Arg and c.932T>G p.Leu311Trp). RESULTS: Biallelic-truncating variants were detected in 16 patients-10 with Fryns syndrome, 1 with MCAHS1, 2 with Fryns syndrome/MCAHS1, and 3 with neurologic phenotype. There was an increased risk of prenatal or neonatal death within this group (6 deaths were in utero or within 2 months of life; 6 pregnancies were terminated). Incidence of polyhydramnios, congenital anomalies (eg, diaphragmatic hernia), and dysmorphism was significantly increased. Biallelic missense or mixed genotype were reported in the remaining 45 cases-32 showed a neurologic phenotype and 12 had MCAHS1. No cases of diaphragmatic hernia or abdominal wall defects were seen in this group except patient 1 in which we found the missense variant p.Ser893Arg to result in functionally null alleles, suggesting the possibility of an undescribed functionally important region in the final exon. For all genotypes, there was complete penetrance for developmental delay and near-complete penetrance for seizures and hypotonia in patients surviving the neonatal period. CONCLUSION: We have expanded the described spectrum of phenotypes and natural history associated with biallelic PIGN variants. Our study shows that biallelic-truncating variants usually result in the more severe Fryns syndrome phenotype, but neurologic problems, such as developmental delay, seizures, and hypotonia, present across all genotypes. Functional analysis should be considered when the genotypes do not correlate with the predicted phenotype because there may be other functionally important regions in PIGN that are yet to be discovered.

Perspectives on the future of dysmorphology.

The field of clinical genetics and genomics continues to evolve. In the past few decades, milestones like the initial sequencing of the human genome, dramatic changes in sequencing technologies, and the introduction of artificial intelligence, have upended the field and offered fascinating new insights. Though difficult to predict the precise paths the field will follow, rapid change may continue to be inevitable. Within genetics, the practice of dysmorphology, as defined by pioneering geneticist David W. Smith in the 1960s as "the study of, or general subject of abnormal development of tissue form" has also been affected by technological advances as well as more general trends in biomedicine. To address possibilities, potential, and perils regarding the future of dysmorphology, a group of clinical geneticists, representing different career stages, areas of focus, and geographic regions, have contributed to this piece by providing insights about how the practice of dysmorphology will develop over the next several decades.

Next-generation phenotyping contributing to the identification of a 4.7 kb deletion in KANSL1 causing Koolen-de Vries syndrome.

Next-generation phenotyping (NGP) is an application of advanced methods of computer vision on medical imaging data such as portrait photos of individuals with rare disorders. NGP on portraits results in gestalt scores that can be used for the selection of appropriate genetic tests, and for the interpretation of the molecular data. Here, we report on an exceptional case of a young girl that was presented at the age of 8 and 15 and enrolled in NGP diagnostics on the latter occasion. The girl had clinical features associated with Koolen-de Vries syndrome (KdVS) and a suggestive facial gestalt. However, chromosomal microarray (CMA), Sanger sequencing, multiplex ligation-dependent probe analysis (MLPA), and trio exome sequencing remained inconclusive. Based on the highly indicative gestalt score for KdVS, the decision was made to perform genome sequencing to also evaluate noncoding variants. This analysis revealed a 4.7 kb de novo deletion partially affecting intron 6 and exon 7 of the KANSL1 gene. This is the smallest reported structural variant to date for this phenotype. The case illustrates how NGP can be integrated into the iterative diagnostic process of test selection and interpretation of sequencing results.

Mutations in PIGU Impair the Function of the GPI Transamidase Complex, Causing Severe Intellectual Disability, Epilepsy, and Brain Anomalies.

The glycosylphosphatidylinositol (GPI) anchor links over 150 proteins to the cell surface and is present on every cell type. Many of these proteins play crucial roles in neuronal development and function. Mutations in 18 of the 29 genes implicated in the biosynthesis of the GPI anchor have been identified as the cause of GPI biosynthesis deficiencies (GPIBDs) in humans. GPIBDs are associated with intellectual disability and seizures as their cardinal features. An essential component of the GPI transamidase complex is PIGU, along with PIGK, PIGS, PIGT, and GPAA1, all of which link GPI-anchored proteins (GPI-APs) onto the GPI anchor in the endoplasmic reticulum (ER). Here, we report two homozygous missense mutations (c.209T>A [p.Ile70Lys] and c.1149C>A [p.Asn383Lys]) in five individuals from three unrelated families. All individuals presented with global developmental delay, severe-to-profound intellectual disability, muscular hypotonia, seizures, brain anomalies, scoliosis, and mild facial dysmorphism. Using multicolor flow cytometry, we determined a characteristic profile for GPI transamidase deficiency. On granulocytes this profile consisted of reduced cell-surface expression of fluorescein-labeled proaerolysin (FLAER), CD16, and CD24, but not of CD55 and CD59; additionally, B cells showed an increased expression of free GPI anchors determined by T5 antibody. Moreover, computer-assisted facial analysis of different GPIBDs revealed a characteristic facial gestalt shared among individuals with mutations in PIGU and GPAA1. Our findings improve our understanding of the role of the GPI transamidase complex in the development of nervous and skeletal systems and expand the clinical spectrum of disorders belonging to the group of inherited GPI-anchor deficiencies.

The Discovery of a LEMD2-Associated Nuclear Envelopathy with Early Progeroid Appearance Suggests Advanced Applications for AI-Driven Facial Phenotyping.

Over a relatively short period of time, the clinical geneticist's "toolbox" has been expanded by machine-learning algorithms for image analysis, which can be applied to the task of syndrome identification on the basis of facial photographs, but these technologies harbor potential beyond the recognition of established phenotypes. Here, we comprehensively characterized two individuals with a hitherto unknown genetic disorder caused by the same de novo mutation in LEMD2 (c.1436C>T;p.Ser479Phe), the gene which encodes the nuclear envelope protein LEM domain-containing protein 2 (LEMD2). Despite different ages and ethnic backgrounds, both individuals share a progeria-like facial phenotype and a distinct combination of physical and neurologic anomalies, such as growth retardation; hypoplastic jaws crowded with multiple supernumerary, yet unerupted, teeth; and cerebellar intention tremor. Immunofluorescence analyses of patient fibroblasts revealed mutation-induced disturbance of nuclear architecture, recapitulating previously published data in LEMD2-deficient cell lines, and additional experiments suggested mislocalization of mutant LEMD2 protein within the nuclear lamina. Computational analysis of facial features with two different deep neural networks showed phenotypic proximity to other nuclear envelopathies. One of the algorithms, when trained to recognize syndromic similarity (rather than specific syndromes) in an unsupervised approach, clustered both individuals closely together, providing hypothesis-free hints for a common genetic etiology. We show that a recurrent de novo mutation in LEMD2 causes a nuclear envelopathy whose prognosis in adolescence is relatively good in comparison to that of classical Hutchinson-Gilford progeria syndrome, and we suggest that the application of artificial intelligence to the analysis of patient images can facilitate the discovery of new genetic disorders.

Mutations in PIGB Cause an Inherited GPI Biosynthesis Defect with an Axonal Neuropathy and Metabolic Abnormality in Severe Cases.

Proteins anchored to the cell surface via glycosylphosphatidylinositol (GPI) play various key roles in the human body, particularly in development and neurogenesis. As such, many developmental disorders are caused by mutations in genes involved in the GPI biosynthesis and remodeling pathway. We describe ten unrelated families with bi-allelic mutations in PIGB, a gene that encodes phosphatidylinositol glycan class B, which transfers the third mannose to the GPI. Ten different PIGB variants were found in these individuals. Flow cytometric analysis of blood cells and fibroblasts from the affected individuals showed decreased cell surface presence of GPI-anchored proteins. Most of the affected individuals have global developmental and/or intellectual delay, all had seizures, two had polymicrogyria, and four had a peripheral neuropathy. Eight children passed away before four years old. Two of them had a clinical diagnosis of DOORS syndrome (deafness, onychodystrophy, osteodystrophy, mental retardation, and seizures), a condition that includes sensorineural deafness, shortened terminal phalanges with small finger and toenails, intellectual disability, and seizures; this condition overlaps with the severe phenotypes associated with inherited GPI deficiency. Most individuals tested showed elevated alkaline phosphatase, which is a characteristic of the inherited GPI deficiency but not DOORS syndrome. It is notable that two severely affected individuals showed 2-oxoglutaric aciduria, which can be seen in DOORS syndrome, suggesting that severe cases of inherited GPI deficiency and DOORS syndrome might share some molecular pathway disruptions.

[Artificial intelligence in the diagnosis of rare disorders: the development of phenotype analysis]. / Künstliche Intelligenz bei der Diagnose Seltener Erkrankungen: die Entwicklung der Phänotyp-Analyse.

Rare diseases can often be diagnosed by carefully assessing the phenotype of the patient, as characteristic deviations (dysmorphisms) occur in many genetic diseases. These affect, for example, the features of the face - the "facial gestalt."This paper highlights an area of artificial intelligence (AI) in which there has been great progress in recent years: the recognition of characteristic patterns in medical image data using deep, convolutional neural networks (next-generation phenotyping - NGP). The technical basis of the method is briefly described and the high relevance of FAIR data for the scientific community to develop AI is discussed. Furthermore, it is explained why decisions made by AI should always remain comprehensible and how it can overcome the challenges with regard to data protection and transparency.In the future, software applications with AI will support medical professionals in the diagnosis of rare diseases. AI will be trustworthy if patients retain their data sovereignty and can understand how the diagnosis was made.

Extending the allelic spectrum at noncoding risk loci of orofacial clefting.

Genome-wide association studies (GWAS) have generated unprecedented insights into the genetic etiology of orofacial clefting (OFC). The moderate effect sizes of associated noncoding risk variants and limited access to disease-relevant tissue represent considerable challenges for biological interpretation of genetic findings. As rare variants with stronger effect sizes are likely to also contribute to OFC, an alternative approach to delineate pathogenic mechanisms is to identify private mutations and/or an increased burden of rare variants in associated regions. This report describes a framework for targeted resequencing at selected noncoding risk loci contributing to nonsyndromic cleft lip with/without cleft palate (nsCL/P), the most frequent OFC subtype. Based on GWAS data, we selected three risk loci and identified candidate regulatory regions (CRRs) through the integration of credible SNP information, epigenetic data from relevant cells/tissues, and conservation scores. The CRRs (total 57 kb) were resequenced in a multiethnic study population (1061 patients; 1591 controls), using single-molecule molecular inversion probe technology. Combining evidence from in silico variant annotation, pedigree- and burden analyses, we identified 16 likely deleterious rare variants that represent new candidates for functional studies in nsCL/P. Our framework is scalable and represents a promising approach to the investigation of additional congenital malformations with multifactorial etiology.

De Novo Mutations in SLC25A24 Cause a Craniosynostosis Syndrome with Hypertrichosis, Progeroid Appearance, and Mitochondrial Dysfunction.

Gorlin-Chaudhry-Moss syndrome (GCMS) is a dysmorphic syndrome characterized by coronal craniosynostosis and severe midface hypoplasia, body and facial hypertrichosis, microphthalmia, short stature, and short distal phalanges. Variable lipoatrophy and cutis laxa are the basis for a progeroid appearance. Using exome and genome sequencing, we identified the recurrent de novo mutations c.650G>A (p.Arg217His) and c.649C>T (p.Arg217Cys) in SLC25A24 in five unrelated girls diagnosed with GCMS. Two of the girls had pronounced neonatal progeroid features and were initially diagnosed with Wiedemann-Rautenstrauch syndrome. SLC25A24 encodes a mitochondrial inner membrane ATP-Mg/Pi carrier. In fibroblasts from affected individuals, the mutated SLC25A24 showed normal stability. In contrast to control cells, the probands' cells showed mitochondrial swelling, which was exacerbated upon treatment with hydrogen peroxide (H2O2). The same effect was observed after overexpression of the mutant cDNA. Under normal culture conditions, the mitochondrial membrane potential of the probands' fibroblasts was intact, whereas ATP content in the mitochondrial matrix was lower than that in control cells. However, upon H2O2 exposure, the membrane potential was significantly elevated in cells harboring the mutated SLC25A24. No reduction of mitochondrial DNA copy number was observed. These findings demonstrate that mitochondrial dysfunction with increased sensitivity to oxidative stress is due to the SLC25A24 mutations. Our results suggest that the SLC25A24 mutations induce a gain of pathological function and link mitochondrial ATP-Mg/Pi transport to the development of skeletal and connective tissue.

PIGT-CDG, a disorder of the glycosylphosphatidylinositol anchor: description of 13 novel patients and expansion of the clinical characteristics.

PURPOSE: To provide a detailed electroclinical description and expand the phenotype of PIGT-CDG, to perform genotype-phenotype correlation, and to investigate the onset and severity of the epilepsy associated with the different genetic subtypes of this rare disorder. Furthermore, to use computer-assisted facial gestalt analysis in PIGT-CDG and to the compare findings with other glycosylphosphatidylinositol (GPI) anchor deficiencies. METHODS: We evaluated 13 children from eight unrelated families with homozygous or compound heterozygous pathogenic variants in PIGT. RESULTS: All patients had hypotonia, severe developmental delay, and epilepsy. Epilepsy onset ranged from first day of life to two years of age. Severity of the seizure disorder varied from treatable seizures to severe neonatal onset epileptic encephalopathies. The facial gestalt of patients resembled that of previously published PIGT patients as they were closest to the center of the PIGT cluster in the clinical face phenotype space and were distinguishable from other gene-specific phenotypes. CONCLUSION: We expand our knowledge of PIGT. Our cases reaffirm that the use of genetic testing is essential for diagnosis in this group of disorders. Finally, we show that computer-assisted facial gestalt analysis accurately assigned PIGT cases to the multiple congenital anomalies-hypotonia-seizures syndrome phenotypic series advocating the additional use of next-generation phenotyping technology.

A likelihood ratio-based method to predict exact pedigrees for complex families from next-generation sequencing data.

MOTIVATION: Next generation sequencing technology considerably changed the way we screen for pathogenic mutations in rare Mendelian disorders. However, the identification of the disease-causing mutation amongst thousands of variants of partly unknown relevance is still challenging and efficient techniques that reduce the genomic search space play a decisive role. Often segregation- or linkage analysis are used to prioritize candidates, however, these approaches require correct information about the degree of relationship among the sequenced samples. For quality assurance an automated control of pedigree structures and sample assignment is therefore highly desirable in order to detect label mix-ups that might otherwise corrupt downstream analysis. RESULTS: We developed an algorithm based on likelihood ratios that discriminates between different classes of relationship for an arbitrary number of genotyped samples. By identifying the most likely class we are able to reconstruct entire pedigrees iteratively, even for highly consanguineous families. We tested our approach on exome data of different sequencing studies and achieved high precision for all pedigree predictions. By analyzing the precision for varying degrees of relatedness or inbreeding we could show that a prediction is robust down to magnitudes of a few hundred loci. AVAILABILITY AND IMPLEMENTATION: A java standalone application that computes the relationships between multiple samples as well as a Rscript that visualizes the pedigree information is available for download as well as a web service at www.gene-talk.de CONTACT: heinrich@molgen.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.

Advances in computer-assisted syndrome recognition by the example of inborn errors of metabolism.

Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer-assisted syndromology. However, the ability to recognize a syndromic facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systematic analysis of these influencing factors we chose the lysosomal storage diseases mucolipidosis as well as mucopolysaccharidosis type I and II that are known for their wide and overlapping phenotypic spectra. For a dysmorphic comparison we used Smith-Lemli-Opitz syndrome as another inborn error of metabolism and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 289 patients in total, achieved a mean accuracy of 62%. We also developed a simulation framework to analyze the effect of potential confounders, such as cohort size, age, sex, or ethnic background on the distinguishability of phenotypes. We found that the true positive rate increases for all analyzed disorders for growing cohorts (n = [10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype-specific value, which has not been reached yet for any of the studied disorders. This should also be a motivation to further intensify data sharing efforts, as computer-assisted syndrome classification can still be improved by enlarging the available training sets.

Reduced cell surface levels of GPI-linked markers in a new case with PIGG loss of function.

Glycosylphosphatidylinositol (GPI) is a glycolipid that tethers more than 150 different proteins to the cell surface. Aberrations in biosynthesis of GPI anchors cause congenital disorders of glycosylation with clinical features including intellectual disability (ID), seizures, and facial dysmorphism. Here, we present two siblings with ID, cerebellar hypoplasia, cerebellar ataxia, early-onset seizures, and minor facial dysmorphology. Using exome sequencing, we identified a homozygous nonsense variant (NM_001127178.1:c.1640G>A, p.Trp547*) in the gene Phosphatidylinositol Glycan Anchor Biosynthesis, Class G (PIGG) in both the patients. Variants in several other GPI anchor synthesis genes lead to a reduced expression of GPI-anchored proteins (GPI-APs) that can be measured by flow cytometry. No significant differences in GPI-APs could be detected in patient granulocytes, consistent with recent findings. However, fibroblasts showed a reduced global level of GPI anchors and of specific GPI-linked markers. These findings suggest that fibroblasts might be more sensitive to pathogenic variants in GPI synthesis pathway and are well suited to screen for GPI-anchor deficiencies. Based on genetic and functional evidence, we confirm that pathogenic variants in PIGG cause an ID syndrome, and we find that loss of function of PIGG is associated with GPI deficiency.

Mutations in PGAP3 impair GPI-anchor maturation, causing a subtype of hyperphosphatasia with mental retardation.

Glycosylphophatidylinositol (GPI)-anchored proteins play important roles in many biological processes, and mutations affecting proteins involved in the synthesis of the GPI anchor are reported to cause a wide spectrum of intellectual disabilities (IDs) with characteristic additional phenotypic features. Here, we describe a total of five individuals (from three unrelated families) in whom we identified mutations in PGAP3, encoding a protein that is involved in GPI-anchor maturation. Three siblings in a consanguineous Pakistani family presented with profound developmental delay, severe ID, no speech, psychomotor delay, and postnatal microcephaly. A combination of autozygosity mapping and exome sequencing identified a 13.8 Mb region harboring a homozygous c.275G>A (p.Gly92Asp) variant in PGAP3 region 17q11.2-q21.32. Subsequent testing showed elevated serum alkaline phosphatase (ALP), a GPI-anchored enzyme, in all three affected children. In two unrelated individuals in a cohort with developmental delay, ID, and elevated ALP, we identified compound-heterozygous variants c.439dupC (p.Leu147Profs(∗)16) and c.914A>G (p.Asp305Gly) and homozygous variant c.314C>G (p.Pro105Arg). The 1 bp duplication causes a frameshift and nonsense-mediated decay. Further evidence supporting pathogenicity of the missense mutations c.275G>A, c.314C>G, and c.914A>G was provided by the absence of the variants from ethnically matched controls, phylogenetic conservation, and functional studies on Chinese hamster ovary cell lines. Taken together with recent data on PGAP2, these results confirm the importance of the later GPI-anchor remodelling steps for normal neuronal development. Impairment of PGAP3 causes a subtype of hyperphosphatasia with ID, a congenital disorder of glycosylation that is also referred to as Mabry syndrome.