GATA2 deficiency syndrome (G2DS) is a rare autosomal dominant genetic disease predisposing to a range of symptoms of which myeloid malignancy and immunodeficiency including recurrent infections are most common. In the last decade since it was first reported, there have been over 465 individuals identified carrying a pathogenic or likely pathogenic germline GATA2 variant with symptoms of G2DS, with 231 of these confirmed to be familial and 22 de novo. For those that develop myeloid malignancy (75% of all carriers with G2DS disease symptoms), the median age of onset is 17 years (range 0-78 years) and myelodysplastic syndrome (MDS) is the first diagnosis in 75% of these cases with acute myeloid leukemia (AML) in a further 9%. All variant types appear to predispose to myeloid malignancy and immunodeficiency. Apart from lymphedema in which haploinsufficiency seems necessary, the mutational requirements of the other less common G2DS phenotypes is still unclear. These predominantly loss-of-function variants impact GATA2 expression and function in numerous ways including perturbations to DNA binding, protein structure, protein:protein interactions, and gene transcription, splicing and expression. In this review, we provide the first expert curated ACMG/AMP classification with codes of published variants compatible for use in clinical or diagnostic settings.

A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions in order to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral’s features have been expanded and its dataset has consistently grown. There are currently 1,615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come.

DECIPHER (https://www.deciphergenomics.org) is a free web platform for sharing anonymised phenotype-linked variant data from rare disease patients. Its dynamic interpretation interfaces contextualise genomic and phenotypic data to enable more informed variant interpretation, incorporating international standards for variant classification. DECIPHER supports almost all types of germline and mosaic variation in the nuclear and mitochondrial genome: sequence variants, short tandem repeats, copy-number variants and large structural variants. Patient phenotypes are deposited using Human Phenotype Ontology (HPO) terms, supplemented by quantitative data, which is aggregated to derive gene-specific phenotypic summaries. It hosts data from >250 projects from ~40 countries, openly sharing ~40,000 patient records containing >51,000 variants and >172,000 phenotype terms. The rich phenotype-linked variant data in DECIPHER drives rare disease research and diagnosis by enabling patient matching within DECIPHER and with other resources, and has been cited in >2,600 publications. In this paper, we describe the types of data deposited to DECIPHER, the variant interpretation tools, and patient matching interfaces which make DECIPHER an invaluable rare disease resource.

We have created a panel of twenty-nine NF1 variant cDNAs representing benign missense (MS) variants, pathogenic MS variants, many with clinically relevant phenotypes, in-frame deletions, splice variants, and nonsense (NS) variants. We have determined the functional consequences of the variants, assessing their ability to produce mature neurofibromin and restore Ras signaling activity in NF1 null (-/-) cells. cDNAs demonstrate variant-specific differences in neurofibromin protein levels, suggesting that some variants lead to protein instability or enhanced degradation. When expressed at high levels, some variant proteins are still able to repress Ras activity, indicating that the NF1 phenotype may be due to protein instability. In contrast, other variant proteins are incapable of repressing Ras activity, indicating that some do not functionally engage Ras and stimulate GTP-ase activity. We observed that stability and Ras activity can be mutually exclusive. These assays allow us to categorize variants by functional effects, may help to classify variants of unknown significance, and may have future implications for more directed therapeutics.

Hereditary disorders are frequently caused by genetic variants that affect pre-mRNA splicing. Whilst genetic variants in the canonical splice motifs are almost always disrupting splicing, the pathogenicity of variants in the non-canonical splice sites (NCSS) and deep intronic (DI) regions are difficult to predict. Multiple splice prediction tools have been developed for this purpose, with the latest tools employing deep learning algorithms. We benchmarked established and deep learning splice prediction tools on gold standard sets of variants in the ABCA4 and MYBPC3 genes associated with Stargardt disease (STGD1) and cardiomyopathy, respectively, with functional assessment in midigene and minigene splice assays. The best performing splice prediction tool for both NCSS and DI variants in ABCA4 was SpliceAI, whilst SpliceSiteFinder-like performed best for NCSS variants in MYBPC3. Overall, the performance in a real time clinical setting is much more modest than reported by the developers of the tools.

Skeletal dysplasias are a heterogeneous group of disorders ranging from mild to lethal skeletal defects. We investigated two unrelated families with individuals presenting with a severe skeletal disorder. In family NMD02, affected individuals had a dysostosis multiplex-like skeletal dysplasia and severe short stature (<-8.5 SD). They manifested increasing coarse facial features, protruding abdomens and progressive skeletal changes, reminiscent of mucopolysaccharidosis. The patients gradually lost mobility and the two oldest affected individuals died in their twenties. The affected child in family ID01 had coarse facial features and severe skeletal dysplasia with clinical features similar to mucopolysaccharidosis. She had short stature, craniosynostosis, kyphoscoliosis and hip-joint subluxation. She died at the age of 5 years. Whole-exome sequencing identified two homozygous variants c.133C>T; p.(Arg45Trp) and c.215dupA; p.(Tyr72Ter) respectively, in the two families, affecting an evolutionary conserved gene TMEM251. Immunofluorescence and confocal studies on Human Osteosarcoma cells indicated that TMEM251 localized to the Golgi complex and plasma membrane. However, p.Arg45Trp mutant TMEM251 protein was targeted less efficiently to the membranes and the localization was punctate. Tmem251 knockdown by siRNA induced dedifferentiation of rat primary chondrocytes. Our work implicates TMEM251 in the pathogenesis of a novel disorder and suggests its potential function in chondrocyte differentiation.

It is possible to estimate the prior probability of pathogenicity for germline disease gene variants based on bioinformatic prediction of variant effect/s. However, routinely used approaches have likely led to the underestimation and underreporting of variants located outside donor and acceptor splice site motifs that affect mRNA processing. This review presents information about hereditary cancer gene germline variants, outside native splice sites, with experimentally validated splicing effects. We list 81 exonic variants that impact splicing regulatory elements in BRCA1, BRCA2, MLH1, MSH2, MSH6 and PMS2. We utilized a pre-existing large-scale BRCA1 functional dataset to map functional splicing regulatory elements, assess the relative performance of different tools to predict effects of 283 variants on such elements, and develop a generic workflow to prioritize variants that may impact splicing regulatory elements. We also describe rare examples of intronic variants that impact branchpoint sites and create pseudoexons. We discuss the challenges in predicting variant effect on branchpoint site usage and pseudoexonization, and suggest strategies to improve the bioinformatic prioritization of such variants for experimental validation. Importantly, our review highlights the importance of considering impact of variants outside donor and acceptor motifs on mRNA splicing and disease causation.

The ATP-binding cassette (ABC) transporter superfamily comprises membrane proteins that efflux various substrates across extra- and intra-cellular membranes. Mutations in ABC genes cause 21 human disorders or phenotypes with Mendelian inheritance, including cystic fibrosis, adrenoleukodystrophy, retinal degeneration, cholesterol, and bile transport defects. Common polymorphisms and rare variants in ABC genes are associated with several complex phenotypes such as gout, gallstones, and cholesterol levels. Overexpression or amplification of specific drug efflux genes contributes to chemotherapy multidrug resistance. Conservation of the ATP-binding domains of ABC transporters defines the superfamily members, and phylogenetic analysis groups the 48 human ABC transporters into seven distinct subfamilies. While the conservation of ABC genes across most vertebrate species is high, there is also considerable gene duplication, deletion, and evolutionary diversification.

Routine exome sequencing (ES) in individuals with neurodevelopmental disorders (NDD) remains inconclusive in >50%. Research analysis of unsolved cases can identify novel candidate genes but is time consuming, subjective, and hard to compare between labs. The field therefore needs automated and standardized assessment methods to prioritize candidates for matchmaking. We developed AutoCaSc (https://autocasc.uni-leipzig.de) based on our candidate scoring scheme (CaSc). We validated our approach using synthetic trios and real in-house trio ES data. AutoCaSc consistently (94.5%) scored variants in valid novel NDD genes in the top three ranks. In 93 real trio exomes, AutoCaSc identified most (97.5%) previously manually scored variants while evaluating additional highly scoring variants missed in manual evaluation. It identified candidate variants in previously undescribed NDD candidate genes ( CNTN2, DLGAP1, SMURF1, NRXN3, PRICKLE1). AutoCaSc enables anybody to quickly screen a variant for its plausibility in NDD. After contributing >40 descriptions of NDD associated genes, we provide usage recommendations based on our extensive experience. Our implementation is capable of pipeline integration and therefore allows screening of large cohorts for candidate genes. AutoCaSc empowers even small labs to a standardized matchmaking collaboration and to contribute to the ongoing identification of novel NDD entities.

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5’/3’ splice sites, branch sites or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on machine learning approach, comprehensive assessment of variant effect on different splicing motifs. We gathered a curated set of 4,616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. Bayesian analysis provided us the number of control variants, i.e. variants without impact on splicing, to mimic the deluge of variants from high throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, significantly better than 0.965 spliceAI for the same dataset. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: [https://sourceforge.net/projects/splicing-prediction-pipeline/](https://sourceforge.net/projects/splicing-prediction-pipeline/)

MORC2 gene encodes a ubiquitously expressed nuclear protein involved in chromatin remodeling, DNA repair, and transcriptional regulation. Heterozygous mutations in MORC2 gene have been associated with a spectrum of disorders affecting the peripheral nervous system such as Charcot-Marie-Tooth (CMT2Z), spinal muscular atrophy-like (SMA-like) with or without cerebellar involvement, and a developmental syndrome associated with impaired growth, craniofacial dysmorphism and axonal neuropathy (DIGFAN syndrome). Such variability in clinical manifestations associated with the increasing number of variants of unknown significance detected by next-generation sequencing constitutes a serious diagnostic challenge. Here we report the characterization of an in vitro model to evaluate the pathogenicity of variants of unknown significance based on MORC2 overexpression in a neuroblastoma cell line SH-EP or in cortical neurons. Likewise, we show that MORC2 mutants affect survival and trigger apoptosis over time in SH-EP cell line. Furthermore, overexpression in primary cortical neurons increases apoptotic cell death and decreases neurite outgrowth. Altogether, these approaches establish the pathogenicity of two new variants p.G444R and p.H446Q in three patients from two families. These new mutations in MORC2 gene are associated with autosomal dominant CMT and with adult late onset SMA-like phenotype, further increasing the spectrum of clinical manifestations associated with MORC2 mutations.

The American College of Medical Genetics and Genomics, and the Association for Molecular Pathology (ACMG/AMP) have proposed a set of evidence-based guidelines to support sequence variant interpretation. The ClinGen hearing loss expert panel (HL-EP) introduced further specifications into the ACMG/AMP framework for genetic hearing loss. This study developed a tool named VIP-HL, aiming to semi-automate the HL ACMG/AMP rules. VIP-HL aggregates information from external databases to automate 13 out of 24 ACMG/AMP rules specified by HL-EP, namely PVS1, PS1, PM1, PM2, PM4, PM5, PP3, BA1, BS1, BS2, BP3, BP4, and BP7. We benchmarked VIP-HL using 50 variants where 83 rules were activated by the ClinGen HL-EP. VIP-HL concordantly activated 96% (80/83) rules, significantly higher than that of by InterVar (47%; 39/83). Of 4948 ClinVar star 2+ variants from 142 deafness-related genes, VIP-HL achieved an overall variant interpretation concordance in 88.0% (4353/4948). VIP-HL is an integrated online tool for reliable automated variant classification in hearing loss genes. It assists curators in variant interpretation and provides a platform for users to share classifications with each other. VIP-HL is available with a user-friendly web interface at http://hearing.genetics.bgi.com/.

Brittle cornea syndrome (BCS) is a rare autosomal recessive disorder characterized by corneal thinning and fragility, leading to corneal rupture, the main hallmark of this disorder. Non-ocular symptoms include hearing loss, but also signs of connective tissue fragility, placing it in the Ehlers-Danlos syndrome (EDS) spectrum. It is caused by biallelic pathogenic variants in ZNF469 or PRDM5, which presumably encode transcription factors for extracellular matrix components. We report the clinical and molecular features of nine novel BCS families, four of which harbor variants in ZNF469 and five in PRDM5. We also performed a genotype and phenotype-oriented literature overview of all (N=85) reported patients with ZNF469 (N=53) and PRDM5 (N=32) variants. Musculoskeletal findings may be the mean reason for referral, and often raise suspicion of another heritable connective tissue disorder such as kyphoscoliotic EDS, osteogenesis imperfecta or Marfan syndrome, especially when corneal rupture has not yet occurred. Our findings highlight the multisystemic nature of BCS and validate its inclusion in the EDS classification. Importantly, gene panels for heritable connective tissue disorders should include ZNF469 and PRDM5 to allow for timely diagnosis and appropriate preventive measures for this rare condition.

Background: The genetic causes of atrial fibrillation (AF) with slow conduction are unknown. Methods: Eight kindreds with familial AF and slow conduction, including a family affected by early onset AF, heart block and incompletely penetrant non-ischemic cardiomyopathy (NICM) underwent whole exome sequencing. Results: A known pathogenic mutation in the desmin (DES) gene resulting in S13F substitution at a PKC phosphorylation site was identified in all four members of the kindred with early-onset AF and heart block, while only two developed NICM. Higher penetrance of the mutation for AF and heart block prompted the screening for DES modifier(s). A second deleterious mutation in the phosphodiesterase 4D interacting-protein (PDE4DIP) gene resulting in A123T substitution segregated with early onset AF, heart block and the DES mutation. Three additional novel deleterious PDE4DIP mutations were identified in four other unrelated kindreds. Characterization of PDE4DIPA123T in vitro suggested impaired compartmentalization of PKA and PDE4D characterized by reduced colocalization with PDE4D, increased cAMP activation leading to higher PKA phosphorylation of the β2-adrenergic-receptor, and decreased PKA phosphorylation of Desmin in response to isoproterenol stimulation compared to wildtype PDE4DIP. Conclusion: Our findings identify an epistatic interaction between DES and PDE4DIP variants, increasing the penetrance for conduction disease and arrhythmia.

Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/ Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP “strength of evidence categories” can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. Strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in, and that the Bayesian nature of the system is inapparent. We conclude that a points-based system has useful attributes of user friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.

The Matchmaker Exchange (MME) launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. This federated network connects databases holding relevant data, where two or more users are looking for a match for the same gene (two-sided matchmaking). The number of unique genes present across MME has steadily increased; there are currently >13,520 unique genes (~68% of all protein coding genes) connected across MME’s nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The dataset accessible across MME includes more than 120,000 cases from over 12,000 contributors in 98 countries. Discovery of potential new disease-gene relationships occurs daily and international collaborations are moving these connections forward to publication. Expansion of data sharing into routine clinical practice has ensured access to discovery for even more individuals with undiagnosed rare genetic disease. MME supports connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are underway to explore additional approaches to matchmaking where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in years to come.

The Multiple Endocrine Neoplasia type 2 (MEN2) RET proto-oncogene database, originally published in 2008, is a comprehensive repository of all publicly available RET gene variations associated with MEN2 syndromes. The variant-specific genotype/phenotype information, age of earliest reported medullary thyroid carcinoma onset, and relevant references with a brief summary of findings are cataloged. The ACMG/AMP 2015 consensus statement on variant classification was modified specifically for MEN2 syndromes and RET variants using ClinGen sequence variant interpretation working group recommendations and ClinGen expert panel manuscripts, as well as manuscripts from the American Thyroid Association Guidelines Task Force on Medullary Thyroid Carcinoma and other MEN2 RET literature. The classifications for the 166 single unique variants in the MEN2 RET database were reanalyzed using the MEN2 RET specifically modified ACMG/AMP classification guidelines. Applying these guidelines added two new variant classifications to the database (likely benign and likely pathogenic) and resulted in clinically significant classification changes ( e.g. from pathogenic to uncertain) in 16.9% (28/166) of the original variants. Of those clinically significant changes, the highest percentage of changes, 46.4% (13/28), were changes from uncertain to benign or likely benign. The modified ACMG/AMP criteria with MEN2 RET specifications will optimize and standardize RET variant classifications.

This letter is a response to the commentary by Jonson & Do (Johnson and Do 2020) on our paper, entitled “A Vietnamese human genetic variation database” (Vinh et al. 2019). The commentators concerned about two issues: Firstly, the relation of Southeast Asian (SEA) and East Asian (EA) groups to African and European groups; Secondly, the history of migration and settlement in Southeast Asia. Our responses will clarify both concerns from the commentators.