Clinical validity assessment of genes frequently tested on intellectual disability/autism sequencing panels.

PURPOSE: Neurodevelopmental disorders (NDDs), such as intellectual disability (ID) and autism spectrum disorder (ASD), exhibit genetic and phenotypic heterogeneity, making them difficult to differentiate without a molecular diagnosis. The Clinical Genome Resource Intellectual Disability/Autism Gene Curation Expert Panel (GCEP) uses systematic curation to distinguish ID/ASD genes that are appropriate for clinical testing (ie, with substantial evidence supporting their relationship to disease) from those that are not. METHODS: Using the Clinical Genome Resource gene-disease validity curation framework, the ID/Autism GCEP classified genes frequently included on clinical ID/ASD testing panels as Definitive, Strong, Moderate, Limited, Disputed, Refuted, or No Known Disease Relationship. RESULTS: As of September 2021, 156 gene-disease pairs have been evaluated. Although most (75%) were determined to have definitive roles in NDDs, 22 (14%) genes evaluated had either Limited or Disputed evidence. Such genes are currently not recommended for use in clinical testing owing to the limited ability to assess the effect of identified variants. CONCLUSION: Our understanding of gene-disease relationships evolves over time; new relationships are discovered and previously-held conclusions may be questioned. Without periodic re-examination, inaccurate gene-disease claims may be perpetuated. The ID/Autism GCEP will continue to evaluate these claims to improve diagnosis and clinical care for NDDs.

Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource.

With advances in genomic sequencing technology, the number of reported gene-disease relationships has rapidly expanded. However, the evidence supporting these claims varies widely, confounding accurate evaluation of genomic variation in a clinical setting. Despite the critical need to differentiate clinically valid relationships from less well-substantiated relationships, standard guidelines for such evaluation do not currently exist. The NIH-funded Clinical Genome Resource (ClinGen) has developed a framework to define and evaluate the clinical validity of gene-disease pairs across a variety of Mendelian disorders. In this manuscript we describe a proposed framework to evaluate relevant genetic and experimental evidence supporting or contradicting a gene-disease relationship and the subsequent validation of this framework using a set of representative gene-disease pairs. The framework provides a semiquantitative measurement for the strength of evidence of a gene-disease relationship that correlates to a qualitative classification: "Definitive," "Strong," "Moderate," "Limited," "No Reported Evidence," or "Conflicting Evidence." Within the ClinGen structure, classifications derived with this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts. Detailed guidance for utilizing this framework and access to the curation interface is available on our website. This evidence-based, systematic method to assess the strength of gene-disease relationships will facilitate more knowledgeable utilization of genomic variants in clinical and research settings.

A homozygous missense variant in the YG box domain in an individual with severe spinal muscular atrophy: a case report and variant characterization.

The vast majority of severe (Type 0) spinal muscular atrophy (SMA) cases are caused by homozygous deletions of survival motor neuron 1 (SMN1). We report a case in which the patient has two copies of SMN1 but clinically presents as Type 0 SMA. The patient is an African American male carrying a homozygous maternally inherited missense variant (c.796T>C) in a cis-oriented SMN1 duplication on one chromosome and an SMN1 deletion on the other chromosome (genotype: 2*+0). Initial extensive genetic workups including exome sequencing were negative. Deletion analysis used in the initial testing for SMA also failed to detect SMA as the patient has two copies of SMN1. Because of high clinical suspicion, SMA diagnosis was finally confirmed based on full-length SMN1 sequencing. The patient was initially treated with risdiplam and later gene therapy with onasemnogene abeparvovec at 5 months without complications. The patient's muscular weakness has stabilized with mild improvement. The patient is now 28 months old and remains stable and diffusely weak, with stable respiratory ventilatory support. This case highlights challenges in the diagnosis of SMA with a non-deletion genotype and provides a clinical example demonstrating that disruption of functional SMN protein polymerization through an amino acid change in the YG-box domain represents a little known but important pathogenic mechanism for SMA. Clinicians need to be mindful about the limitations of the current diagnostic approach for SMA in detecting non-deletion genotypes.

The HGNC Database in 2008: a resource for the human genome.

The HUGO Gene Nomenclature Committee (HGNC) aims to assign a unique and ideally meaningful name and symbol to every human gene. The HGNC database currently comprises over 24 000 public records containing approved human gene nomenclature and associated gene information. Following our recent relocation to the European Bioinformatics Institute our homepage can now be found at http://www.genenames.org, with direct links to the searchable HGNC database and other related database resources, such as the HCOP orthology search tool and manually curated gene family webpages.

The human gamma-glutamyltransferase gene family.

Assays for gamma-glutamyl transferase (GGT1, EC 2.3.2.2) activity in blood are widely used in a clinical setting to measure tissue damage. The well-characterized GGT1 is an extracellular enzyme that is anchored to the plasma membrane of cells. There, it hydrolyzes and transfers gamma-glutamyl moieties from glutathione and other gamma-glutamyl compounds to acceptors. As such, it has a critical function in the metabolism of glutathione and in the conversion of the leukotriene LTC4 to LTD4. GGT deficiency in man is rare and for the few patients reported to date, mutations in GGT1 have not been described. These patients do secrete glutathione in urine and fail to metabolize LTC4. Earlier pre-genome investigations had indicated that besides GGT1, the human genome contains additional related genes or sequences. These sequences were given multiple different names, leading to inconsistencies and confusion. Here we systematically evaluated all human sequences related to GGT1 using genomic and cDNA database searches and identified thirteen genes belonging to the extended GGT family, of which at least six appear to be active. In collaboration with the HUGO Gene Nomenclature Committee (HGNC) we have designated possible active genes with nucleotide or amino acid sequence similarity to GGT1, as GGT5 (formerly GGL, GGTLA1/GGT-rel), GGT6 (formerly rat ggt6 homologue) and GGT7 (formerly GGTL3, GGT4). Two loci have the potential to encode only the light chain portion of GGT and have now been designated GGTLC1 (formerly GGTL6, GGTLA4) and GGTLC2. Of the five full-length genes, three lack of significant nucleotide sequence homology but have significant (GGT5, GGT7) or very limited (GGT6) amino acid similarity to GGT1 and belong to separate families. GGT6 and GGT7 have not yet been described, raising the possibility that leukotriene synthesis, glutathione metabolism or gamma-glutamyl transfer is regulated by their, as of yet uncharacterized, enzymatic activities. In view of the widespread clinical use of assays that measure gamma-glutamyl transfer activity, this would appear to be of significant interest.

The HUGO Gene Nomenclature Database, 2006 updates.

The HUGO Gene Nomenclature Committee (HGNC) aims to give every human gene a unique and ideally meaningful name and symbol. The HGNC database, previously known as Genew, contains over 22,000 public records with approved human gene nomenclature and associated information. The database has undergone major improvements throughout the last year, is publicly available for online searching at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl and has a new custom downloads interface at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/gdlw.pl.

GigaDB: promoting data dissemination and reproducibility.

Often papers are published where the underlying data supporting the research are not made available because of the limitations of making such large data sets publicly and permanently accessible. Even if the raw data are deposited in public archives, the essential analysis intermediaries, scripts or software are frequently not made available, meaning the science is not reproducible. The GigaScience journal is attempting to address this issue with the associated data storage and dissemination portal, the GigaScience database (GigaDB). Here we present the current version of GigaDB and reveal plans for the next generation of improvements. However, most importantly, we are soliciting responses from you, the users, to ensure that future developments are focused on the data storage and dissemination issues that still need resolving. Database URL: http://www.gigadb.org.

Online resources for genomic structural variation.

Genomic structural variation (SV) can be thought of on a continuum from a single base pair insertion/deletion (INDEL) to large megabase-scale rearrangements involving insertions, deletions, duplications, inversions, or translocations of whole chromosomes or chromosome arms. These variants can occur in coding or noncoding DNA, they can be inherited or arise sporadically in the germline or somatic cells. Many of these events are segregating in the population and can be considered common alleles while others are new alleles and thus rare events. All species studied to date harbor structural variants and these may be benign, contributing to phenotypes such as sensory perception and immunity, or pathogenic resulting in genomic disorders including DiGeorge/velocardiofacial, Smith-Margenis, Williams-Beuren, and Prader-Willi syndromes. As structural variants are identified, validated, and their significance, origin, and prevalence are elucidated, it is of critical importance that these data be collected and collated in a way that can be easily accessed and analyzed. This chapter describes current structural variation online resources (see Fig. 1 and Table 1), highlights the challenges in capturing, storing, and displaying SV data, and discusses how dbVar and DGVa, the genomic structural variation databases developed at NCBI and EBI, respectively, were designed to address these issues.

GigaDB: announcing the GigaScience database.

With the launch of GigaScience journal, here we provide insight into the accompanying database GigaDB, which allows the integration of manuscript publication with supporting data and tools. Reinforcing and upholding GigaScience's goals to promote open-data and reproducibility of research, GigaDB also aims to provide a home, when a suitable public repository does not exist, for the supporting data or tools featured in the journal and beyond.