Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for 'actionable' genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. The combination of deep phenotyping data with functional and computational tests and with outcomes in population cohorts, enabled us to: (i) identify the genetic aetiology of divergent clinical phenotypes of MCT8 deficiency with genotype-phenotype relationships present across survival and 24 out of 32 disease features; (ii) demonstrate a mild phenocopy in ~400,000 individuals with common genetic variants in MCT8; (iii) assess therapeutic effectiveness, which did not differ among LoF-categories; (iv) advance structural insights in normal and mutated MCT8 by delineating seven critical functional domains; (v) create a pathogenicity-severity MCT8 variant classifier that accurately predicted pathogenicity (AUC:0.91) and severity (AUC:0.86) for 8151 variants. Our information-dense mapping provides a generalizable approach to advance multiple dimensions of rare genetic disorders.

• MeSH
• deep learning * MeSH
• dítě MeSH
• dospělí MeSH
• fenotyp * MeSH
• genetická variace MeSH
• genetické asociační studie MeSH
• genomika metody   MeSH
• hormony štítné žlázy metabolismus   genetika   MeSH
• lidé MeSH
• mentální retardace vázaná na chromozom X genetika   metabolismus   MeSH
• mladiství MeSH
• mutace ztráty funkce MeSH
• předškolní dítě MeSH
• přenašeče monokarboxylových kyselin * genetika   metabolismus   MeSH
• stupeň závažnosti nemoci MeSH
• svalová atrofie genetika   metabolismus   patologie   MeSH
• svalová hypotonie genetika   metabolismus   MeSH
• symportéry * genetika   metabolismus   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• hormony štítné žlázy MeSH
• přenašeče monokarboxylových kyselin * MeSH
• SLC16A2 protein, human MeSH Prohlížeč
• symportéry * MeSH

Up to 80% of rare disease patients remain undiagnosed after genomic sequencing1, with many probably involving pathogenic variants in yet to be discovered disease-gene associations. To search for such associations, we developed a rare variant gene burden analytical framework for Mendelian diseases, and applied it to protein-coding variants from whole-genome sequencing of 34,851 cases and their family members recruited to the 100,000 Genomes Project2. A total of 141 new associations were identified, including five for which independent disease-gene evidence was recently published. Following in silico triaging and clinical expert review, 69 associations were prioritized, of which 30 could be linked to existing experimental evidence. The five associations with strongest overall genetic and experimental evidence were monogenic diabetes with the known β cell regulator3,4 UNC13A, schizophrenia with GPR17, epilepsy with RBFOX3, Charcot-Marie-Tooth disease with ARPC3 and anterior segment ocular abnormalities with POMK. Further confirmation of these and other associations could lead to numerous diagnoses, highlighting the clinical impact of large-scale statistical approaches to rare disease-gene association discovery.

• Publikační typ
• časopisecké články MeSH

Genetic diagnosis of rare diseases requires accurate identification and interpretation of genomic variants. Clinical and molecular scientists from 37 expert centers across Europe created the Solve-Rare Diseases Consortium (Solve-RD) resource, encompassing clinical, pedigree and genomic rare-disease data (94.5% exomes, 5.5% genomes), and performed systematic reanalysis for 6,447 individuals (3,592 male, 2,855 female) with previously undiagnosed rare diseases from 6,004 families. We established a collaborative, two-level expert review infrastructure that allowed a genetic diagnosis in 506 (8.4%) families. Of 552 disease-causing variants identified, 464 (84.1%) were single-nucleotide variants or short insertions/deletions. These variants were either located in recently published novel disease genes (n = 67), recently reclassified in ClinVar (n = 187) or reclassified by consensus expert decision within Solve-RD (n = 210). Bespoke bioinformatics analyses identified the remaining 15.9% of causative variants (n = 88). Ad hoc expert review, parallel to the systematic reanalysis, diagnosed 249 (4.1%) additional families for an overall diagnostic yield of 12.6%. The infrastructure and collaborative networks set up by Solve-RD can serve as a blueprint for future further scalable international efforts. The resource is open to the global rare-disease community, allowing phenotype, variant and gene queries, as well as genome-wide discoveries.

• MeSH
• databáze genetické MeSH
• exom genetika   MeSH
• genom lidský genetika   MeSH
• genomika * metody   MeSH
• lidé MeSH
• rodokmen MeSH
• výpočetní biologie metody   MeSH
• vzácné nemoci * genetika   diagnóza   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH

Identification of genomic variability in population plays an important role in the clinical diagnostics of human genetic diseases. Thanks to rapid technological development in the field of massive parallel sequencing technologies, also known as next-generation sequencing (NGS), complex genomic analyses are now easier and cheaper than ever before, which consequently leads to more effective utilization of these techniques in clinical practice. However, interpretation of data from NGS is still challenging due to several issues caused by natural variability of DNA sequences in human populations. Therefore, development and realization of projects focused on description of genetic variability of local population (often called "national or digital genome") with a NGS technique is one of the best approaches to address this problem. The next step of the process is to share such data via publicly available databases. Such databases are important for the interpretation of variants with unknown significance or (likely) pathogenic variants in rare diseases or cancer or generally for identification of pathological variants in a patient's genome. In this paper, we have compiled an overview of published results of local genome sequencing projects from United Kingdom and Europe together with future plans and perspectives for newly announced ones.

• Klíčová slova
• United Kingdom, genetic variability Europe, national genome project, population, whole-genome sequencing,
• MeSH
• genomika metody   MeSH
• lidé MeSH
• nádory * genetika   MeSH
• sekvenování celého genomu MeSH
• vysoce účinné nukleotidové sekvenování * metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Geografické názvy
• Spojené království MeSH

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: When germline mutations are suspected as causal in cancer, patient DNA may be sequenced to detect variants in relevant genes. If a particular mutation has not been reported in reliable family studies, genetic counselors are facing a dilemma of appropriately informing patients. Many sequencing facilities provide an interpretation of the findings based on the available sequence databases or on prediction tools that are curated from bioinformatics and mechanistic datasets. The counseling dilemma is exacerbated if the pedigree data are not informative but the in silico predictions suggest pathogenicity. METHODS: We present here a real world example of the c.256G > A CDKN2A variant, which was detected in one melanoma patient where two siblings were diagnosed with melanoma in situ. We investigated a detailed family history of the affected siblings in order to survey probability of the cancer risks within the context to this mutation. RESULTS: This c.256G > A CDKN2A variant was detected in one of the brothers and in the melanoma-free mother while the other brother in the family tested negative. The variant had been previously described in one patient from a melanoma family. In the family under investigation, the mother's 16 first-and second-degree relatives had survived past the median onset age for melanoma and none presented melanoma. We tested the variant using multiple bioinformatic tools that all predicted deleteriousness of the variant. The genetic counseling report to the melanoma patient stated that the CDKN2A variant was 'likely pathogenic' and the disease was defined as 'likely hereditary melanoma'. CONCLUSIONS: The pedigree data showed at the most a low penetrance variant, which, if taken into consideration, might have altered the provided diagnosis. When dealing with 'practically' unknown variants the counselors would be advised to incorporate a detailed family history rather than basing predictions on functionality provided by sequencing facilities.

• Klíčová slova
• Deleteriousness, Functionality, Genetic counseling, Melanoma suppressor gene,
• Publikační typ
• časopisecké články MeSH

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

• Klíčová slova
• Faces, data protection, data sharing, patient information, phenotyping, rare disease,
• Publikační typ
• časopisecké články MeSH

Charcot-Marie-Tooth disease (CMT) is an umbrella term for inherited neuropathies affecting an estimated one in 2,500 people. Over 120 CMT and related genes have been identified and clinical gene panels often contain more than 100 genes. Such a large genomic space will invariantly yield variants of uncertain clinical significance (VUS) in nearly any person tested. This rise in number of VUS creates major challenges for genetic counseling. Additionally, fewer individual variants in known genes are being published as the academic merit is decreasing, and most testing now happens in clinical laboratories, which typically do not correlate their variants with clinical phenotypes. For CMT, we aim to encourage and facilitate the global capture of variant data to gain a large collection of alleles in CMT genes, ideally in conjunction with phenotypic information. The Inherited Neuropathy Variant Browser provides user-friendly open access to currently reported variation in CMT genes. Geneticists, physicians, and genetic counselors can enter variants detected by clinical tests or in research studies in addition to genetic variation gathered from published literature, which are then submitted to ClinVar biannually. Active participation of the broader CMT community will provide an advance over existing resources for interpretation of CMT genetic variation.

• Klíčová slova
• VUS, database, inherited neuropathy, variants of unknown significance,
• MeSH
• alely MeSH
• charakteristiky bydlení * MeSH
• Charcotova-Marieova-Toothova nemoc genetika   MeSH
• genetická variace * MeSH
• internet * MeSH
• lidé MeSH
• uživatelské rozhraní počítače MeSH
• vyhledávač MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

Provision of a molecularly confirmed diagnosis in a timely manner for children and adults with rare genetic diseases shortens their "diagnostic odyssey," improves disease management, and fosters genetic counseling with respect to recurrence risks while assuring reproductive choices. In a general clinical genetics setting, the current diagnostic rate is approximately 50%, but for those who do not receive a molecular diagnosis after the initial genetics evaluation, that rate is much lower. Diagnostic success for these more challenging affected individuals depends to a large extent on progress in the discovery of genes associated with, and mechanisms underlying, rare diseases. Thus, continued research is required for moving toward a more complete catalog of disease-related genes and variants. The International Rare Diseases Research Consortium (IRDiRC) was established in 2011 to bring together researchers and organizations invested in rare disease research to develop a means of achieving molecular diagnosis for all rare diseases. Here, we review the current and future bottlenecks to gene discovery and suggest strategies for enabling progress in this regard. Each successful discovery will define potential diagnostic, preventive, and therapeutic opportunities for the corresponding rare disease, enabling precision medicine for this patient population.

• Klíčová slova
• IRDiRC, Matchmaker Exchange, disease modeling, gene discovery, genome sequencing, ontologies, rare diseases, solving the unsolved, transcriptome sequencing,
• MeSH
• databáze faktografické MeSH
• exom MeSH
• genom lidský MeSH
• lidé MeSH
• mezinárodní spolupráce * MeSH
• vzácné nemoci diagnóza   genetika   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH