The impact of GeneMatcher on international data sharing and collaboration.

GeneMatcher (genematcher.org) is a tool designed to connect individuals with an interest in the same gene. Now used around the world to create collaborations and generate the evidence needed to support novel disease gene identification, GeneMatcher is a founding member of the Matchmaker Exchange (MME; matchmakerexchange.org) and strongest possible advocate for global data sharing including those in resource-limited environments. As of October 1, 2021, there are 12,531 submitters from 94 countries who have submitted 58,134 submissions with 13,498 unique genes in the database. Among these genes, 8970 (64%) have matched at least once and the total number of matches is 378,806, growing by about 10,000 per month. GeneMatcher submitters increase by 80-120 each month and submissions grow by >800 per month, while unique genes and gene matches continue to grow steadily at rate of about 80 per month. The number of genes without a match peaked at 4371 in February of 2019 and despite the increase in the number of new submissions, the number of unique genes without a match continues to slowly decline, currently standing at 4,016. All submissions in GeneMatcher are available for matching across the MME.

Discovery of over 200 new and expanded genetic conditions using GeneMatcher.

GeneMatcher is a platform through which various stakeholders can connect with others interested in candidate gene findings. GeneDx, a diagnostic laboratory, has utilized GeneMatcher over the last seven years to successfully facilitate connections between clinicians and researchers, generating fruitful research collaborations. Our ultimate goal in reporting candidate gene findings is to amass sufficient evidence to establish novel disease-gene relationships (DGRs), thus providing diagnostic answers to families and clinicians. Our database of over 300,000 clinical exomes has been a major driver of DGR discovery. Our laboratory accounts for over 20% of total GeneMatcher submissions. Largely fueled by GeneMatcher matches, we have published over 200 articles involving new DGRs or expanded phenotypes for known disease-causing genes in the past three years. These endeavors require commitments to sharing data and dedicating resources to investigate potential matches. Ultimately, GeneMatcher enables collaboration on a broad scale: we are grateful to the clinicians, researchers, patients, and caregivers who have partnered with us to accelerate the pace of DGR discovery. GeneMatcher opens the door to new partnerships, new discoveries, and families finding answers that otherwise may not have been possible.

A clinical laboratory's experience using GeneMatcher-Building stronger gene-disease relationships.

The use of whole-genome sequencing (WGS) has accelerated the pace of gene discovery and highlighted the need for open and collaborative data sharing in the search for novel disease genes and variants. GeneMatcher (GM) is designed to facilitate connections between researchers, clinicians, health-care providers, and others to help in the identification of additional patients with variants in the same candidate disease genes. The Illumina Clinical Services Laboratory offers a WGS test for patients with suspected rare and undiagnosed genetic disease  and regularly submits potential candidate genes to GM to strengthen gene-disease relationships. We describe our experience with GM, including criteria for evaluation of candidate genes, and our workflow for the submission and review process. We have made 69 submissions, 36 of which are currently active. Ten percent of submissions have resulted in publications, with an additional 14 submissions part of ongoing collaborations and expected to result in a publication.

Diagnostic testing laboratories are valuable partners for disease gene discovery: 5-year experience with GeneMatcher.

Although the rates of disease gene discovery have steadily increased with the expanding use of genome and exome sequencing by clinical and research laboratories, only ~16% of genes in the genome have confirmed disease associations. Here we describe our clinical laboratory's experience utilizing GeneMatcher, an online portal designed to promote disease gene discovery and data sharing. Since 2016, we submitted 246 candidates from 243 unique genes to GeneMatcher, of which 111 (45%) are now clinically characterized. Submissions meeting our candidate gene-reporting criteria based on a scoring system using patient and molecular-weighted evidence were significantly more likely to be characterized as of October 2021 versus genes that did not meet our clinical-reporting criteria (p = 0.025). We reported relevant findings related to these newly characterized gene-disease associations in 477 probands. In 218 (46%) instances, we issued reclassifications after an initial negative or candidate gene (uncertain) report. We coauthored 104 publications delineating gene-disease relationships, including descriptions of new associations (60%), additional supportive evidence (13%), subsequent descriptive cohorts (23%), and phenotypic expansions (4%). Clinical laboratories are pivotal for disease gene discovery efforts and can screen phenotypes based on genotype matches, contact clinicians of relevant cases, and issue proactive reclassification reports.

Outcome of over 1500 matches through the Matchmaker Exchange for rare disease gene discovery: The 2-year experience of Care4Rare Canada.

PURPOSE: Matchmaking has emerged as a useful strategy for building evidence toward causality of novel disease genes in patients with undiagnosed rare diseases. The Matchmaker Exchange (MME) is a collaborative initiative that facilitates international data sharing for matchmaking purposes; however, data on user experience is limited. METHODS: Patients enrolled as part of the Finding of Rare Disease Genes in Canada (FORGE) and Care4Rare Canada research programs had their exome sequencing data reanalyzed by a multidisciplinary research team over a 2-year period. Compelling variants in genes not previously associated with a human phenotype were submitted through the MME node PhenomeCentral, and outcomes were collected. RESULTS: In this study, 194 novel candidate genes were submitted to the MME, resulting in 1514 matches, and 15% of the genes submitted resulted in collaborations. Most submissions resulted in at least 1 match, and most matches were with GeneMatcher (82%), where additional email exchange was required to evaluate the match because of the lack of phenotypic or inheritance information. CONCLUSION: Matchmaking through the MME is an effective way to investigate novel candidate genes; however, it is a labor-intensive process. Engagement from the community to contribute phenotypic, genotypic, and inheritance data will ensure that matchmaking continues to be a useful approach in the future.

Phenotypic spectrum of the recurrent TRPM3 p.(Val837Met) substitution in seven individuals with global developmental delay and hypotonia.

TRPM3 encodes a transient receptor potential cation channel of the melastatin family, expressed in the central nervous system and in peripheral sensory neurons of the dorsal root ganglia. The recurrent substitution in TRPM3: c.2509G>A, p.(Val837Met) has been associated with syndromic intellectual disability and seizures. In this report, we present the clinical and molecular features of seven previously unreported individuals, identified by exome sequencing, with the recurrent p.(Val837Met) variant and global developmental delay. Other shared clinical features included congenital hypotonia, dysmorphic facial features (broad forehead, deep-set eyes, and down turned mouth), exotropia, and musculoskeletal issues (hip dysplasia, hip dislocation, scoliosis). Seizures were observed in two of seven individuals (febrile seizure in one and generalized tonic-clonic seizures with atonic drops in another), and epileptiform activity was observed in an additional two individuals. This report extends the number of affected individuals to 16 who are heterozygous for the de novo recurrent substitution p.(Val837Met). In contrast with the initial report, epilepsy was not a mandatory feature observed in this series. TRPM3 pathogenic variation should be considered in individuals with global developmental delays, moderate-severe intellectual disability with, or without, childhood-onset epilepsy.

Next-generation sequencing and the evolution of data sharing.

Disease gene identification often relies on identifying multiple affected individuals with similar phenotypes and candidate variants in the same gene. Phenotypic and genomic data sharing tools have facilitated connections that led to novel disease gene discoveries and better characterization and recognition of rare diseases. Additionally, data sharing has evolved. From gene-based matches to variant-level information with increasing use of phenotypic information. We expect that these initiatives will continue to expand in the future affording clinicians, researchers, and most importantly, patients and their families faster and more comprehensive answers.

2.5 years' experience of GeneMatcher data-sharing: a powerful tool for identifying new genes responsible for rare diseases.

PURPOSE: Exome sequencing (ES) powerfully identifies the molecular bases of heterogeneous conditions such as intellectual disability and/or multiple congenital anomalies (ID/MCA). Current ES analysis, combining diagnosis analysis restricted to disease-causing genes reported in OMIM database and subsequent research investigation extended to other genes, indicated causal and candidate genes around 40% and 10%. Nonconclusive results are frequent in such ultrarare conditions that recurrence and genotype-phenotype correlations are limited. International data-sharing permits the gathering of additional patients carrying variants in the same gene to draw definitive conclusions on their implication as disease causing. Several web-based tools have been developed and grouped in Matchmaker Exchange. In this study, we report our current experience as a regional center that has implemented ES as a first-line diagnostic test since 2013, working with a research laboratory devoted to disease gene identification. METHODS: We used GeneMatcher over 2.5 years to share 71 novel candidate genes identified by ES. RESULTS: Matches occurred in 60/71 candidate genes allowing to confirm the implication of 39% of matched genes as causal and to rule out 6% of them. CONCLUSION: The introduction of user-friendly gene-matching tools, such as GeneMatcher, appeared to be an essential step for the rapid identification of novel disease genes responsible for ID/MCA.

GeneMatcher aids in the identification of a new malformation syndrome with intellectual disability, unique facial dysmorphisms, and skeletal and connective tissue abnormalities caused by de novo variants in HNRNPK.

We report a new syndrome due to loss-of-function variants in the heterogeneous nuclear ribonucleoprotein K gene (HNRNPK). We describe two probands: one with a de novo frameshift (NM_002140.3: c.953+1dup), and the other with a de novo splice donor site variant (NM_002140.3: c.257G>A). Both probands have intellectual disability, a shared unique craniofacial phenotype, and connective tissue and skeletal abnormalities. The identification of this syndrome was made possible by a new online tool, GeneMatcher, which facilitates connections between clinicians and researchers based on shared interest in candidate genes. This report demonstrates that new Web-based approaches can be effective in helping investigators solve exome sequencing projects, and also highlights the newer paradigm of "reverse phenotyping," where characterization of syndromic features follows the identification of genetic variants.

A holistic approach to maximise diagnostic output in trio exome sequencing.

Introduction: Rare genetic diseases are a major cause for severe illness in children. Whole exome sequencing (WES) is a powerful tool for identifying genetic causes of rare diseases. For a better and faster assessment of the vast number of variants that are identified in the index patient in WES, parental sequencing can be applied ("trio WES"). Methods: We assessed the diagnostic rate of routine trio WES including analysis of copy number variants in 224 pediatric patients during an evaluation period of three years. Results: Trio WES provided a diagnosis in 67 (30%) of all 224 analysed children. The turnaround time of trio WES analysis has been reduced significantly from 41 days in 2019 to 23 days in 2021. Copy number variants could be identified to be causative in 10 cases (4.5%), underlying the importance of copy number variant analysis. Variants in three genes which were previously not associated with a clinical condition (GAD1, TMEM222 and ZNFX1) were identified using the matching tool GeneMatcher and were part of the first description of a new syndrome. Discussion: Trio WES has proven to have a high diagnostic yield and to shorten the process of identifying the correct diagnosis in paediatric patients. Re-evaluation of all 224 trio WES 1-3 years after initial analysis did not establish new diagnoses. Initiating (trio) WES as a first-tier diagnostics including copy number variant detection should be considered as early as possible, especially for children treated in ICU, if a monogenetic disease is suspected.

PhenoDB, GeneMatcher and VariantMatcher, tools for analysis and sharing of sequence data.

BACKGROUND: With the advent of whole exome (ES) and genome sequencing (GS) as tools for disease gene discovery, rare variant filtering, prioritization and data sharing have become essential components of the search for disease genes and variants potentially contributing to disease phenotypes. The computational storage, data manipulation, and bioinformatic interpretation of thousands to millions of variants identified in ES and GS, respectively, is a challenging task. To aid in that endeavor, we constructed PhenoDB, GeneMatcher and VariantMatcher. RESULTS: PhenoDB is an accessible, freely available, web-based platform that allows users to store, share, analyze and interpret their patients' phenotypes and variants from ES/GS data. GeneMatcher is accessible to all stakeholders as a web-based tool developed to connect individuals (researchers, clinicians, health care providers and patients) around the globe with interest in the same gene(s), variant(s) or phenotype(s). Finally, VariantMatcher was developed to enable public sharing of variant-level data and phenotypic information from individuals sequenced as part of multiple disease gene discovery projects. Here we provide updates on PhenoDB and GeneMatcher applications and implementation and introduce VariantMatcher. CONCLUSION: Each of these tools has facilitated worldwide data sharing and data analysis and improved our ability to connect genes to phenotypic traits. Further development of these platforms will expand variant analysis, interpretation, novel disease-gene discovery and facilitate functional annotation of the human genome for clinical genomics implementation and the precision medicine initiative.

Matchmaker Exchange.

In well over half of the individuals with rare disease who undergo clinical or research next-generation sequencing, the responsible gene cannot be determined. Some reasons for this relatively low yield include unappreciated phenotypic heterogeneity; locus heterogeneity; somatic and germline mosaicism; variants of uncertain functional significance; technically inaccessible areas of the genome; incorrect mode of inheritance investigated; and inadequate communication between clinicians and basic scientists with knowledge of particular genes, proteins, or biological systems. To facilitate such communication and improve the search for patients or model organisms with similar phenotypes and variants in specific candidate genes, we have developed the Matchmaker Exchange (MME). MME was created to establish a federated network connecting databases of genomic and phenotypic data using a common application programming interface (API). To date, seven databases can exchange data using the API (GeneMatcher, PhenomeCentral, DECIPHER, MyGene2, matchbox, Australian Genomics Health Alliance Patient Archive, and Monarch Initiative; the latter included for model organism matching). This article guides usage of the MME for rare disease gene discovery. © 2017 by John Wiley & Sons, Inc.