Comprehensive Characterization of Cancer Driver Genes and Mutations.

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

Expanding the genetic and phenotypic landscape of replication factor C complex-related disorders: RFC4 deficiency is linked to a multisystemic disorder.

The precise regulation of DNA replication is vital for cellular division and genomic integrity. Central to this process is the replication factor C (RFC) complex, encompassing five subunits, which loads proliferating cell nuclear antigen onto DNA to facilitate the recruitment of replication and repair proteins and enhance DNA polymerase processivity. While RFC1's role in cerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) is known, the contributions of RFC2-5 subunits on human Mendelian disorders is largely unexplored. Our research links bi-allelic variants in RFC4, encoding a core RFC complex subunit, to an undiagnosed disorder characterized by incoordination and muscle weakness, hearing impairment, and decreased body weight. We discovered across nine affected individuals rare, conserved, predicted pathogenic variants in RFC4, all likely to disrupt the C-terminal domain indispensable for RFC complex formation. Analysis of a previously determined cryo-EM structure of RFC bound to proliferating cell nuclear antigen suggested that the variants disrupt interactions within RFC4 and/or destabilize the RFC complex. Cellular studies using RFC4-deficient HeLa cells and primary fibroblasts demonstrated decreased RFC4 protein, compromised stability of the other RFC complex subunits, and perturbed RFC complex formation. Additionally, functional studies of the RFC4 variants affirmed diminished RFC complex formation, and cell cycle studies suggested perturbation of DNA replication and cell cycle progression. Our integrated approach of combining in silico, structural, cellular, and functional analyses establishes compelling evidence that bi-allelic loss-of-function RFC4 variants contribute to the pathogenesis of this multisystemic disorder. These insights broaden our understanding of the RFC complex and its role in human health and disease.

Systematic analysis of variants escaping nonsense-mediated decay uncovers candidate Mendelian diseases.

Protein-truncating variants (PTVs) near the 3' end of genes may escape nonsense-mediated decay (NMD). PTVs in the NMD-escape region (PTVescs) can cause Mendelian disease but are difficult to interpret given their varying impact on protein function. Previously, PTVesc burden was assessed in an epilepsy cohort, but no large-scale analysis has systematically evaluated these variants in rare disease. We performed a retrospective analysis of 29,031 neurodevelopmental disorder (NDD) parent-offspring trios referred for clinical exome sequencing to identify PTVesc de novo mutations (DNMs). We identified 1,376 PTVesc DNMs and 133 genes that were significantly enriched (binomial p < 0.001). The PTVesc-enriched genes included those with PTVescs previously described to cause dominant Mendelian disease (e.g., SEMA6B, PPM1D, and DAGLA). We annotated ClinVar variants for PTVescs and identified 948 genes with at least one high-confidence pathogenic variant. Twenty-two known Mendelian PTVesc-enriched genes had no prior evidence of PTVesc-associated disease. We found 22 additional PTVesc-enriched genes that are not well established to be associated with Mendelian disease, several of which showed phenotypic similarity between individuals harboring PTVesc variants in the same gene. Four individuals with PTVesc mutations in RAB1A had similar phenotypes including NDD and spasticity. PTVesc mutations in IRF2BP1 were found in two individuals who each had severe immunodeficiency manifesting in NDD. Three individuals with PTVesc mutations in LDB1 all had NDD and multiple congenital anomalies. Using a large-scale, systematic analysis of DNMs, we extend the mutation spectrum for known Mendelian disease-associated genes and identify potentially novel disease-associated genes.

Bi-allelic SNAPC4 variants dysregulate global alternative splicing and lead to neuroregression and progressive spastic paraparesis.

The vast majority of human genes encode multiple isoforms through alternative splicing, and the temporal and spatial regulation of those isoforms is critical for organismal development and function. The spliceosome, which regulates and executes splicing reactions, is primarily composed of small nuclear ribonucleoproteins (snRNPs) that consist of small nuclear RNAs (snRNAs) and protein subunits. snRNA gene transcription is initiated by the snRNA-activating protein complex (SNAPc). Here, we report ten individuals, from eight families, with bi-allelic, deleterious SNAPC4 variants. SNAPC4 encoded one of the five SNAPc subunits that is critical for DNA binding. Most affected individuals presented with delayed motor development and developmental regression after the first year of life, followed by progressive spasticity that led to gait alterations, paraparesis, and oromotor dysfunction. Most individuals had cerebral, cerebellar, or basal ganglia volume loss by brain MRI. In the available cells from affected individuals, SNAPC4 abundance was decreased compared to unaffected controls, suggesting that the bi-allelic variants affect SNAPC4 accumulation. The depletion of SNAPC4 levels in HeLa cell lines via genomic editing led to decreased snRNA expression and global dysregulation of alternative splicing. Analysis of available fibroblasts from affected individuals showed decreased snRNA expression and global dysregulation of alternative splicing compared to unaffected cells. Altogether, these data suggest that these bi-allelic SNAPC4 variants result in loss of function and underlie the neuroregression and progressive spasticity in these affected individuals.

Care4Rare Canada: Outcomes from a decade of network science for rare disease gene discovery.

The past decade has witnessed a rapid evolution in rare disease (RD) research, fueled by the availability of genome-wide (exome and genome) sequencing. In 2011, as this transformative technology was introduced to the research community, the Care4Rare Canada Consortium was launched: initially as FORGE, followed by Care4Rare, and Care4Rare SOLVE. Over what amounted to three eras of diagnosis and discovery, the Care4Rare Consortium used exome sequencing and, more recently, genome and other 'omic technologies to identify the molecular cause of unsolved RDs. We achieved a diagnostic yield of 34% (623/1,806 of participating families), including the discovery of deleterious variants in 121 genes not previously associated with disease, and we continue to study candidate variants in novel genes for 145 families. The Consortium has made significant contributions to RD research, including development of platforms for data collection and sharing and instigating a Canadian network to catalyze functional characterization research of novel genes. The Consortium was instrumental to implementing genome-wide sequencing as a publicly funded test for RD diagnosis in Canada. Despite the successes of the past decade, the challenge of solving all RDs remains enormous, and the work is far from over. We must leverage clinical and 'omic data for secondary use, develop tools and policies to support safe data sharing, continue to explore the utility of new and emerging technologies, and optimize research protocols to delineate complex disease mechanisms. Successful approaches in each of these realms is required to offer diagnostic clarity to all families with RDs.

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.

BACKGROUND: Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest. RESULTS: GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed. CONCLUSIONS: GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

The genomic landscape of Ménière's disease: a path to endolymphatic hydrops.

BACKGROUND: Ménière's disease (MD) is a disorder of the inner ear that causes episodic bouts of severe dizziness, roaring tinnitus, and fluctuating hearing loss. To date, no targeted therapy exists. As such, we have undertaken a large whole genome sequencing study on carefully phenotyped unilateral MD patients with the goal of gene/pathway discovery and a move towards targeted intervention. This study was a retrospective review of patients with a history of Ménière's disease. Genomic DNA, acquired from saliva samples, was purified and subjected to whole genome sequencing. RESULTS: Stringent variant calling, performed on 511 samples passing quality checks, followed by gene-based filtering by recurrence and proximity in molecular interaction networks, led to 481 high priority MD genes. These high priority genes, including MPHOSPH8, MYO18A, TRIOBP, OTOGL, TNC, and MYO6, were previously implicated in hearing loss, balance, and cochlear function, and were significantly enriched in common variant studies of hearing loss. Validation in an independent MD cohort confirmed 82 recurrent genes. Pathway analysis pointed to cell-cell adhesion, extracellular matrix, and cellular energy maintenance as key mediators of MD. Furthermore, the MD-prioritized genes were highly expressed in human inner ear hair cells and dark/vestibular cells, and were differentially expressed in a mouse model of hearing loss. CONCLUSION: By enabling the development of model systems that may lead to targeted therapies and MD screening panels, the genes and variants identified in this study will inform diagnosis and treatment of MD.

High-throughput diagnostic markers for foliar fungal disease resistance and high oleic acid content in groundnut.

BACKGROUND: Foliar diseases namely late leaf spot (LLS) and leaf rust (LR) reduce yield and deteriorate fodder quality in groundnut. Also the high oleic acid content has emerged as one of the most important traits for industries and consumers due to its increased shelf life and health benefits. RESULTS: Genetic mapping combined with pooled sequencing approaches identified candidate resistance genes (LLSR1 and LLSR2 for LLS and LR1 for LR) for both foliar fungal diseases. The LLS-A02 locus housed LLSR1 gene for LLS resistance, while, LLS-A03 housed LLSR2 and LR1 genes for LLS and LR resistance, respectively. A total of 49 KASPs markers were developed from the genomic regions of important disease resistance genes, such as NBS-LRR, purple acid phosphatase, pentatricopeptide repeat-containing protein, and serine/threonine-protein phosphatase. Among the 49 KASP markers, 41 KASPs were validated successfully on a validation panel of contrasting germplasm and breeding lines. Of the 41 validated KASPs, 39 KASPs were designed for rust and LLS resistance, while two KASPs were developed using fatty acid desaturase (FAD) genes to control high oleic acid levels. These validated KASP markers have been extensively used by various groundnut breeding programs across the world which led to development of thousands of advanced breeding lines and few of them also released for commercial cultivation. CONCLUSION: In this study, high-throughput and cost-effective KASP assays were developed, validated and successfully deployed to improve the resistance against foliar fungal diseases and oleic acid in groundnut. So far deployment of allele-specific and KASP diagnostic markers facilitated development and release of two rust- and LLS-resistant varieties and five high-oleic acid groundnut varieties in India. These validated markers provide opportunities for routine deployment in groundnut breeding programs.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing.

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing, the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare diseases. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery, which should, in turn, increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks such as Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, and researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Lethal phenotypes in Mendelian disorders.

PURPOSE: Existing resources that characterize the essentiality status of genes are based on either proliferation assessment in human cell lines, viability evaluation in mouse knockouts, or constraint metrics derived from human population sequencing studies. Several repositories document phenotypic annotations for rare disorders; however, there is a lack of comprehensive reporting on lethal phenotypes. METHODS: We queried Online Mendelian Inheritance in Man for terms related to lethality and classified all Mendelian genes according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. We characterized the genes across these lethality categories, examined the evidence on viability from mouse models and explored how this information could be used for novel gene discovery. RESULTS: We developed the Lethal Phenotypes Portal to showcase this curated catalog of human essential genes. Differences in the mode of inheritance, physiological systems affected, and disease class were found for genes in different lethality categories, as well as discrepancies between the lethal phenotypes observed in mouse and human. CONCLUSION: We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.

Extremophiles in a changing world.

Extremophiles and their products have been a major focus of research interest for over 40 years. Through this period, studies of these organisms have contributed hugely to many aspects of the fundamental and applied sciences, and to wider and more philosophical issues such as the origins of life and astrobiology. Our understanding of the cellular adaptations to extreme conditions (such as acid, temperature, pressure and more), of the mechanisms underpinning the stability of macromolecules, and of the subtleties, complexities and limits of fundamental biochemical processes has been informed by research on extremophiles. Extremophiles have also contributed numerous products and processes to the many fields of biotechnology, from diagnostics to bioremediation. Yet, after 40 years of dedicated research, there remains much to be discovered in this field. Fortunately, extremophiles remain an active and vibrant area of research. In the third decade of the twenty-first century, with decreasing global resources and a steadily increasing human population, the world's attention has turned with increasing urgency to issues of sustainability. These global concerns were encapsulated and formalized by the United Nations with the adoption of the 2030 Agenda for Sustainable Development and the presentation of the seventeen Sustainable Development Goals (SDGs) in 2015. In the run-up to 2030, we consider the contributions that extremophiles have made, and will in the future make, to the SDGs.

The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism.

Autism spectrum disorder (ASD) is a common and highly heritable neurodevelopmental disorder. During the last 15 years, advances in genomic technologies and the availability of increasingly large patient cohorts have greatly expanded our knowledge of the genetic architecture of ASD and its neurobiological mechanisms. Over two hundred risk regions and genes carrying rare de novo and transmitted high-impact variants have been identified. Additionally, common variants with small individual effect size are also important, and a number of loci are now being uncovered. At the same time, these new insights have highlighted ongoing challenges. In this perspective article, we summarize developments in ASD genetic research and address the enormous impact of large-scale genomic initiatives on ASD gene discovery.

Genomic Data Sharing for Novel Mendelian Disease Gene Discovery: The Matchmaker Exchange.

In the last decade, exome and/or genome sequencing has become a common test in the diagnosis of individuals with features of a rare Mendelian disorder. Despite its success, this test leaves the majority of tested individuals undiagnosed. This review describes the Matchmaker Exchange (MME), a federated network established to facilitate the solving of undiagnosed rare-disease cases through data sharing. MME supports genomic matchmaking, the act of connecting two or more parties looking for cases with similar phenotypes and variants in the same candidate genes. An application programming interface currently connects six matchmaker nodes-the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER), GeneMatcher, PhenomeCentral, seqr, MyGene2, and the Initiative on Rare and Undiagnosed Diseases (IRUD) Exchange-resulting in a collective data set spanning more than 150,000 cases from more than 11,000 contributors in 88 countries. Here, we describe the successes and challenges of MME, its individual matchmaking nodes, plans for growing the network, and considerations for future directions.

The Canadian Rare Diseases Models and Mechanisms (RDMM) Network: Connecting Understudied Genes to Model Organisms.

Advances in genomics have transformed our ability to identify the genetic causes of rare diseases (RDs), yet we have a limited understanding of the mechanistic roles of most genes in health and disease. When a novel RD gene is first discovered, there is minimal insight into its biological function, the pathogenic mechanisms of disease-causing variants, and how therapy might be approached. To address this gap, the Canadian Rare Diseases Models and Mechanisms (RDMM) Network was established to connect clinicians discovering new disease genes with Canadian scientists able to study equivalent genes and pathways in model organisms (MOs). The Network is built around a registry of more than 500 Canadian MO scientists, representing expertise for over 7,500 human genes. RDMM uses a committee process to identify and evaluate clinician-MO scientist collaborations and approve 25,000 Canadian dollars in catalyst funding. To date, we have made 85 clinician-MO scientist connections and funded 105 projects. These collaborations help confirm variant pathogenicity and unravel the molecular mechanisms of RD, and also test novel therapies and lead to long-term collaborations. To expand the impact and reach of this model, we made the RDMM Registry open-source, portable, and customizable, and we freely share our committee structures and processes. We are currently working with emerging networks in Europe, Australia, and Japan to link international RDMM networks and registries and enable matches across borders. We will continue to create meaningful collaborations, generate knowledge, and advance RD research locally and globally for the benefit of patients and families living with RD.

The genetics of neuropsychiatric diseases: looking in and beyond the exome.

Next-generation sequencing, which allows genome-wide detection of rare and de novo mutations, is transforming neuropsychiatric disease genetics through identifying on an unprecedented scale genes and protein-coding mutations that confer risk. Although understanding how regulatory variants influence risk remains a challenge, we are likely transitioning into a phase of neuropsychiatric disease genetics in which the rate-limiting step may no longer be gene discovery. Instead, the future will concentrate more on the biological and clinical translation of the torrent of specific risk mutations identified through next-generation sequencing. Here, we review the recent progress that resulted specifically from exome sequencing and emphasize the need for rigorous statistical evaluation of the expanding data sets, as well as expanded functional analysis of implicated proteins and mutations. Then, we introduce some of the expected opportunities and challenges investigators face when moving beyond the exome. Finally, we briefly highlight the challenge of deriving translational benefit from the progress in genetics.

PatientMatcher: A customizable Python-based open-source tool for matching undiagnosed rare disease patients via the Matchmaker Exchange network.

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype-phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the Matchmaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by "matching" the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open-source Python and MongoDB-based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical routine in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom-fit similarity score algorithm and adjustable matching results notifications.

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.

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.

seqr: A web-based analysis and collaboration tool for rare disease genomics.

Exome and genome sequencing have become the tools of choice for rare disease diagnosis, leading to large amounts of data available for analyses. To identify causal variants in these datasets, powerful filtering and decision support tools that can be efficiently used by clinicians and researchers are required. To address this need, we developed seqr - an open-source, web-based tool for family-based monogenic disease analysis that allows researchers to work collaboratively to search and annotate genomic callsets. To date, seqr is being used in several research pipelines and one clinical diagnostic lab. In our own experience through the Broad Institute Center for Mendelian Genomics, seqr has enabled analyses of over 10,000 families, supporting the diagnosis of more than 3,800 individuals with rare disease and discovery of over 300 novel disease genes. Here, we describe a framework for genomic analysis in rare disease that leverages seqr's capabilities for variant filtration, annotation, and causal variant identification, as well as support for research collaboration and data sharing. The seqr platform is available as open source software, allowing low-cost participation in rare disease research, and a community effort to support diagnosis and gene discovery in rare disease.

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.