Data sharing in the undiagnosed diseases network

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level 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 the years to come.

Shortcutting the diagnostic odyssey: the multidisciplinary Program for Undiagnosed Rare Diseases in adults (UD-PrOZA)

Background

In order to facilitate the diagnostic process for adult patients suffering from a rare disease, the Undiagnosed Disease Program (UD-PrOZA) was founded in 2015 at the Ghent University Hospital in Belgium. In this study we report the five-year results of our multidisciplinary approach in rare disease diagnostics.

Methods

Patients referred by a healthcare provider, in which an underlying rare disease is likely, qualify for a UD-PrOZA evaluation. UD-PrOZA uses a multidisciplinary clinical approach combined with state-of-the-art genomic technologies in close collaboration with research facilities to diagnose patients.

Results

Between 2015 and 2020, 692 patients (94% adults) were referred of which 329 (48%) were accepted for evaluation. In 18% (60 of 329) of the cases a definite diagnosis was made. 88% (53 of 60) of the established diagnoses had a genetic origin. 65% (39 of 60) of the genetic diagnoses were made through whole exome sequencing (WES). The mean time interval between symptom-onset and diagnosis was 19 years. Key observations included novel genotype–phenotype correlations, new variants in known disease genes and the identification of three new disease genes. In 13% (7 of 53), identifying the molecular cause was associated with therapeutic recommendations and in 88% (53 of 60), gene specific genetic counseling was made possible. Actionable secondary findings were reported in 7% (12 of 177) of the patients in which WES was performed.

Conclusion

UD-PrOZA offers an innovative interdisciplinary platform to diagnose rare diseases in adults with previously unexplained medical problems and to facilitate translational research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02365-y.

The undiagnosed diseases program: Approach to diagnosis

Undiagnosed and rare conditions are collectively common and affect millions of people worldwide. The NIH Undiagnosed Diseases Program (UDP) strives to achieve both a comprehensive diagnosis and a better understanding of the mechanisms of disease for many of these individuals. Through the careful review of records, a well-orchestrated inpatient evaluation, genomic sequencing and testing, and with the use of emerging strategies such as matchmaking programs, the UDP succeeds nearly 30 percent of the time for these highly selective cases. Although the UDP process is built on a unique set of resources, case examples demonstrate steps genetic professionals can take, in both clinical and research settings, to arrive at a diagnosis for their most challenging cases.

Data Sharing Advances Rare and Neglected Disease Clinical Research and Treatments

Because of the decreased cost and increased ease of whole genome analysis, the diagnosis of rare, orphan diseases has entered a new era. This new technological advance, combined with the worldwide web connections, now permits sharing, searching, and linking genotype, phenotype, and other information to facilitate diagnosis. Databases currently accessible and searchable by researchers, clinicians, and patients will be presented and discussed.

De Novo Variants in WDR37 Are Associated with Epilepsy, Colobomas, Dysmorphism, Developmental Delay, Intellectual Disability, and Cerebellar Hypoplasia

WD40 repeat-containing proteins form a large family of proteins present in all eukaryotes. Here, we identified five pediatric probands with de novo variants in WDR37, which encodes a member of the WD40 repeat protein family. Two probands shared one variant and the others have variants in nearby amino acids outside the WD40 repeats. The probands exhibited shared phenotypes of epilepsy, colobomas, facial dysmorphology reminiscent of CHARGE syndrome, developmental delay and intellectual disability, and cerebellar hypoplasia. The WDR37 protein is highly conserved in vertebrate and invertebrate model organisms and is currently not associated with a human disease. We generated a null allele of the single Drosophila ortholog to gain functional insights and replaced the coding region of the fly gene CG12333/wdr37 with GAL4. These flies are homozygous viable but display severe bang sensitivity, a phenotype associated with seizures in flies. Additionally, the mutant flies fall when climbing the walls of the vials, suggesting a defect in grip strength, and repeat the cycle of climbing and falling. Similar to wall clinging defect, mutant males often lose grip of the female abdomen during copulation. These phenotypes are rescued by using the GAL4 in the CG12333/wdr37 locus to drive the UAS-human reference WDR37 cDNA. The two variants found in three human subjects failed to rescue these phenotypes, suggesting that these alleles severely affect the function of this protein. Taken together, our data suggest that variants in WDR37 underlie a novel syndromic neurological disorder.

Integrative LHS for precision medicine research: A shared NIH and Taiwan CIMS experience

INTRODUCTION

Precision medicine is an important milestone toward the attainment of personalized medicine. A learning health system (LHS) may facilitate the evidence collection and knowledge generation process for disease-based research and for the diagnosis, classification, or treatment of each disease subtype to improve patient care.

METHODS

The LHS design and implementation used by Taichung Veterans General Hospital (TCVGH) in Taiwan for their newly funded precision medicine research, a dementia registry study, was modeled from an LHS developed at the National Institutes of Health in the United States. This Clinical Informatics and Management System (CIMS), including its subsystems, facilitates and enhances operations associated with the institutional review board, clinical research data collection and study management, the hospital biobank, and the participating health research centers to support their precision medicine research aimed at improving patient care.

RESULTS

The implementation of a shared-design, full-cycle LHS with an enhanced CIMS, combined with hospital-based real-world data marts, has made the TCVGH dementia registry study a reality. The research data, including clinical assessment and genomics analysis information collected in CIMS, combined with data marts, are the foundation of the TCVGH dementia registry for outcome analyses. These high-quality datasets are useful for clinical validation, new hypotheses, and knowledge generation, leading to new clinical recommendations or guidelines for better patient treatment and care. The cyclic data flow supports the full-cycle LHS for TCVGH's dementia research to improve the care of elderly patients.

CONCLUSIONS

Knowledge generation requires high-quality research and health care datasets. While the details of LHS implementation methods in the United States and Taiwan may differ slightly, the LHS concept design and basic system architecture, with improved CIMSs, were proven feasible. As a result, learning health processes in support of translational research and the potential for improvement in patient care were significantly facilitated.

Analysis and Annotation of Whole-Genome or Whole-Exome Sequencing Derived Variants for Clinical Diagnosis

Over the last 10 years, next-generation sequencing (NGS) has transformed genomic research through substantial advances in technology and reduction in the cost of sequencing, and also in the systems required for analysis of these large volumes of data. This technology is now being used as a standard molecular diagnostic test in some clinical settings. The advances in sequencing have come so rapidly that the major bottleneck in identification of causal variants is no longer the sequencing or analysis (given access to appropriate tools), but rather clinical interpretation. Interpretation of genetic findings in a complex and ever changing clinical setting is scarcely a new challenge, but the task is increasingly complex in clinical genome-wide sequencing given the dramatic increase in dataset size and complexity. This increase requires application of appropriate interpretation tools, as well as development and application of appropriate methodologies and standard procedures. This unit provides an overview of these items. Specific challenges related to implementation of genome-wide sequencing in a clinical setting are discussed. © 2017 by John Wiley & Sons, Inc.

Navigating the Phenotype Frontier: The Monarch Initiative

The principles of genetics apply across the entire tree of life. At the cellular level we share biological mechanisms with species from which we diverged millions, even billions of years ago. We can exploit this common ancestry to learn about health and disease, by analyzing DNA and protein sequences, but also through the observable outcomes of genetic differences, i.e. phenotypes. To solve challenging disease problems we need to unify the heterogeneous data that relates genomics to disease traits. Without a big-picture view of phenotypic data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) provides tools for genotype-phenotype analysis, genomic diagnostics, and precision medicine across broad areas of disease.

International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases

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.

The Undiagnosed Diseases Network: Accelerating Discovery about Health and Disease

Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science.

Clinical Interpretation of Genomic Variations

Novel high-throughput sequencing technologies generate large-scale genomic data and are used extensively for disease mapping of monogenic and/or complex disorders, personalized treatment, and pharmacogenomics. Next-generation sequencing is rapidly becoming routine tool for diagnosis and molecular monitoring of patients to evaluate therapeutic efficiency. The next-generation sequencing platforms generate huge amounts of genetic variation data and it remains a challenge to interpret the variations that are identified. Such data interpretation needs close collaboration among bioinformaticians, clinicians, and geneticists. There are several problems that must be addressed, such as the generation of new algorithms for mapping and annotation, harmonization of the terminology, correct use of nomenclature, reference genomes for different populations, rare disease variant databases, and clinical reports.

User-Friendly Data-Sharing Practices for Fostering Collaboration within a Research Network: Roles of a Vanguard Center for a Community-Based Study

Although various attempts have been made to build collaborative cultures for data sharing, their effectiveness is still questionable. The Jackson Heart Study (JHS) Vanguard Center (JHSVC) at the NIH-funded Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN) Data Coordinating Center (DCC) may be a new concept in that the data are being shared with a research network where a plethora of scientists/researchers are working together to achieve their common goal. This study describes the current practices to share the JHS data through the mechanism of JHSVC. The JHS is the largest single-site cohort study to prospectively investigate the determinants of cardiovascular disease among African-Americans. It has adopted a formal screened access method through a formalized JHSVC mechanism, in which only a qualified scientist(s) can access the data. The role of the DCC was to help RTRN researchers explore hypothesis-driven ideas to enhance the output and impact of JHS data through customized services, such as feasibility tests, data querying, manuscript proposal development and data analyses for publication. DCC has implemented these various programs to facilitate data utility. A total of 300 investigators attended workshops and/or received training booklets. DCC provided two online and five onsite workshops and developed/distributed more than 250 copies of the booklet to help potential data users understand the structure of and access to the data. Information on data use was also provided through the RTRN website. The DCC efforts led to the production of five active manuscript proposals, seven completed publications, 11 presentations and four NIH grant proposals. These outcomes resulted from activities during the first four years; over the last couple of years, there were few new requests. Our study suggested that DCC-customized services enhanced the accessibility of JHS data and their utility by RTRN researchers and helped to achieve the principal goal of JHSVC of scientific productivity. In order to achieve long-term success, the following, but not limited to these, should be addressed in the current data sharing practices: preparation of new promotional strategies in response to changes in technology and users’ needs, collaboration with the Network statisticians, harmonization of the JHS data with the other local-based heart datasets to meet the needs of the potential users from the broader geographical areas, adoption of the RTRN comprehensive data-sharing policy to broaden the variety of research topics and implementation of an ongoing monitoring program to evaluate its success.

All the World's a Stage: Facilitating Discovery Science and Improved Cancer Care through the Global Alliance for Genomics and Health

The recent explosion of genetic and clinical data generated from tumor genome analysis presents an unparalleled opportunity to enhance our understanding of cancer, but this opportunity is compromised by the reluctance of many in the scientific community to share datasets and the lack of interoperability between different data platforms. The Global Alliance for Genomics and Health is addressing these barriers and challenges through a cooperative framework that encourages "team science" and responsible data sharing, complemented by the development of a series of application program interfaces that link different data platforms, thus breaking down traditional silos and liberating the data to enable new discoveries and ultimately benefit patients.

The Matchmaker Exchange: a platform for rare disease gene discovery

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.