RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research

Building national patient registries in Mexico: insights from the MexOMICS Consortium

Objective

To introduce MexOMICS, a Mexican Consortium focused on establishing electronic databases to collect, cross-reference, and share health-related and omics data on the Mexican population.

Methods

Since 2019, the MexOMICS Consortium has established three electronic-based registries: the Mexican Twin Registry (TwinsMX), Mexican Lupus Registry (LupusRGMX), and the Mexican Parkinson's Research Network (MEX-PD), designed and implemented using the Research Electronic Data Capture web-based application. Participants were enrolled through voluntary participation and on-site engagement with medical specialists. We also acquired DNA samples and Magnetic Resonance Imaging scans in subsets of participants.

Results

The registries have successfully enrolled a large number of participants from a variety of regions within Mexico: TwinsMX (n = 2,915), LupusRGMX (n = 1,761) and MEX-PD (n = 750). In addition to sociodemographic, psychosocial, and clinical data, MexOMICS has collected DNA samples to study the genetic biomarkers across the three registries. Cognitive function has been assessed with the Montreal Cognitive Assessment in a subset of 376 MEX-PD participants. Furthermore, a subset of 267 twins have participated in cognitive evaluations with the Creyos platform and in MRI sessions acquiring structural, functional, and spectroscopy brain imaging; comparable evaluations are planned for LupusRGMX and MEX-PD.

Conclusions

The MexOMICS registries offer a valuable repository of information concerning the potential interplay of genetic and environmental factors in health conditions among the Mexican population.

Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening

BACKGROUND

Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem.

METHODS

In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted.

RESULTS

In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed.

CONCLUSIONS

The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.

Australian public perspectives on genomic data governance: responsibility, regulation, and logistical considerations

Genomic sequencing generates huge volumes of data, which may be collected or donated to form large genomic databases. Such information can be stored for future use, either for the data donor themselves or by researchers to help improve our understanding of the genetic basis of disease. Creating datasets of this magnitude and diversity is only possible if patients, their families, and members of the public worldwide share their data. However, there is no consensus on the best technical approach to data sharing that also minimises risks to individuals and exploration of stakeholders' views on aspects of genomic data governance models-the ways genomic data is stored, managed, shared and used-has been minimal. To address this need, we conducted focus groups with 39 members of the Australian public exploring their views and preferences for different aspects of genomic data governance models. We found that consent and control were essential to participants, as they wanted the option to choose who had access to their data and for what purposes. Critically, participants wanted a trustworthy body to enforce regulation of data storage, sharing and usage. While participants recognised the importance of data accessibility, they also expressed a strong desire for data security. Finally, financial responsibility for data storage raised concerns for inequity as well as organisations and individuals using data in ethically contentious ways to generate profit. Our findings highlight some of the trade-offs that need to be considered in the development of genomic data governance systems.

TBC1D32 variants disrupt retinal ciliogenesis and cause retinitis pigmentosa

Retinitis pigmentosa (RP) is the most common inherited retinal disease (IRD) and is characterized by photoreceptor degeneration and progressive vision loss. We report 4 patients presenting with RP from 3 unrelated families with variants in TBC1D32, which to date has never been associated with an IRD. To validate TBC1D32 as a putative RP causative gene, we combined Xenopus in vivo approaches and human induced pluripotent stem cell-derived (iPSC-derived) retinal models. Our data showed that TBC1D32 was expressed during retinal development and that it played an important role in retinal pigment epithelium (RPE) differentiation. Furthermore, we identified a role for TBC1D32 in ciliogenesis of the RPE. We demonstrated elongated ciliary defects that resulted in disrupted apical tight junctions, loss of functionality (delayed retinoid cycling and altered secretion balance), and the onset of an epithelial-mesenchymal transition-like phenotype. Last, our results suggested photoreceptor differentiation defects, including connecting cilium anomalies, that resulted in impaired trafficking to the outer segment in cones and rods in TBC1D32 iPSC-derived retinal organoids. Overall, our data highlight a critical role for TBC1D32 in the retina and demonstrate that TBC1D32 mutations lead to RP. We thus identify TBC1D32 as an IRD-causative gene.

Rare diseases' genetic newborn screening as the gateway to future genomic medicine: the Screen4Care EU-IMI project

Following the reverse genetics strategy developed in the 1980s to pioneer the identification of disease genes, genome(s) sequencing has opened the era of genomics medicine. The human genome project has led to an innumerable series of applications of omics sciences on global health, from which rare diseases (RDs) have greatly benefited. This has propelled the scientific community towards major breakthroughs in disease genes discovery, in technical innovations in bioinformatics, and in the development of patients' data registries and omics repositories where sequencing data are stored. Rare diseases were the first diseases where nucleic acid-based therapies have been applied. Gene therapy, molecular therapy using RNA constructs, and medicines modulating transcription or translation mechanisms have been developed for RD patients and started a new era of medical science breakthroughs. These achievements together with optimization of highly scalable next generation sequencing strategies now allow movement towards genetic newborn screening. Its applications in human health will be challenging, while expected to positively impact the RD diagnostic journey. Genetic newborn screening brings many complexities to be solved, technical, strategic, ethical, and legal, which the RD community is committed to address. Genetic newborn screening initiatives are therefore blossoming worldwide, and the EU-IMI framework has funded the project Screen4Care. This large Consortium will apply a dual genetic and digital strategy to design a comprehensive genetic newborn screening framework to be possibly translated into the future health care.

Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review

Background

Given the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is required to develop artificial intelligence-based decision support that can assist clinicians in their diagnostic decision-making and shorten rare disease patients' diagnostic odyssey. The aim is to identify key challenges in the process of mapping European rare disease databases, relevant to ML-based screening technologies in terms of organizational, FAIR and legal principles.

Methods

A scoping review was conducted based on the PRISMA-ScR checklist. The primary article search was conducted in three electronic databases (MEDLINE/Pubmed, Scopus, and Web of Science) and a secondary search was performed in Google scholar and on the organizations' websites. Each step of this review was carried out independently by two researchers. A charting form for relevant study analysis was developed and used to categorize data and identify data items in three domains - organizational, FAIR and legal.

Results

At the end of the screening process, 73 studies were eligible for review based on inclusion and exclusion criteria with more than 60% (n = 46) of the research published in the last 5 years and originated only from EU/EEA countries. Over the ten-year period (2013-2022), there is a clear cycling trend in the publications, with a peak of challenges reporting every four years. Within this trend, the following dynamic was identified: except for 2016, organizational challenges dominated the articles published up to 2018; legal challenges were the most frequently discussed topic from 2018 to 2022. The following distribution of the data items by domains was observed - (1) organizational (n = 36): data accessibility and sharing (20.2%); long-term sustainability (18.2%); governance, planning and design (17.2%); lack of harmonization and standardization (17.2%); quality of data collection (16.2%); and privacy risks and small sample size (11.1%); (2) FAIR (n = 15): findable (17.9%); accessible sustainability (25.0%); interoperable (39.3%); and reusable (17.9%); and (3) legal (n = 33): data protection by all means (34.4%); data management and ownership (22.9%); research under GDPR and member state law (20.8%); trust and transparency (13.5%); and digitalization of health (8.3%). We observed a specific pattern repeated in all domains during the process of data charting and data item identification - in addition to the outlined challenges, good practices, guidelines, and recommendations were also discussed. The proportion of publications addressing only good practices, guidelines, and recommendations for overcoming challenges when mapping RD databases in at least one domain was calculated to be 47.9% (n = 35).

Conclusion

Despite the opportunities provided by innovation - automation, electronic health records, hospital-based information systems, biobanks, rare disease registries and European Reference Networks - the results of the current scoping review demonstrate a diversity of the challenges that must still be addressed, with immediate actions on ensuring better governance of rare disease registries, implementing FAIR principles, and enhancing the EU legal framework.

Attitudes and beliefs regarding race-targeted genetic testing of Black people: A systematic review

Geographical ancestry has been associated with an increased risk of various genetic conditions. Race and ethnicity often have been used as proxies for geographical ancestry. Despite numerous problems associated with the crude reliance on race and ethnicity as proxies for geographical ancestry, some genetic testing in the clinical, research, and employment settings has been and continues to be race- or ethnicity-based. Race-based or race-targeted genetic testing refers to genetic testing offered only or primarily to people of particular racial or ethnic groups because of presumed differences among groups. One current example is APOL1 testing of Black kidney donors. Race-based genetic testing raises numerous ethical and policy questions. Given the ongoing reliance on the Black race in genetic testing, it is important to understand the views of people who identify as Black or are identified as Black (including African American, Afro-Caribbean, and Hispanic Black) regarding race-based genetic testing that targets Black people because of their race. We conducted a systematic review of studies and reports of stakeholder-engaged projects that examined how people who identify as or are identified as Black perceive genetic testing that specifically presumes genetic differences exist among racial groups or uses race as a surrogate for ancestral genetic variation and targets Black people. Our review identified 14 studies that explicitly studied this question and another 13 that implicitly or tacitly studied this matter. We found four main factors that contribute to a positive attitude toward race-targeted genetic testing (facilitators) and eight main factors that are associated with concerns regarding race-targeted genetic testing (barriers). This review fills an important gap. These findings should inform future genetic research and the policies and practices developed in clinical, research, public health, or other settings regarding genetic testing.

Querying semantic catalogues of biomedical databases

BACKGROUND

Secondary use of health data is a valuable source of knowledge that boosts observational studies, leading to important discoveries in the medical and biomedical sciences. The fundamental guiding principle for performing a successful observational study is the research question and the approach in advance of executing a study. However, in multi-centre studies, finding suitable datasets to support the study is challenging, time-consuming, and sometimes impossible without a deep understanding of each dataset.

METHODS

We propose a strategy for retrieving biomedical datasets of interest that were semantically annotated, using an interface built by applying a methodology for transforming natural language questions into formal language queries. The advantages of creating biomedical semantic data are enhanced by using natural language interfaces to issue complex queries without manipulating a logical query language.

RESULTS

Our methodology was validated using Alzheimer's disease datasets published in a European platform for sharing and reusing biomedical data. We converted data to semantic information format using biomedical ontologies in everyday use in the biomedical community and published it as a FAIR endpoint. We have considered natural language questions of three types: single-concept questions, questions with exclusion criteria, and multi-concept questions. Finally, we analysed the performance of the question-answering module we used and its limitations. The source code is publicly available at https://bioinformatics-ua.github.io/BioKBQA/.

CONCLUSION

We propose a strategy for using information extracted from biomedical data and transformed into a semantic format using open biomedical ontologies. Our method uses natural language to formulate questions to be answered by this semantic data without the direct use of formal query languages.

Precision medicine via the integration of phenotype-genotype information in neonatal genome project

The explosion of next-generation sequencing (NGS) has enabled the widespread use of genomic data in precision medicine. Currently, several neonatal genome projects have emerged to explore the advantages of NGS to diagnose or screen for rare genetic disorders. These projects have made remarkable achievements, but still the genome data could be further explored with the assistance of phenotype collection. In contrast, longitudinal birth cohorts are great examples to record and apply phenotypic information in clinical studies starting at the neonatal period, especially the trajectory analyses for health development or disease progression. It is obvious that efficient integration of genotype and phenotype benefits not only the clinical management of rare genetic disorders but also the risk assessment of complex diseases. Here, we first summarize the recent neonatal genome projects as well as some longitudinal birth cohorts. Then, we propose two simplified strategies by integrating genotypic and phenotypic information in precision medicine based on current studies. Finally, research collaborations, sociological issues, and future perspectives are discussed. How to maximize neonatal genomic information to benefit the pediatric population remains an area in need of more research and effort.

An overlook on the current registries for rare and complex connective tissue diseases and the future scenario of TogethERN ReCONNET

Background

Patient registries play a crucial role in supporting clinical practice, healthcare planning and medical research, offering a real-world picture on rare and complex connective tissue diseases (rCTDs). ERN ReCONNET launched the first European Registry Infrastructure with the aim to plan, upgrade and link registries for rCTDs, with the final goal to promote a harmonized data collection approach all over Europe for rCTDs.

Methods

An online survey addressed to healthcare professionals and patients' representatives active in the field of rCTDs was integrated by an extensive database search in order to build a mapping of existing registries for rCTDs.

Findings

A total of 140 registries were found, 38 of which include multiple diseases. No disease-specific registry was identified for relapsing polychondritis, mixed connective tissue disease and undifferentiated connective tissue disease.

Discussion

This overview on the existing registries for rCTDs provides a useful starting point to identify the gaps and the strengths of registries on the coverage of rCTDs, and to develop a common data set and data collection approach for the establishment of the TogethERN ReCONNET Infrastructure.

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple "yes" or "no" responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a "lingua franca" to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects.

The cell polarity protein Vangl2 in the muscle shapes the neuromuscular synapse by binding to and regulating the tyrosine kinase MuSK

The development of the neuromuscular junction (NMJ) requires dynamic trans-synaptic coordination orchestrated by secreted factors, including Wnt family morphogens. To investigate how these synaptic cues in NMJ development are transduced, particularly in the regulation of acetylcholine receptor (AChR) accumulation in the postsynaptic membrane, we explored the function of Van Gogh-like protein 2 (Vangl2), a core component of Wnt planar cell polarity signaling. We found that conditional, muscle-specific ablation of Vangl2 in mice reproduced the NMJ differentiation defects seen in mice with global Vangl2 deletion. These alterations persisted into adulthood and led to NMJ disassembly, impaired neurotransmission, and deficits in motor function. Vangl2 and the muscle-specific receptor tyrosine kinase MuSK were functionally associated in Wnt signaling in the muscle. Vangl2 bound to and promoted the signaling activity of MuSK in response to Wnt11. The loss of Vangl2 impaired RhoA activation in cultured mouse myotubes and caused dispersed, rather than clustered, organization of AChRs at the postsynaptic or muscle cell side of NMJs in vivo. Our results identify Vangl2 as a key player of the core complex of molecules shaping neuromuscular synapses and thus shed light on the molecular mechanisms underlying NMJ assembly.

Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%).

PhenomeCentral: 7 years of rare disease matchmaking

A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions in order to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral's features have been expanded and its dataset has consistently grown. There are currently 1,615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come. This article is protected by copyright. All rights reserved.

Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources. This article is protected by copyright. All rights reserved.

Curation and expansion of Human Phenotype Ontology for defined groups of inborn errors of immunity

BACKGROUND

Accurate, detailed, and standardized phenotypic descriptions are essential to support diagnostic interpretation of genetic variants and to discover new diseases. The Human Phenotype Ontology (HPO), extensively used in rare disease research, provides a rich collection of vocabulary with standardized phenotypic descriptions in a hierarchical structure. However, to date, the use of HPO has not yet been widely implemented in the field of inborn errors of immunity (IEIs), mainly due to a lack of comprehensive IEI-related terms.

OBJECTIVES

We sought to systematically review available terms in HPO for the depiction of IEIs, to expand HPO, yielding more comprehensive sets of terms, and to reannotate IEIs with HPO terms to provide accurate, standardized phenotypic descriptions.

METHODS

We initiated a collaboration involving expert clinicians, geneticists, researchers working on IEIs, and bioinformaticians. Multiple branches of the HPO tree were restructured and extended on the basis of expert review. Our ontology-guided machine learning coupled with a 2-tier expert review was applied to reannotate defined subgroups of IEIs.

RESULTS

We revised and expanded 4 main branches of the HPO tree. Here, we reannotated 73 diseases from 4 International Union of Immunological Societies-defined IEI disease subgroups with HPO terms. We achieved a 4.7-fold increase in the number of phenotypic terms per disease. Given the new HPO annotations, we demonstrated improved ability to computationally match selected IEI cases to their known diagnosis, and improved phenotype-driven disease classification.

CONCLUSIONS

Our targeted expansion and reannotation presents enhanced precision of disease annotation, will enable superior HPO-based IEI characterization, and hence benefit both IEI diagnostic and research activities.

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.

Patients' priorities and expectations on an EU registry for rare bone and mineral conditions

BACKGROUND

Understanding the natural history of rare bone and mineral conditions is essential for improving clinical practice and the development of new diagnostics and therapeutics. Recruitment and long-term participation in registries are key challenges for researchers.

METHODS

To understand the user needs, the European Reference Network on Rare Bone Diseases (ERN BOND) and European Patient Advocacy Groups developed and implemented a multinational survey about the patient's preferred database content and functionality through an iterative consensus process. The survey was disseminated by national and international patient groups and healthcare professionals. The findings were analysed using descriptive statistics and multivariate regression.

RESULTS

There were 493 eligible responses from 378 adults, 15 children and 100 parents, guardians or carers (PGC) across 22 rare bone and mineral conditions. Osteogenesis imperfecta constituted 53.4% of responses. Contents related to improving treatment and medical services scored the highest and contents about anxiety and socializing scored less highly. Additional content was recommended by 205 respondents. Respondents preferred data entry by their Healthcare Provider (HCP). However, less than 50% of adults received followup from their specialist HCP at least annually and 29% were followed up as needed.

CONCLUSIONS

This survey of individuals, their family, guardians and carers has prioritised the key components for an EU-based rare bone and mineral condition research database. The survey highlights issues around collecting psychosocial impacts as well as measures of HCP trust. The survey demonstrated that using only specialist centre visits for data collection, while preferred by patients, will miss a substantial number of individuals, limiting generalisability. Combined HCP and patient platforms will be required to collect representative and complete natural history data for this patient group.

E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 1st eNMD Congress: Nice, France, March 22-23, 2019

By definition, neuromuscular diseases are rare and fluctuating in terms of symptoms; patients are often lately diagnosed, do not have enough information to understand their condition and be proactive in their management. Usually, insufficient resources or services are available, leading to patients' social burden. From a medical perspective, the rarity of such diseases leads to the unfamiliarity of the medical staff and caregiver and an absence of consensus in disease assessment, treatment, and management. Innovations have to be developed in response to patients' and physicians' unmet needs.It is vital to improve several aspects of patients' quality of life with a better comprehension of their disease, simplify their management and follow-up, help their caregiver, and reduce the social and economic burden for living with a rare debilitating disease. Database construction regrouping patients' data and symptoms according to specific country registration on data privacy will be critical in establishing a clear consensus on neuromuscular disease treatment.Clinicians also need technological innovations to help them recognize neuromuscular diseases, find the best therapeutic approach based on medical consensus, and tools to follow patients' states regularly. Diagnosis also has to be improved by implementing automated systems to analyze a considerable amount of data, representing a significant step forward to accelerate the diagnosis and the patients' follow up. Further, the development of new tools able to precisely measure specific outcomes reliably is of the matter of importance in clinical trials to assess the efficacy of a newly developed compound.In this context, creation of an expert community is essential to communicate and share ideas. To this end, 97 clinicians, healthcare professionals, researchers, and representatives of private companies from 9 different countries met to discuss the new perspective and challenges to develop and implement innovative tools in the field of neuromuscular diseases.

BBMRI-ERIC Negotiator: Implementing Efficient Access to Biobanks

Various biological resources, such as biobanks and disease-specific registries, have become indispensable resources to better understand the epidemiology and biological mechanisms of disease and are fundamental for advancing medical research. Nevertheless, biobanks and similar resources still face significant challenges to become more findable and accessible by users on both national and global scales. One of the main challenges for users is to find relevant resources using cataloging and search services such as the BBMRI-ERIC Directory, operated by European Research Infrastructure on Biobanking and Biomolecular Resources (BBMRI-ERIC), as these often do not contain the information needed by the researchers to decide if the resource has relevant material/data; these resources are only weakly characterized. Hence, the researcher is typically left with too many resources to explore and investigate. In addition, resources often have complex procedures for accessing holdings, particularly for depletable biological materials. This article focuses on designing a system for effective negotiation of access to holdings, in which a researcher can approach many resources simultaneously, while giving each resource team the ability to implement their own mechanisms to check if the material/data are available and to decide if access should be provided. The BBMRI-ERIC has developed and implemented an access and negotiation tool called the BBMRI-ERIC Negotiator. The Negotiator enables access negotiation to more than 600 biobanks from the BBMRI-ERIC Directory and other discovery services such as GBA/BBMRI-ERIC Locator or RD-Connect Finder. This article summarizes the principles that guided the design of the tool, the terminology used and underlying data model, request workflows, authentication and authorization mechanism(s), and the mechanisms and monitoring processes to stimulate the desired behavior of the resources: to effectively deliver access to biological material and data.

Concept libraries for automatic electronic health record based phenotyping: A review

Introduction

Electronic health records (EHR) are linked together to examine disease history and to undertake research into the causes and outcomes of disease. However, the process of constructing algorithms for phenotyping (e.g., identifying disease characteristics) or health characteristics (e.g., smoker) is very time consuming and resource costly. In addition, results can vary greatly between researchers. Reusing or building on algorithms that others have created is a compelling solution to these problems. However, sharing algorithms is not a common practice and many published studies do not detail the clinical code lists used by the researchers in the disease/characteristic definition. To address these challenges, a number of centres across the world have developed health data portals which contain concept libraries (e.g., algorithms for defining concepts such as disease and characteristics) in order to facilitate disease phenotyping and health studies.

Objectives

This study aims to review the literature of existing concept libraries, examine their utilities, identify the current gaps, and suggest future developments.

Methods

The five-stage framework of Arksey and O'Malley was used for the literature search. This approach included defining the research questions, identifying relevant studies through literature review, selecting eligible studies, charting and extracting data, and summarising and reporting the findings.

Results

This review identified seven publicly accessible Electronic Health data concept libraries which were developed in different countries including UK, USA, and Canada. The concept libraries (n = 7) investigated were either general libraries that hold phenotypes of multiple specialties (n = 4) or specialized libraries that manage only certain specialities such as rare diseases (n = 3). There were some clear differences between the general libraries such as archiving data from different electronic sources, and using a range of different types of coding systems. However, they share some clear similarities such as enabling users to upload their own code lists, and allowing users to use/download the publicly accessible code. In addition, there were some differences between the specialized libraries such as difference in ability to search, and if it was possible to use different searching queries such as simple or complex searches. Conversely, there were some similarities between the specialized libraries such as enabling users to upload their own concepts into the libraries and to show where they were published, which facilitates assessing the validity of the concepts. All the specialized libraries aimed to encourage the reuse of research methods such as lists of clinical code and/or metadata.

Conclusion

The seven libraries identified have been developed independently and appear to replicate similar concepts but in different ways. Collaboration between similar libraries would greatly facilitate the use of these libraries for the user. The process of building code lists takes time and effort. Access to existing code lists increases consistency and accuracy of definitions across studies. Concept library developers should collaborate with each other to raise awareness of their existence and of their various functions, which could increase users’ contributions to those libraries and promote their wide-ranging adoption.

Ethics and equity in rare disease research and healthcare

Rapid advances in next-generation sequencing technology, particularly whole exome sequencing and whole genome sequencing, have greatly affected our understanding of genetic variation underlying rare genetic diseases. Herein, we describe ethical principles of guiding consent and sharing of genomics research data. We also discuss ethical dilemmas in rare diseases research and patient recruitment policies and address bioethical and societal aspects influencing the ethical framework for genetic testing. Moreover, we focus on addressing ethical issues surrounding research in low- and middle-income countries. Overall, this perspective aims to address key aspects and issues for building proper ethical frameworks, when conducting research involving genomics data with a particular emphasis on rare diseases and genetics testing.

Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.

Mutation-specific pathophysiological mechanisms define different neurodevelopmental disorders associated with SATB1 dysfunction

Whereas large-scale statistical analyses can robustly identify disease-gene relationships, they do not accurately capture genotype-phenotype correlations or disease mechanisms. We use multiple lines of independent evidence to show that different variant types in a single gene, SATB1, cause clinically overlapping but distinct neurodevelopmental disorders. Clinical evaluation of 42 individuals carrying SATB1 variants identified overt genotype-phenotype relationships, associated with different pathophysiological mechanisms, established by functional assays. Missense variants in the CUT1 and CUT2 DNA-binding domains result in stronger chromatin binding, increased transcriptional repression, and a severe phenotype. In contrast, variants predicted to result in haploinsufficiency are associated with a milder clinical presentation. A similarly mild phenotype is observed for individuals with premature protein truncating variants that escape nonsense-mediated decay, which are transcriptionally active but mislocalized in the cell. Our results suggest that in-depth mutation-specific genotype-phenotype studies are essential to capture full disease complexity and to explain phenotypic variability.

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1

Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1. This enzyme catalyzes the removal of sialic acid moieties from glycoproteins and glycolipids. Therefore, the defective or deficient protein leads to the buildup of sialylated glycoproteins as well as several characteristic symptoms of sialidosis including visual impairment, ataxia, hepatomegaly, dysostosis multiplex, and developmental delay. In this study, we used a bibliometric tool to generate links between lysosomal storage disease (LSD) targets and existing bioactivity data that could be curated in order to build machine learning models and screen compounds in silico. We focused on sialidase as an example, and we used the data curated from the literature to build a Bayesian model which was then used to score compound libraries and rank these molecules for in vitro testing. Two compounds were identified from in vitro testing using microscale thermophoresis, namely sulfameter (K _d 2.15 ± 1.02 μM) and mexenone (K _d 8.88 ± 4.02 μM), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.

Mapping, Infrastructure, and Data Analysis for the Brazilian Network of Rare Diseases: Protocol for the RARASnet Observational Cohort Study

Background

A rare disease is a medical condition with low prevalence in the general population, but these can collectively affect up to 10% of the population. Thus, rare diseases have a significant impact on the health care system, and health professionals must be familiar with their diagnosis, management, and treatment.

Objective

This paper aims to provide health indicators regarding the rare diseases in Brazil and to create a network of reference centers with health professionals from different regions of the country. RARASnet proposes to map, analyze, and communicate all the data regarding the infrastructure of the centers and the patients’ progress or needs. The focus of the proposed study is to provide all the technical infrastructure and analysis, following the World Health Organization and the Brazilian Ministry of Health guidelines.

Methods

To build this digitized system, we will provide a security framework to assure the privacy and protection of each patient when collecting data. Systems development life cycle methodologies will also be applied to align software development, infrastructure operation, and quality assurance. After data collection of all information designed by the specialists, the computational analysis, modeling, and results will be communicated in scientific research papers and a digital health observatory.

Results

The project has several activities, and it is in an initial stage. Initially, a survey was given to all health care centers to understand the technical aspects of each network member, such as the existence of computers, technical support staff, and digitized systems. In this survey, we detected that 59% (23/39) of participating health units have electronic medical records, while 41% (16/39) have paper records. Therefore, we will have different strategies to access the data from each center in the data collection phase. Later, we will standardize and analyze the clinical and epidemiological data and use these data to develop a national network for monitoring rare diseases and a digital health observatory to make the information available. The project had its financing approved in December 2019. Retrospective data collection started in October 2020, and we expect to finish in January 2021. During the third quarter of 2020, we enrolled 40 health institutions from all regions of Brazil.

Conclusions

The nature of rare disease diagnosis is complex and diverse, and many problems will be faced in the evolution of the project. However, decisions based on data analysis are the best option for the improvement of the rare disease network in Brazil. The creation of RARASnet, along with all the digitized infrastructure, can improve the accessibility of information and standardization of rare diseases in the country.

International Registered Report Identifier (IRRID)

DERR1-10.2196/24826

Preferences for Accessing Electronic Health Records for Research Purposes: Views of Parents Who Have a Child With a Known or Suspected Genetic Condition

OBJECTIVES

The purpose of this study was to examine parental preferences for researchers accessing their child's electronic health record across 3 groups: those with a child with (1) a known genetic condition (fragile X syndrome FXS), (2) a suspected genetic condition (autism spectrum disorder [ASD]), and (3) no known genetic condition (typically developing).

METHODS

After extensive formative work, a discrete choice experiment was designed consisting of 5 attributes, each with 2 or 3 levels, including (1) type of researcher, (2) the use of personally identifiable information, (3) the use of sensitive information, (4) personal importance of research, and (5) return of results. Stratified mixed logit and latent class conditional logit models were examined.

RESULTS

Parents of children with FXS or ASD had relatively higher preferences for research conducted by nonprofits than parents of typically developing children. Parents of children with ASD also preferred research using non-identifiable and nonsensitive information. Parents of children with FXS or ASD also had preferences for research that was personally important and returned either summary or individual results. Although a few child and family characteristics were related to preferences, they did not overall define the subgroups of parents.

CONCLUSIONS

Although electronic health record preference research has been conducted with the general public, this is the first study to examine the opinions of parents who have a child with a known or suspected genetic condition. These parents were open to studies using their child's electronic health record because they may have more to gain from this type of research.

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD)

Background

The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientific understanding and underlying evidence for these diseases have expanded over time, existing practices to generate knowledge from these publications and resources have not been able to keep pace. Through determining the applicability of computational approaches to enhance or replace manual curation tasks, we aim to both improve the sustainability and relevance of consumer health information, but also to develop a foundational database, from which translational science researchers may start to unravel disease characteristics that are vital to the research process.

Results

We developed a meta-ontology based integrative knowledge graph for rare diseases in Neo4j. This integrative knowledge graph includes a total of 3,819,623 nodes and 84,223,681 relations from 34 different biomedical data resources, including curated drug and rare disease associations. Semi-automatic mappings were generated for 2154 unique FDA orphan designations to 776 unique GARD diseases, and 3322 unique FDA designated drugs to UNII, as well as 180,363 associations between drug and indication from Inxight Drugs, which were integrated into the knowledge graph. We conducted four case studies to demonstrate the capabilities of this integrative knowledge graph in accelerating the curation of scientific understanding on rare diseases through the generation of disease mappings/profiles and pathogenesis associations.

Conclusions

By integrating well-established database resources, we developed an integrative knowledge graph containing a large volume of biomedical and research data. Demonstration of several immediate use cases and limitations of this process reveal both the potential feasibility and barriers of utilizing graph-based resources and approaches to support their use by providers of consumer health information, such as GARD, that may struggle with the needs of maintaining knowledge reliant on an evolving and growing evidence-base. Finally, the successful integration of these datasets into a freely accessible knowledge graph highlights an opportunity to take a translational science view on the field of rare diseases by enabling researchers to identify disease characteristics, which may play a role in the translation of discover across different research domains.

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

BACKGROUND

Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis.

METHODS

A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset.

RESULTS

A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrix-associated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix.

CONCLUSION

We uncovered a candidate gene signature correlated with HF, ISCH and CMP in the left ventricle, which may help provide better prognosis and therapeutic decisions and in HF, ISCH and CMP patients.

Improved Diagnosis of Rare Disease Patients through Systematic Detection of Runs of Homozygosity

Autozygosity is associated with an increased risk of genetic rare disease, thus being a relevant factor for clinical genetic studies. More than 2400 exome sequencing data sets were analyzed and screened for autozygosity on the basis of detection of >1 Mbp runs of homozygosity (ROHs). A model was built to predict if an individual is likely to be a consanguineous offspring (accuracy, 98%), and probability of consanguinity ranges were established according to the total ROH size. Application of the model resulted in the reclassification of the consanguinity status of 12% of the patients. The analysis of a subset of 79 consanguineous cases with the Rare Disease (RD)–Connect Genome-Phenome Analysis Platform, combining variant filtering and homozygosity mapping, enabled a 50% reduction in the number of candidate variants and the identification of homozygous pathogenic variants in 41 patients, with an overall diagnostic yield of 52%. The newly defined consanguinity ranges provide, for the first time, specific ROH thresholds to estimate inbreeding within a pedigree on disparate exome sequencing data, enabling confirmation or (re)classification of consanguineous status, hence increasing the efficiency of molecular diagnosis and reporting on secondary consanguinity findings, as recommended by American College of Medical Genetics and Genomics guidelines.

Interoperability of population-based patient registries

•

Inter-linkage of patient-registry data provides a rich source of secondary data usage.

•

Mapping of heterogeneous metadata systems can be accomplished via semantic links.

•

Semantic linkage between patient registries provides the basis for interoperability.

•

Semantic metadata registry frameworks can facilitate access back to primary data sources.

Enabling full interoperability within and between population-based patient-registry domains would open up access to a rich and unique source of health data for secondary data usage. Previous attempts to tackle patient-registry interoperability have met with varying degrees of success, but a unifying solution remains elusive. The purpose of this paper is to show by practical example how a solution is attainable via the implementation of an existing framework based of the concept of federated, semantic metadata registries. One important feature motivating the use of this framework is that it can be implemented gradually and independently within each patient-registry domain. By employing linked open data principles, the framework extends the ISO/IEC 11179 standard to provide both syntactic and semantic interoperability of data elements with the means of specifying automated extraction scripts for retrieval of data from different registry content models. The examples provided address the domain of European population-based cancer registries to demonstrate the feasibility of the approach. One of the examples shows how quick gains are derivable by allowing retrieval of aggregated core data sets. The other examples show how aggregated full sets of data and record-level data might also be retrieved from each local registry. An infrastructure of patient-registry domains adhering to the principles of the framework would provide the semantic contexts and inter-linkage of data necessary for automated search and retrieval of registry data. It would thereby also lay the foundation for making registry data serviceable to artificial intelligence (AI) applications.

The use of machine learning in rare diseases: a scoping review

Background

Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied.

Methods

Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010–2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed.

Results

Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and “omics” data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%).

Conclusion

Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

Copy number variants (CNVs): a powerful tool for iPSC-based modelling of ASD

Patients diagnosed with chromosome microdeletions or duplications, known as copy number variants (CNVs), present a unique opportunity to investigate the relationship between patient genotype and cell phenotype. CNVs have high genetic penetrance and give a good correlation between gene locus and patient clinical phenotype. This is especially effective for the study of patients with neurodevelopmental disorders (NDD), including those falling within the autism spectrum disorders (ASD). A key question is whether this correlation between genetics and clinical presentation at the level of the patient can be translated to the cell phenotypes arising from the neurodevelopment of patient induced pluripotent stem cells (iPSCs).Here, we examine how iPSCs derived from ASD patients with an associated CNV inform our understanding of the genetic and biological mechanisms underlying the aetiology of ASD. We consider selection of genetically characterised patient iPSCs; use of appropriate control lines; aspects of human neurocellular biology that can capture in vitro the patient clinical phenotype; and current limitations of patient iPSC-based studies. Finally, we consider how future research may be enhanced to maximise the utility of CNV patients for research of pathological mechanisms or therapeutic targets.

HmtVar: a new resource for human mitochondrial variations and pathogenicity data

Interest in human mitochondrial genetic data is constantly increasing among both clinicians and researchers, due to the involvement of mitochondrial DNA (mtDNA) in a number of physiological and pathological processes. Thanks to new sequencing technologies and modern databases, the large amount of information on mtDNA variability may be exploited to gain insights into the relationship between mtDNA variants, phenotypes and diseases. To facilitate this process, we have developed the HmtVar resource, a variant-focused database that allows the exploration of a dataset of over 40 000 human mitochondrial variants. Mitochondrial variation data, initially gathered from the HmtDB platform, are integrated with in-house pathogenicity assessments based on various evaluation criteria and with a set of additional annotations from third-party resources. The result is a comprehensive collection of information of crucial importance for human mitochondrial variation studies and investigation of common and rare diseases in which the mitochondrion may be involved. HmtVar is accessible at https://www.hmtvar.uniba.it and data may be retrieved using either a web interface through the Query page or a state-of-the-art API for programmatic access.

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

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.

Five new cases of syndromic intellectual disability due to KAT6A mutations: widening the molecular and clinical spectrum

BACKGROUND

Pathogenic variants of the lysine acetyltransferase 6A or KAT6A gene are associated with a newly identified neurodevelopmental disorder characterized mainly by intellectual disability of variable severity and speech delay, hypotonia, and heart and eye malformations. Although loss of function (LoF) mutations were initially reported as causing this disorder, missense mutations, to date always involving serine residues, have recently been associated with a form of the disorder without cardiac involvement.

RESULTS

In this study we present five new patients, four with truncating mutations and one with a missense change and the only one not presenting with cardiac anomalies. The missense change [p.(Gly359Ser)], also predicted to affect splicing by in silico tools, was functionally tested in the patient's lymphocyte RNA revealing a splicing effect for this allele that would lead to a frameshift and premature truncation.

CONCLUSIONS

An extensive revision of the clinical features of these five patients revealed high concordance with the 80 cases previously reported, including developmental delay with speech delay, feeding difficulties, hypotonia, a high bulbous nose, and recurrent infections. Other features present in some of these five patients, such as cryptorchidism in males, syndactyly, and trigonocephaly, expand the clinical spectrum of this syndrome.

Rare disease data stewardship in Canada

The Canadian Genomics Partnership for Rare Diseases, spearheaded by Genome Canada, will integrate genome-wide sequencing to rare disease clinical care in Canada. Centralized and tiered models of data stewardship are proposed to ensure that the data generated can be shared for secondary clinical, research, and quality assurance purposes in compliance with ethics and law. The principal ethico-legal obligations of clinicians, researchers, and institutions are synthesized. Governance infrastructures such as registered access platforms, data access compliance offices, and Beacon systems are proposed as potential organizational and technical foundations of responsible rare disease data sharing. The appropriate delegation of responsibilities, the transparent communication of rights and duties, and the integration of data privacy safeguards into infrastructure design are proposed as the cornerstones of rare disease data stewardship.

Broad donor consent for human biobanks in Germany and Europe: a strategy to facilitate cross-border sharing and exchange of human biological materials and related data

Background

Human biobanks are generally recognized as essential resources for effective biomedical research. All over the world biosamples and data from human subjects are collected in large biobanks. The biological material is stored long term for current and future (undetermined) research issues, which often require cross-border exchange of biosamples and related data.

Content

Commonly, the informed consent for research on human biospecimen is intended to cover only defined, specific research objectives. In June 2016, the biobank Task-Force of the Working Party of the German Medical Ethics Committees (WP-GMEC) updated its template for the broad use of human biological samples and related data. It complies with the current Organisation for Economic Co-operation and Development (OECD) and World Medical Association (WMA) recommendations and furnishes a framework that permits long-term storage and multi-purpose research use of human biological material and related data, including cross-border research.

However, both (i) human biobanks storing and (ii) research projects requesting “broad consent” biological samples generally require an ethical approval; in addition, “broad consent” conditions should be reciprocated by making biobank processes transparent and by fostering both donor and public involvement.

Outlook

The broad consent template of the WP-GMEC clearly states that biological samples and data donated for medical research serve to address current and future research questions. It appears perfectly suited as a template for a Europe-wide harmonized broad consent facilitating biobank-based cross-border research.

The use or generation of biomedical data and existing medicines to discover and establish new treatments for patients with rare diseases - recommendations of the IRDiRC Data Mining and Repurposing Task Force

The number of available therapies for rare diseases remains low, as fewer than 6% of rare diseases have an approved treatment option. The International Rare Diseases Research Consortium (IRDiRC) set up the multi-stakeholder Data Mining and Repurposing (DMR) Task Force to examine the potential of applying biomedical data mining strategies to identify new opportunities to use existing pharmaceutical compounds in new ways and to accelerate the pace of drug development for rare disease patients. In reviewing past successes of data mining for drug repurposing, and planning for future biomedical research capacity, the DMR Task Force identified four strategic infrastructure investment areas to focus on in order to accelerate rare disease research productivity and drug development: (1) improving the capture and sharing of self-reported patient data, (2) better integration of existing research data, (3) increasing experimental testing capacity, and (4) sharing of rare disease research and development expertise. Additionally, the DMR Task Force also recommended a number of strategies to increase data mining and repurposing opportunities for rare diseases research as well as the development of individualized and precision medicine strategies.

Comparison of Causative Variant Prioritization Tools Using Next-generation Sequencing Data in Japanese Patients with Mendelian Disorders

Background

During the investigation of causative variants of Mendelian disorders using next-generation sequencing, the enormous number of possible candidates makes the detection process complex, and the use of multidimensional methods is required. Although the utility of several variant prioritization tools has been reported, their effectiveness in Japanese patients remains largely unknown.

Methods

We selected 5 free variant prioritization tools (PhenIX, hiPHIVE, Phen-Gen, eXtasy-order statistics, and eXtasy-combined max) and assessed their effectiveness in Japanese patient populations. To compare these tools, we conducted 2 studies: one based on simulated data of 100 diseases and another based on the exome data of 20 in-house patients with Mendelian disorders. To this end we selected 100 pathogenic variants from the "Database of Pathogenic Variants (DPV)" and created 100 variant call format (VCF) files that each had pathogenic variants based on reference human genome data from the 1000 Genomes Project. The later "in-house" study used exome data from 20 Japanese patients with Mendelian disorders. In both studies, we utilized 1-5 terms of "Human Phenotype Ontology" as clinical information.

Results

In our analysis based on simulated disease data, the detection rate of the top 10 causative variants was 91% for hiPHIVE, and 88% for PhenIX, based on 100 sets of simulated disease VCF data. Also, both software packages detected 82% of the top 1 causative variants. When we used data from our in-house patients instead, we found that these two programs (PhenIX and hiPHIVE) produced higher detection rates than the other three systems in our study. The detection rate of the top 1 causative variant was 71.4% for PhenIX, 65.0% for hiPHIVE.

Conclusion

The rates of detecting causative variants in two Exomizer software packages, hiPHIVE and PhenIX, were higher than for the other three software systems we analyzed, with respect to Japanese patients.

VarAFT: a variant annotation and filtration system for human next generation sequencing data

With the rapidly developing high-throughput sequencing technologies known as next generation sequencing or NGS, our approach to gene hunting and diagnosis has drastically changed. In <10 years, these technologies have moved from gene panel to whole genome sequencing and from an exclusively research context to clinical practice. Today, the limit is not the sequencing of one, many or all genes but rather the data analysis. Consequently, the challenge is to rapidly and efficiently identify disease-causing mutations within millions of variants. To do so, we developed the VarAFT software to annotate and pinpoint human disease-causing mutations through access to multiple layers of information. VarAFT was designed both for research and clinical contexts and is accessible to all scientists, regardless of bioinformatics training. Data from multiple samples may be combined to address all Mendelian inheritance modes, cancers or population genetics. Optimized filtration parameters can be stored and re-applied to large datasets. In addition to classical annotations from dbNSFP, VarAFT contains unique features at the disease (OMIM), phenotypic (HPO), gene (Gene Ontology, pathways) and variation levels (predictions from UMD-Predictor and Human Splicing Finder) that can be combined to optimally select candidate pathogenic mutations. VarAFT is freely available at: http://varaft.eu.

Share and protect our health data: an evidence based approach to rare disease patients' perspectives on data sharing and data protection - quantitative survey and recommendations

Background

The needs and benefits of sharing health data to advance scientific research and improve clinical benefits have been well documented in recent years, specifically in the field of rare diseases where knowledge and expertise are limited and patient populations are geographically dispersed. Understanding what patients want and need from rare disease research and data sharing is important to ensure their participation and engagement in the process, and to ensure that these wishes and needs are embedded within research design. EURORDIS-Rare Diseases Europe regularly surveys the rare disease community to identify its perspectives and needs on a number of issues in order to represent rare disease patients and be their voice within European and International initiatives and policy developments.

Here, we present key findings from a large quantitative survey conducted with patients with rare diseases and family members as part of a continuous evidence-based advocacy process developed at EURORDIS. The aim of this survey was to explore patient and family perspectives on data sharing and data protection in research and healthcare settings and develop relevant recommendations to support shaping of future data sharing initiatives in rare disease research.

This survey, translated into 23 languages, was carried out via the Rare Barometer Programme and was designed to be accessible to a diverse population with a wide range of education backgrounds. It was widely disseminated via patient organisations worldwide to ensure that a wide range of voices and experiences were represented.

Main findings

Rare disease patients, regardless of the severity of their disease and their socio-demographic profile, are clearly supportive of data sharing to foster research and improve healthcare. However, rare disease patients’ willingness to share their data does come with specific requirements in order to respect their privacy, choices and needs for information regarding the use of their data.

Conclusions

To ensure sustainability and success of international data sharing initiatives in health and research for rare diseases, appropriate legislations need to be implemented and multi-stakeholder efforts need to be pursued to foster cultural and technological changes enabling the systematic integration of patients’ preferences regarding sharing of their own health data.

Electronic supplementary material

The online version of this article (10.1186/s13023-019-1123-4) contains supplementary material, which is available to authorized users.

Congenital Myasthenic Syndromes or Inherited Disorders of Neuromuscular Transmission: Recent Discoveries and Open Questions

Congenital myasthenic syndromes (CMS) form a heterogeneous group of rare diseases characterized by fatigable muscle weakness. They are genetically-inherited and caused by defective synaptic transmission at the cholinergic neuromuscular junction (NMJ). The number of genes known to cause CMS when mutated is currently 30, and the relationship between fatigable muscle weakness and defective functions is quite well-understood for many of them. However, some of the most recent discoveries in individuals with CMS challenge our knowledge of the NMJ, where the basis of the pathology has mostly been investigated in animal models. Frontier forms between CMS and congenital myopathy, which have been genetically and clinically identified, underline the poorly understood interplay between the synaptic and extrasynaptic molecules in the neuromuscular system. In addition, precise electrophysiological and histopathological investigations of individuals with CMS suggest an important role of NMJ plasticity in the response to CMS pathogenesis. While efficient drug-based treatments are already available to improve neuromuscular transmission for most forms of CMS, others, as well as neurological and muscular comorbidities, remain resistant. Taken together, the available pathological data point to physiological issues which remain to be understood in order to achieve precision medicine with efficient therapeutics for all individuals suffering from CMS.

National platform for Rare Diseases Data Registry of Japan

INTRODUCTION

In Japan, there are approximately 300 projects conducting research on rare diseases supported by the Ministry of Health, Labour and Welfare of Japan (MHLW) and the Japan Agency for Medical Research and Development (AMED). Diverse data, including clinical, genomic, and sample-related data, are generated by these projects. However, at present, such data are managed individually by each project. This makes it difficult for third parties to ascertain the data generated by projects.

METHODS

Again this background, at the beginning of 2017, the AMED started the National Platform for Rare Diseases Data Registry of Japan (RADDAR-J), whose mission is to construct a cross-sectional data integration platform incorporating projects supported by the AMED and MHLW. RADDAR-J promotes data sharing by the projects in accordance with the data-sharing policy established by the AMED, which classifies data sharing into three categories based on the strategies used to protect the rights of researchers while promoting data sharing. RADDAR-J integrates and analyzes data shared by each project to add value to the resources and promote secondary use by third parties while protecting the rights of the researchers who shared their data. The platform is designed to provide incentives to projects that shared their data by supporting registry construction or genomic analysis to promote data sharing. RADDAR-J also has the function of data identification to securely integrate data originating from the same person. RADDAR-J accelerates clinical research by encouraging each project to utilize a central ethics committee.

RESULTS/CONCLUSION

The use of the platform by projects is expected to lead to streamlined data collection, improved quality assurance, improved access to data, and promotion of joint research and the secondary use of shared data. These benefits will accelerate research into diagnosis and treatment technologies and will hopefully lead to improved quality of life for patients with rare diseases.

Research activity and capability in the European reference network MetabERN

Background

MetabERN is one of the 24 European Reference Networks created according to the European Union directive 2011/24/EU on patient’s rights in cross border healthcare. MetabERN associates 69 centres in 18 countries, which provide care for patients with Hereditary Metabolic Diseases, and have the mission to reinforce research and provide training for health professionals in this field. MetabERN performed a survey in December 2017 with the aim to produce an overview documenting research activities and potentials within the network. As the centres are multidisciplinary, separated questionnaires were sent to the clinical, university and laboratory teams. Answers were received from 52 out of the 69 centres of the network, covering 16 countries. A descriptive analysis of the information collected is presented.

Results

The answers indicate a marked interest of the respondents for research, who expressed high motivation and commitment, and estimated that the conditions to do research in their institution were mostly satisfactory. They are active in research, which according to several indicators, is competitive and satisfies standards of excellence, as well as the education programs offered in the respondent’s universities. Research in the centres is primarily performed in genetics, pathophysiology, and epidemiology, and focuses on issues related to diagnosis. Few respondents declared having activity in human and social sciences, including research on patient’s quality of life, patient’s awareness, or methods for social support. Infrastructures offering services for medical research were rarely known and used by respondents, including national and international biobanking platforms. In contrast, respondents often participate to patient registries, even beyond their specific field of interest.

Conclusions

Taken as a whole, these results provide an encouraging picture of the research capacities and activities in the MetabERN network, which, with respect to the number and representativeness of the investigated centres, gives a comprehensive picture of research on Hereditary Metabolic Diseases in Europe, as well as the priorities for future actions. Marginal activity in human and social sciences points out the limited multidisciplinary constitution of the responding teams with possible consequences on their current capability to participate to patient’s empowerment programs and efficiently collaborate with patient’s advocacy groups.

Electronic supplementary material

The online version of this article (10.1186/s13023-019-1091-8) contains supplementary material, which is available to authorized users.

Attitudes of publics who are unwilling to donate DNA data for research

With the use of genetic technology, researchers have the potential to inform medical diagnoses and treatment in actionable ways. Accurate variant interpretation is a necessary condition for the utility of genetic technology to unfold. This relies on the ability to access large genomic datasets so that comparisons can be made between variants of interest. This can only be successful if DNA and medical data are donated by large numbers of people to 'research', including clinical, non-profit and for-profit research initiatives, in order to be accessed by scientists and clinicians worldwide. The objective of the 'Your DNA, Your Say' global survey is to explore public attitudes, values and opinions towards willingness to donate and concerns regarding the donation of one's personal data for use by others. Using a representative sample of 8967 English-speaking publics from the UK, the USA, Canada and Australia, we explore the characteristics of people who are unwilling (n = 1426) to donate their DNA and medical information, together with an exploration of their reasons. Understanding this perspective is important for making sense of the interaction between science and society. It also helps to focus engagement initiatives on the issues of concern to some publics.

Exploiting machine learning for end-to-end drug discovery and development

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.

Genomic variant sharing: a position statement

Sharing de-identified genetic variant data is essential for the practice of genomic medicine and is demonstrably beneficial to patients. Robust genetic diagnoses that inform medical management cannot be made accurately without reference to genetic test results from other patients, as well as population controls. Errors in this process can result in delayed, missed or erroneous diagnoses, leading to inappropriate or missed medical interventions for the patient and their family. The benefits of sharing individual genetic variants, and the harms of not sharing them, are numerous and well-established. Databases and mechanisms already exist to facilitate deposition and sharing of pseudonomised genetic variants, but clarity and transparency around best practice is needed to encourage widespread use, prevent inconsistencies between different communities, maximise individual privacy and ensure public trust. We therefore recommend that widespread sharing of a small number of individual genetic variants associated with limited clinical information should become standard practice in genomic medicine. Information robustly linking genetic variants with specific conditions is fundamental biological knowledge, not personal information, and therefore should not require consent to share. For additional case-level detail about individual patients or more extensive genomic information, which is often essential for clinical interpretation, it may be more appropriate to use a controlled-access model for data sharing, with the ultimate aim of making as much information as open and de-identified as possible with appropriate consent.