PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases

Diagnosing rare diseases after the exome

High-throughput sequencing has ushered in a diversity of approaches for identifying genetic variants and understanding genome structure and function. When applied to individuals with rare genetic diseases, these approaches have greatly accelerated gene discovery and patient diagnosis. Over the past decade, exome sequencing has emerged as a comprehensive and cost-effective approach to identify pathogenic variants in the protein-coding regions of the genome. However, for individuals in whom exome-sequencing fails to identify a pathogenic variant, we discuss recent advances that are helping to reduce the diagnostic gap.

Registered access: authorizing data access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease

BACKGROUND

Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added.

METHODS

We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care.

RESULTS

A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes.

CONCLUSIONS

The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).

Meeting Patients' Right to the Correct Diagnosis: Ongoing International Initiatives on Undiagnosed Rare Diseases and Ethical and Social Issues

The time required to reach a correct diagnosis is a key concern for rare disease (RD) patients. Diagnostic delay can be intolerably long, often described as an “odyssey” and, for some, a diagnosis may remain frustratingly elusive. The International Rare Disease Research Consortium proposed, as ultimate goal for 2017–2027, to enable all people with a suspected RD to be diagnosed within one year of presentation, if the disorder is known. Subsequently, unsolved cases would enter a globally coordinated diagnostic and research pipeline. In-depth analysis of the genotype through next generation sequencing, together with a standardized in-depth phenotype description and sophisticated high-throughput approaches, have been applied as diagnostic tools to increase the chance of a timely and accurate diagnosis. The success of this approach is evident in the Orphanet database. From 2010 to March 2017 over 600 new RDs and roughly 3600 linked genes have been described and identified. However, combination of -omics and phenotype data, as well as international sharing of this information, has raised ethical concerns. Values to be assessed include not only patient autonomy but also family implications, beneficence, non-maleficence, justice, solidarity and reciprocity, which must be respected and promoted and, at the same time, balanced among each other. In this work we suggest that, to maximize patients’ involvement in the search for a diagnosis and identification of new causative genes, undiagnosed patients should have the possibility to: (1) actively participate in the description of their phenotype; (2) choose the level of visibility of their profile in matchmaking databases; (3) express their preferences regarding return of new findings, in particular which level of Variant of Unknown Significance (VUS) significance should be considered relevant to them. The quality of the relationship between individual patients and physicians, and between the patient community and the scientific community, is critically important for optimizing the use of available data and enabling international collaboration in order to provide a diagnosis, and the attached support, to unsolved cases. The contribution of patients to collecting and coding data comprehensively is critical for efficient use of data downstream of data collection.

PubCaseFinder: A Case-Report-Based, Phenotype-Driven Differential-Diagnosis System for Rare Diseases

Recently, to speed up the differential-diagnosis process based on symptoms and signs observed from an affected individual in the diagnosis of rare diseases, researchers have developed and implemented phenotype-driven differential-diagnosis systems. The performance of those systems relies on the quantity and quality of underlying databases of disease-phenotype associations (DPAs). Although such databases are often developed by manual curation, they inherently suffer from limited coverage. To address this problem, we propose a text-mining approach to increase the coverage of DPA databases and consequently improve the performance of differential-diagnosis systems. Our analysis showed that a text-mining approach using one million case reports obtained from PubMed could increase the coverage of manually curated DPAs in Orphanet by 125.6%. We also present PubCaseFinder (see Web Resources), a new phenotype-driven differential-diagnosis system in a freely available web application. By utilizing automatically extracted DPAs from case reports in addition to manually curated DPAs, PubCaseFinder improves the performance of automated differential diagnosis. Moreover, PubCaseFinder helps clinicians search for relevant case reports by using phenotype-based comparisons and confirm the results with detailed contextual information.

SpainUDP: The Spanish Undiagnosed Rare Diseases Program

One of the IRDiRC goals for 2017–2027 is to achieve definitive diagnosis for rare undiagnosed diseases within one year, as delay in diagnosis remains one of the pending issues in the rare diseases field. The Spanish Undiagnosed Rare Diseases Program (SpainUDP) was created in response to this challenging scenario to cover patients’ needs and after seeing the success of the Undiagnosed Diseases Program (UDP) in the USA. SpainUDP offers a multidisciplinary approach to those patients who have long sought a diagnosis without any success. During the first phase of the protocol, undiagnosed cases are sent to SpainUDP by individual patients or families, patient organizations or hospitals. After careful analysis of phenotype, data from sequencing experiments (WES) is processed with a standard pipeline and detailed standardized phenotypic information (mapped to the Human Phenotype Ontology, HPO) is connected to genetic data. In addition, the participation of SpainUDP in international initiatives such as the European projects RD-Connect and Solve RD, the Undiagnosed Diseases Network International (UDNI), and the MatchMaker Exchange (MME) platform, allows the establishment of a global data sharing strategy across multiple projects submitting data to these international initiatives. From the official beginning of the program (at the end of 2015) until early 2018, 147 cases were accepted in SpainUDP. During this time, 37 cases (25%) dropped out the program due to several reasons. The remaining 110 cases are distributed as follows: phenotypic and genotypic (WES) characterization was finished in 30 cases, of which 20 (67%) were diagnosed; 21 cases are pending on variants’ validation by Sanger sequencing; in 25 cases, WES is ongoing and 34 cases are being studied for deep phenotypic characterization. In conclusion, SpainUDP aims to achieve a diagnosis following two recommendations of the IRDiRC: the patients’ diagnosis in as short a time as possible and the promotion of data sharing (especially genomic) at the international level.

[From symptom to syndrome using modern software support]

Diagnosing rare diseases can be challenging for clinicians. This article gives an overview on novel approaches, which enable automated phenotype-driven analyses of differential diagnoses for rare diseases as well as genomic variation data of affected individuals. The focus lies on reliable methods for collating clinical phenotypic data and new algorithms for precise and robust assessment of the similarity between phenotypic profiles. The Human Phenotype Ontology project (HPO; www.human-phenotype-ontology.org ) provides an ontology for collating symptoms and clinical phenotypic abnormalities. Using ontologies makes it possible to capture these data in a precise and comprehensive fashion as well as to apply reliable and robust automated analyses. Tools, such as the Phenomizer, enable the algorithmic calculation of similarity values amongst patients or between patients and disease descriptions. Such digital tools represent a solid foundation for differential diagnostic applications. Many rare diseases have a strong genetic component but the analysis of the coding DNA variants in rare disease patients is an enormously complex procedure, which often impedes successful molecular diagnostics. In this situation a combined analysis of the patients HPO-coded phenotypic features and the genomic characteristics of the variants can be of substantial help. In this case the HPO project and the associated algorithms are helpful: it is therefore an important component for phenotype-driven translational research and prioritization of disease-relavant genomic variations.

Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes

Integration of detailed phenotype information with genetic data is well established to facilitate accurate diagnosis of hereditary disorders. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from heterogeneous EHR narratives remains a challenge. Here, we present EHR-Phenolyzer, a high-throughput EHR framework for extracting and analyzing phenotypes. EHR-Phenolyzer extracts and normalizes Human Phenotype Ontology (HPO) concepts from EHR narratives and then prioritizes genes with causal variants on the basis of the HPO-coded phenotype manifestations. We assessed EHR-Phenolyzer on 28 pediatric individuals with confirmed diagnoses of monogenic diseases and found that the genes with causal variants were ranked among the top 100 genes selected by EHR-Phenolyzer for 16/28 individuals (p < 2.2 × 10^-16), supporting the value of phenotype-driven gene prioritization in diagnostic sequence interpretation. To assess the generalizability, we replicated this finding on an independent EHR dataset of ten individuals with a positive diagnosis from a different institution. We then assessed the broader utility by examining two additional EHR datasets, including 31 individuals who were suspected of having a Mendelian disease and underwent different types of genetic testing and 20 individuals with positive diagnoses of specific Mendelian etiologies of chronic kidney disease from exome sequencing. Finally, through several retrospective case studies, we demonstrated how combined analyses of genotype data and deep phenotype data from EHRs can expedite genetic diagnoses. In summary, EHR-Phenolyzer leverages EHR narratives to automate phenotype-driven analysis of clinical exomes or genomes, facilitating the broader implementation of genomic medicine.

Harmonising phenomics information for a better interoperability in the rare disease field

HIPBI-RD (Harmonising phenomics information for a better interoperability in the rare disease field) is a three-year project which started in 2016 funded via the E-Rare 3 ERA-NET program. This project builds on three resources largely adopted by the rare disease (RD) community: Orphanet, its ontology ORDO (the Orphanet Rare Disease Ontology), HPO (the Human Phenotype Ontology) as well as PhenoTips software for the capture and sharing of structured phenotypic data for RD patients. Our project is further supported by resources developed by the European Bioinformatics Institute and the Garvan Institute. HIPBI-RD aims to provide the community with an integrated, RD-specific bioinformatics ecosystem that will harmonise the way phenomics information is stored in databases and patient files worldwide, and thereby contribute to interoperability. This ecosystem will consist of a suite of tools and ontologies, optimized to work together, and made available through commonly used software repositories. The project workplan follows three main objectives: The HIPBI-RD ecosystem will contribute to the interpretation of variants identified through exome and full genome sequencing by harmonising the way phenotypic information is collected, thus improving diagnostics and delineation of RD. The ultimate goal of HIPBI-RD is to provide a resource that will contribute to bridging genome-scale biology and a disease-centered view on human pathobiology. Achievements in Year 1.

Paediatric genomics: diagnosing rare disease in children

The majority of rare diseases affect children, most of whom have an underlying genetic cause for their condition. However, making a molecular diagnosis with current technologies and knowledge is often still a challenge. Paediatric genomics is an immature but rapidly evolving field that tackles this issue by incorporating next-generation sequencing technologies, especially whole-exome sequencing and whole-genome sequencing, into research and clinical workflows. This complex multidisciplinary approach, coupled with the increasing availability of population genetic variation data, has already resulted in an increased discovery rate of causative genes and in improved diagnosis of rare paediatric disease. Importantly, for affected families, a better understanding of the genetic basis of rare disease translates to more accurate prognosis, management, surveillance and genetic advice; stimulates research into new therapies; and enables provision of better support.

Clinical validity of phenotype-driven analysis software PhenoVar as a diagnostic aid for clinical geneticists in the interpretation of whole-exome sequencing data

PURPOSE

We sought to determine the diagnostic yield of whole-exome sequencing (WES) combined with phenotype-driven analysis of variants in patients with suspected genetic disorders.

METHODS

WES was performed on a cohort of 51 patients presenting dysmorphisms with or without neurodevelopmental disorders of undetermined etiology. For each patient, a clinical geneticist reviewed the phenotypes and used the phenotype-driven analysis software PhenoVar (http://phenovar.med.usherbrooke.ca/) to analyze WES variants. The prioritized list of potential diagnoses returned was reviewed by the clinical geneticist, who selected candidate variants to be confirmed by segregation analysis. Conventional analysis of the individual variants was performed in parallel. The resulting candidate variants were subsequently reviewed by the same geneticist, to identify any additional potential diagnoses.

RESULTS

A molecular diagnosis was identified in 35% of the patients using the conventional analysis, and 17 of these 18 diagnoses were independently identified using PhenoVar. The only diagnosis initially missed by PhenoVar was rescued when the optional "minimal phenotypic cutoff" filter was omitted. PhenoVar reduced by half the number of potential diagnoses per patient compared with the conventional analysis.

CONCLUSION

Phenotype-driven software prioritizes WES variants, provides an efficient diagnostic aid to clinical geneticists and laboratories, and should be incorporated in clinical practice.

Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability

Background

Massively parallel genetic sequencing allows rapid testing of known intellectual disability (ID) genes. However, the discovery of novel syndromic ID genes requires molecular confirmation in at least a second or a cluster of individuals with an overlapping phenotype or similar facial gestalt. Using computer face-matching technology we report an automated approach to matching the faces of non-identical individuals with the same genetic syndrome within a database of 3681 images [1600 images of one of 10 genetic syndrome subgroups together with 2081 control images]. Using the leave-one-out method, two research questions were specified:

Using two-dimensional (2D) photographs of individuals with one of 10 genetic syndromes within a database of images, did the technology correctly identify more than expected by chance: i) a top match? ii) at least one match within the top five matches? or iii) at least one in the top 10 with an individual from the same syndrome subgroup?

Was there concordance between correct technology-based matches and whether two out of three clinical geneticists would have considered the diagnosis based on the image alone?

Results

The computer face-matching technology correctly identifies a top match, at least one correct match in the top five and at least one in the top 10 more than expected by chance (P < 0.00001). There was low agreement between the technology and clinicians, with higher accuracy of the technology when results were discordant (P < 0.01) for all syndromes except Kabuki syndrome.

Conclusions

Although the accuracy of the computer face-matching technology was tested on images of individuals with known syndromic forms of intellectual disability, the results of this pilot study illustrate the potential utility of face-matching technology within deep phenotyping platforms to facilitate the interpretation of DNA sequencing data for individuals who remain undiagnosed despite testing the known developmental disorder genes.

Electronic supplementary material

The online version of this article (10.1186/s12896-017-0410-1) contains supplementary material, which is available to authorized users.

Whole-exome sequencing is a valuable diagnostic tool for inherited peripheral neuropathies: Outcomes from a cohort of 50 families

The inherited peripheral neuropathies (IPNs) are characterized by marked clinical and genetic heterogeneity and include relatively frequent presentations such as Charcot-Marie-Tooth disease and hereditary motor neuropathy, as well as more rare conditions where peripheral neuropathy is associated with additional features. There are over 250 genes known to cause IPN-related disorders but it is estimated that in approximately 50% of affected individuals a molecular diagnosis is not achieved. In this study, we examine the diagnostic utility of whole-exome sequencing (WES) in a cohort of 50 families with 1 or more affected individuals with a molecularly undiagnosed IPN with or without additional features. Pathogenic or likely pathogenic variants in genes known to cause IPN were identified in 24% (12/50) of the families. A further 22% (11/50) of families carried sequence variants in IPN genes in which the significance remains unclear. An additional 12% (6/50) of families had variants in novel IPN candidate genes, 3 of which have been published thus far as novel discoveries (KIF1A, TBCK, and MCM3AP). This study highlights the use of WES in the molecular diagnostic approach of highly heterogeneous disorders, such as IPNs, places it in context of other published neuropathy cohorts, while further highlighting associated benefits for discovery.

Rare genetic diseases: update on diagnosis, treatment and online resources

Rare genetic diseases collectively impact a significant portion of the world's population. For many diseases there is limited information available, and clinicians can find difficulty in differentiating between clinically similar conditions. This leads to problems in genetic counseling and patient treatment. The biomedical market is affected because pharmaceutical and biotechnology industries do not see advantages in addressing rare disease treatments, or because the cost of the treatments is too high. By contrast, technological advances including DNA sequencing and analysis, together with computer-aided tools and online resources, are allowing a more thorough understanding of rare disorders. Here, we discuss how the collection of various types of information together with the use of new technologies is facilitating diagnosis and, consequently, treatment of rare diseases.

Rare Diseases: Drug Discovery and Informatics Resource

A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers' awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.

Matchmaker Exchange

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

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.

Model Organisms Facilitate Rare Disease Diagnosis and Therapeutic Research

Efforts to identify the genetic underpinnings of rare undiagnosed diseases increasingly involve the use of next-generation sequencing and comparative genomic hybridization methods. These efforts are limited by a lack of knowledge regarding gene function, and an inability to predict the impact of genetic variation on the encoded protein function. Diagnostic challenges posed by undiagnosed diseases have solutions in model organism research, which provides a wealth of detailed biological information. Model organism geneticists are by necessity experts in particular genes, gene families, specific organs, and biological functions. Here, we review the current state of research into undiagnosed diseases, highlighting large efforts in North America and internationally, including the Undiagnosed Diseases Network (UDN) (Supplemental Material, File S1) and UDN International (UDNI), the Centers for Mendelian Genomics (CMG), and the Canadian Rare Diseases Models and Mechanisms Network (RDMM). We discuss how merging human genetics with model organism research guides experimental studies to solve these medical mysteries, gain new insights into disease pathogenesis, and uncover new therapeutic strategies.

"Matching" consent to purpose: The example of the Matchmaker Exchange

The Matchmaker Exchange (MME) connects rare disease clinicians and researchers to facilitate the sharing of data from undiagnosed patients for the purpose of novel gene discovery. Such sharing raises the odds that two or more similar patients with candidate genes in common may be found, thereby allowing their condition to be more readily studied and understood. Consent considerations for data sharing in MME included both the ethical and legal differences between clinical and research settings and the level of privacy risk involved in sharing varying amounts of rare disease patient data to enable patient matches. In this commentary, we discuss these consent considerations and the resulting MME Consent Policy as they may be relevant to other international data sharing initiatives.

WDR26 Haploinsufficiency Causes a Recognizable Syndrome of Intellectual Disability, Seizures, Abnormal Gait, and Distinctive Facial Features

We report 15 individuals with de novo pathogenic variants in WDR26. Eleven of the individuals carry loss-of-function mutations, and four harbor missense substitutions. These 15 individuals comprise ten females and five males, and all have intellectual disability with delayed speech, a history of febrile and/or non-febrile seizures, and a wide-based, spastic, and/or stiff-legged gait. These subjects share a set of common facial features that include a prominent maxilla and upper lip that readily reveal the upper gingiva, widely spaced teeth, and a broad nasal tip. Together, these features comprise a recognizable facial phenotype. We compared these features with those of chromosome 1q41q42 microdeletion syndrome, which typically contains WDR26, and noted that clinical features are consistent between the two subsets, suggesting that haploinsufficiency of WDR26 contributes to the pathology of 1q41q42 microdeletion syndrome. Consistent with this, WDR26 loss-of-function single-nucleotide mutations identified in these subjects lead to nonsense-mediated decay with subsequent reduction of RNA expression and protein levels. We derived a structural model of WDR26 and note that missense variants identified in these individuals localize to highly conserved residues of this WD-40-repeat-containing protein. Given that WDR26 mutations have been identified in ∼1 in 2,000 of subjects in our clinical cohorts and that WDR26 might be poorly annotated in exome variant-interpretation pipelines, we would anticipate that this disorder could be more common than currently appreciated.

Defining Disease, Diagnosis, and Translational Medicine within a Homeostatic Perturbation Paradigm: The National Institutes of Health Undiagnosed Diseases Program Experience

Traditionally, the use of genomic information for personalized medical decisions relies on prior discovery and validation of genotype-phenotype associations. This approach constrains care for patients presenting with undescribed problems. The National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP) hypothesized that defining disease as maladaptation to an ecological niche allows delineation of a logical framework to diagnose and evaluate such patients. Herein, we present the philosophical bases, methodologies, and processes implemented by the NIH UDP. The NIH UDP incorporated use of the Human Phenotype Ontology, developed a genomic alignment strategy cognizant of parental genotypes, pursued agnostic biochemical analyses, implemented functional validation, and established virtual villages of global experts. This systematic approach provided a foundation for the diagnostic or non-diagnostic answers provided to patients and serves as a paradigm for scalable translational research.

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.

Semantic prioritization of novel causative genomic variants

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

We address the problem of how to distinguish which of the many thousands of DNA sequence variants carried by an individual with a rare disease is responsible for the disease phenotypes. This can help clinicians arrive at a diagnosis, but also can be instrumental in improving our understanding of the pathobiology of the disease. Many methods are currently available to help with the problem of determining causative variant, using information about evolutionary conservation and prediction of the functional consequences of the sequence variant. We have developed a novel algorithm (PVP) which augments existing strategies by using the similarity of the patients phenotype to known phenotype-genotype data in human and model organism databases to further rank potential candidate genes. In a retrospective study, we apply PVP to the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism, and find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

[Diagnostics in human genetics : Integration of phenotypic and genomic data]

The development of reliable methods for annotation of clinical phenotypes and algorithms to calculate similarity values for clinical phenotype profiles will be a major challenge for genomic personalized medicine, since combined analysis of phenotypic features and genetic variants can increase diagnostic yield, especially with exome or genome sequencing. The Human Phenotype Ontology project (HPO; www.human-phenotype-ontology.org ) provides an ontology for capturing phenotypic abnormalities in human disease in a precise and comprehensive fashion. The HPO not only enables reliable integration of disease-relevant information from numerous databases, but it also allows for similarity between patients or between patients and disease descriptions to be calculated algorithmically. The HPO thereby represents a solid foundation for differential diagnostic applications as well as for translational research and prioritization of novel disease genes in exome or genome sequencing projects.

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.

Mutations in EXTL3 Cause Neuro-immuno-skeletal Dysplasia Syndrome

EXTL3 regulates the biosynthesis of heparan sulfate (HS), important for both skeletal development and hematopoiesis, through the formation of HS proteoglycans (HSPGs). By whole-exome sequencing, we identified homozygous missense mutations c.1382C>T, c.1537C>T, c.1970A>G, and c.2008T>G in EXTL3 in nine affected individuals from five unrelated families. Notably, we found the identical homozygous missense mutation c.1382C>T (p.Pro461Leu) in four affected individuals from two unrelated families. Affected individuals presented with variable skeletal abnormalities and neurodevelopmental defects. Severe combined immunodeficiency (SCID) with a complete absence of T cells was observed in three families. EXTL3 was most abundant in hematopoietic stem cells and early progenitor T cells, which is in line with a SCID phenotype at the level of early T cell development in the thymus. To provide further support for the hypothesis that mutations in EXTL3 cause a neuro-immuno-skeletal dysplasia syndrome, and to gain insight into the pathogenesis of the disorder, we analyzed the localization of EXTL3 in fibroblasts derived from affected individuals and determined glycosaminoglycan concentrations in these cells as well as in urine and blood. We observed abnormal glycosaminoglycan concentrations and increased concentrations of the non-sulfated chondroitin disaccharide D0a0 and the disaccharide D0a4 in serum and urine of all analyzed affected individuals. In summary, we show that biallelic mutations in EXTL3 disturb glycosaminoglycan synthesis and thus lead to a recognizable syndrome characterized by variable expression of skeletal, neurological, and immunological abnormalities.

PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations

Cross-sectional phenotype studies are used by genetics researchers to better understand how phenotypes vary across patients with genetic diseases, both within and between cohorts. Analyses within cohorts identify patterns between phenotypes and patients (e.g., co-occurrence) and isolate special cases (e.g., potential outliers). Comparing the variation of phenotypes between two cohorts can help distinguish how different factors affect disease manifestation (e.g., causal genes, age of onset, etc.). PhenoStacks is a novel visual analytics tool that supports the exploration of phenotype variation within and between cross-sectional patient cohorts. By leveraging the semantic hierarchy of the Human Phenotype Ontology, phenotypes are presented in context, can be grouped and clustered, and are summarized via overviews to support the exploration of phenotype distributions. The design of PhenoStacks was motivated by formative interviews with genetics researchers: we distil high-level tasks, present an algorithm for simplifying ontology topologies for visualization, and report the results of a deployment evaluation with four expert genetics researchers. The results suggest that PhenoStacks can help identify phenotype patterns, investigate data quality issues, and inform data collection design.

Undiagnosed Diseases: Italy-US Collaboration and International Efforts to Tackle Rare and Common Diseases Lacking a Diagnosis

Rare diseases (RD), according to European Union criteria, affect 5 per 10,000 persons, or 30 million people, in the EU; in the USA, RD are defined as conditions that affect fewer than 200,000 individuals in the population (320 million). Most known rare disorders are severe and chronic, with many being degenerative and life threatening. There are roughly 5000-8000 rare diseases (European Commission, DG Health and Food Safety, Public Health, Rare Diseases, Policy.http://ec.europa.eu/health/rare_diseases/policy/index_en.htm. Accessed 19 December 2016; NORD-The National Organization for Rare Diseases: https://rarediseases.org/). Patient populations for individual RD are small and scattered; international collaborations are crucial to pool resources fragmented across individual countries for better diagnosis and treatment. Undiagnosed RD (URD) are conditions that elude diagnosis; some patients wait years for a definitive diagnosis. URD may include groups of unnamed disorders with common characteristics, phenotypically well described diseases, diseases with an unknown molecular basis, or those due to unknown, non-genetic factors.The US NIH Undiagnosed Diseases Program arose in 2008 to provide a diagnosis for individuals who had long sought one without success; in 2013 a nationwide Undiagnosed Diseases Network was established in the United States. In 2015, the Undiagnosed Disease Network International (UDNI) was established and includes US, Australia, Canada, Japan, Italy and other European countries. Other national initiatives have also been undertaken and are in progress all over the world.

Improved Diagnosis and Care for Rare Diseases through Implementation of Precision Public Health Framework

Public health relies on technologies to produce and analyse data, as well as effectively develop and implement policies and practices. An example is the public health practice of epidemiology, which relies on computational technology to monitor the health status of populations, identify disadvantaged or at risk population groups and thereby inform health policy and priority setting. Critical to achieving health improvements for the underserved population of people living with rare diseases is early diagnosis and best care. In the rare diseases field, the vast majority of diseases are caused by destructive but previously difficult to identify protein-coding gene mutations. The reduction in cost of genetic testing and advances in the clinical use of genome sequencing, data science and imaging are converging to provide more precise understandings of the 'person-time-place' triad. That is: who is affected (people); when the disease is occurring (time); and where the disease is occurring (place). Consequently we are witnessing a paradigm shift in public health policy and practice towards 'precision public health'.Patient and stakeholder engagement has informed the need for a national public health policy framework for rare diseases. The engagement approach in different countries has produced highly comparable outcomes and objectives. Knowledge and experience sharing across the international rare diseases networks and partnerships has informed the development of the Western Australian Rare Diseases Strategic Framework 2015-2018 (RD Framework) and Australian government health briefings on the need for a National plan.The RD Framework is guiding the translation of genomic and other technologies into the Western Australian health system, leading to greater precision in diagnostic pathways and care, and is an example of how a precision public health framework can improve health outcomes for the rare diseases population.Five vignettes are used to illustrate how policy decisions provide the scaffolding for translation of new genomics knowledge, and catalyze transformative change in delivery of clinical services. The vignettes presented here are from an Australian perspective and are not intended to be comprehensive, but rather to provide insights into how a new and emerging 'precision public health' paradigm can improve the experiences of patients living with rare diseases, their caregivers and families.The conclusion is that genomic public health is informed by the individual and family needs, and the population health imperatives of an early and accurate diagnosis; which is the portal to best practice care. Knowledge sharing is critical for public health policy development and improving the lives of people living with rare diseases.

The Human Phenotype Ontology in 2017

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

'IRDiRC Recognized Resources': a new mechanism to support scientists to conduct efficient, high-quality research for rare diseases

The International Rare Diseases Research Consortium (IRDiRC) has created a quality label, 'IRDiRC Recognized Resources', formerly known as 'IRDiRC Recommended'. It is a peer-reviewed quality indicator process established based on the IRDiRC Policies and Guidelines to designate resources (ie, standards, guidelines, tools, and platforms) designed to accelerate the pace of discoveries and translation into clinical applications for the rare disease (RD) research community. In its first year of implementation, 13 resources successfully applied for this designation, each focused on key areas essential to IRDiRC objectives and to the field of RD research more broadly. These included data sharing for discovery, knowledge organisation and ontologies, networking patient registries, and therapeutic development. 'IRDiRC Recognized Resources' is a mechanism aimed to provide community-approved contributions to RD research higher visibility, and encourage researchers to adopt recognised standards, guidelines, tools, and platforms that facilitate research advances guided by the principles of interoperability and sharing.

Registered access: a 'Triple-A' approach

We propose a standard model for a novel data access tier – registered access – to facilitate access to data that cannot be published in open access archives owing to ethical and legal risk. Based on an analysis of applicable research ethics and other legal and administrative frameworks, we discuss the general characteristics of this Registered Access Model, which would comprise a three-stage approval process: Authentication, Attestation and Authorization. We are piloting registered access with the Demonstration Projects of the Global Alliance for Genomics and Health for which it may provide a suitable mechanism for access to certain data types and to different types of data users.

Explorations to improve the completeness of exome sequencing

BACKGROUND

Exome sequencing has advanced to clinical practice and proven useful for obtaining molecular diagnoses in rare diseases. In approximately 75 % of cases, however, a clinical exome study does not produce a definitive molecular diagnosis. These residual cases comprise a new diagnostic challenge for the genetics community. The Undiagnosed Diseases Program of the National Institutes of Health routinely utilizes exome sequencing for refractory clinical cases. Our preliminary data suggest that disease-causing variants may be missed by current standard-of-care clinical exome analysis. Such false negatives reflect limitations in experimental design, technical performance, and data analysis.

RESULTS

We present examples from our datasets to quantify the analytical performance associated with current practices, and explore strategies to improve the completeness of data analysis. In particular, we focus on patient ascertainment, exome capture, inclusion of intronic variants, and evaluation of medium-sized structural variants.

CONCLUSIONS

The strategies we present may recover previously-missed, disease causing variants in second-pass exome analysis. Understanding the limitations of the current clinical exome search space provides a rational basis to improve methods for disease variant detection using genome-scale sequencing techniques.

State of rare disease management in Southeast Asia

Background

Rare diseases, also referred to as orphan diseases, are characterised by their low prevalence with majority of them are chronically debilitating and life threatening. Given the low prevalence and the widely dispersed but very small patient base for each disease, there may often be a disproportion in the availability of treatments and resources to manage patients, spur research and train experts. This is especially true in Southeast Asian countries that are currently in the process of implementing or revising their universal health coverage schemes. This paper aims to examine the status of rare disease management in Southeast Asian countries. It will serve as the basis for a more active discussion on how countries in the region can address an under-recognised rare disease burden and enhance national and regional capacities.

Methods

The study consists of literature reviews and key stakeholders interviews in six focus countries, including the Philippines, Singapore, Malaysia, Indonesia, Vietnam, and Thailand and five countries as best practice, comprising of France, Canada, Australia, Taiwan, and South Korea. Rare disease management initiatives across each country were examined based on the World Health Organization’s framework for action in strengthening health systems.

Results

The results suggest rare disease management remains challenging across Southeast Asia, as many of the focus countries face fundamental issues from basic healthcare systems to funding. Nonetheless, there are substantial improvement opportunities, including leveraging best practices from around the world and organising a multi-stakeholder and regional approach and strategy.

Conclusions

Southeast Asian countries have made significant progress in the management of rare disease, but there remain key areas for substantial development opportunities.

The Current Landscape of Genetic Testing in Cardiovascular Malformations: Opportunities and Challenges

Human cardiovascular malformations (CVMs) frequently have a genetic contribution. Through the application of novel technologies, such as next-generation sequencing, DNA sequence variants associated with CVMs are being identified at a rapid pace. While clinicians are now able to offer testing with NGS gene panels or whole exome sequencing to any patient with a CVM, the interpretation of genetic variation remains problematic. Variable phenotypic expression, reduced penetrance, inconsistent phenotyping methods, and the lack of high-throughput functional testing of variants contribute to these challenges. This article elaborates critical issues that impact the decision to broadly implement clinical molecular genetic testing in CVMs. Major benefits of testing include establishing a genetic diagnosis, facilitating cost-effective screening of family members who may have subclinical disease, predicting recurrence risk in offsprings, enabling early diagnosis and anticipatory management of CV and non-CV disease phenotypes, predicting long-term outcomes, and facilitating the development of novel therapies aimed at disease improvement or prevention. Limitations include financial cost, psychosocial cost, and ambiguity of interpretation of results. Multiplex families and patients with syndromic features are two groups where disease causation could potentially be firmly established. However, these account for the minority of the overall CVM population, and there is increasing recognition that genotypes previously associated with syndromes also exist in patients who lack non-CV findings. In all circumstances, ongoing dialog between cardiologists and clinical geneticists will be needed to accurately interpret genetic testing and improve these patients’ health. This may be most effectively implemented by the creation and support of CV genetics services at centers committed to pursuing testing for patients.

The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles

Despite the increasing prevalence of clinical sequencing, the difficulty of identifying additional affected families is a key obstacle to solving many rare diseases. There may only be a handful of similar patients worldwide, and their data may be stored in diverse clinical and research databases. Computational methods are necessary to enable finding similar patients across the growing number of patient repositories and registries. We present the Matchmaker Exchange Application Programming Interface (MME API), a protocol and data format for exchanging phenotype and genotype profiles to enable matchmaking among patient databases, facilitate the identification of additional cohorts, and increase the rate with which rare diseases can be researched and diagnosed. We designed the API to be straightforward and flexible in order to simplify its adoption on a large number of data types and workflows. We also provide a public test data set, curated from the literature, to facilitate implementation of the API and development of new matching algorithms. The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously identified matches and generate several new leads currently being validated. The API is available at https://github.com/ga4gh/mme-apis.

Novel bioinformatic developments for exome sequencing

With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.

A Clinician's perspective on clinical exome sequencing

Clinical exome sequencing has clearly improved our ability as clinicians to identify the cause of a wide variety of disorders. Prior to exome sequencing, a majority of patients with apparent syndromes never received a specific molecular genetic diagnosis despite extensive diagnostic odysseys. Even for those receiving an answer to the question of what caused their disorder, the diagnostic odyssey often spanned years to decades. Determining the particular genetic cause in an individual patient can be challenging due to inherent phenotypic and genetic heterogeneity of disease, technical limitations of testing or both. Blended phenotypes, due to multiple monogenic disorders in the same patient, are true dilemmas for traditional genetic evaluations, but are increasingly being diagnosed through clinical exome sequencing. New sequencing technologies have increased the proportion of patients receiving molecular diagnoses, while significantly shortening the time scale, providing multiple benefits for the health-care team, patient and family.

Novel bioinformatic developments for exome sequencing

With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.

Gene discovery for Mendelian conditions via social networking: de novo variants in KDM1A cause developmental delay and distinctive facial features

PURPOSE

The pace of Mendelian gene discovery is slowed by the "n-of-1 problem"-the difficulty of establishing the causality of a putatively pathogenic variant in a single person or family. Identification of an unrelated person with an overlapping phenotype and suspected pathogenic variant in the same gene can overcome this barrier, but it is often impeded by lack of a convenient or widely available way to share data on candidate variants/genes among families, clinicians, and researchers.

METHODS

Social networking among families, clinicians, and researchers was used to identify three children with variants of unknown significance in KDM1A and similar phenotypes.

RESULTS

De novo variants in KDM1A underlie a new syndrome characterized by developmental delay and distinctive facial features.

CONCLUSION

Social networking is a potentially powerful strategy to discover genes for rare Mendelian conditions, particularly those with nonspecific phenotypic features. To facilitate the efforts of families to share phenotypic and genomic information with each other, clinicians, and researchers, we developed the Repository for Mendelian Genomics Family Portal (RMD-FP; http://uwcmg.org/#/family). Design and development of MyGene2 (http://www.mygene2.org), a Web-based tool that enables families, clinicians, and researchers to search for gene matches based on analysis of phenotype and exome data deposited into the RMD-FP, is under way.Genet Med 18 8, 788-795.

Undiagnosed Diseases Network International (UDNI): White paper for global actions to meet patient needs

In 2008, the National Institutes of Health's (NIH) Undiagnosed Disease Program (UDP) was initiated to provide diagnoses for individuals who had long sought one without success. As a result of two international conferences (Rome 2014 and Budapest 2015), the Undiagnosed Diseases Network International (UDNI) was established, modeled in part after the NIH UDP. Undiagnosed diseases are a global health issue, calling for an international scientific and healthcare effort. To meet this demand, the UDNI has built a consensus framework of principles, best practices and governance; the Board of Directors reflects its international character, as it includes experts from Australia, Canada, Hungary, Italy, Japan and the USA. The UDNI involves centers with internationally recognized expertise, and its scientific resources and know-how aim to fill the knowledge gaps that impede diagnosis. Consequently, the UDNI fosters the translation of research into medical practice. Active patient involvement is critical; the Patient Advisory Group is expected to play an increasing role in UDNI activities. All information for physicians and patients will be available at the UDNI website.

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.