A Diagnosis for All Rare Genetic Diseases: The Horizon and the Next Frontiers

Genetics of inherited peripheral neuropathies and the next frontier: looking backwards to progress forwards

Inherited peripheral neuropathies (IPNs) encompass a clinically and genetically heterogeneous group of disorders causing length-dependent degeneration of peripheral autonomic, motor and/or sensory nerves. Despite gold-standard diagnostic testing for pathogenic variants in over 100 known associated genes, many patients with IPN remain genetically unsolved. Providing patients with a diagnosis is critical for reducing their 'diagnostic odyssey', improving clinical care, and for informed genetic counselling. The last decade of massively parallel sequencing technologies has seen a rapid increase in the number of newly described IPN-associated gene variants contributing to IPN pathogenesis. However, the scarcity of additional families and functional data supporting variants in potential novel genes is prolonging patient diagnostic uncertainty and contributing to the missing heritability of IPNs. We review the last decade of IPN disease gene discovery to highlight novel genes, structural variation and short tandem repeat expansions contributing to IPN pathogenesis. From the lessons learnt, we provide our vision for IPN research as we anticipate the future, providing examples of emerging technologies, resources and tools that we propose that will expedite the genetic diagnosis of unsolved IPN families.

Genetic aspects of ataxias in a cohort of Turkish patients

INTRODUCTION

Ataxia is one of the clinical findings of the movement disorder disease group. Although there are many underlying etiological reasons, genetic etiology has an increasing significance thanks to the recently developing technology. The aim of this study is to present the variants detected in WES analysis excluding non-genetic causes, in patients with ataxia.

METHODS

Thirty-six patients who were referred to us with findings of ataxia and diagnosed through WES or other molecular genetic analysis methods were included in our study. At the same time, information such as the onset time of the complaints, consanguinity status between parents, and the presence of relatives with similar symptoms were evaluated. If available, the patient's biochemical and radiological test results were presented.

RESULTS

Thirty-six patients were diagnosed through WES or CES. The rate of detected autosomal recessive inheritance disease was 80.5%, while that of autosomal dominant inheritance disease was 19.5%. Abnormal cerebellum was detected on brain MRI images in 26 patients, while polyneuropathy was detected on EMG in eleven of them. While the majority of the patients were compatible with similar cases reported in the literature, five patients had different/additional features (variants in MCM3AP, AGTPBP1, GDAP2, and SH3TC2 genes).

CONCLUSIONS

The diagnosis of ataxia patients with unknown etiology is made possible thanks to these clues. Consideration of a genetic approach is recommended in patients with ataxia of unknown etiology.

Consensus reporting guidelines to address gaps in descriptions of ultra-rare genetic conditions

Genome-wide sequencing and genetic matchmaker services are propelling a new era of genotype-driven ascertainment of novel genetic conditions. The degree to which reported phenotype data in discovery-focused studies address informational priorities for clinicians and families is unclear. We identified reports published from 2017 to 2021 in 10 genetics journals of novel Mendelian disorders. We adjudicated the quality and detail of the phenotype data via 46 questions pertaining to six priority domains: (I) Development, cognition, and mental health; (II) Feeding and growth; (III) Medication use and treatment history; (IV) Pain, sleep, and quality of life; (V) Adulthood; and (VI) Epilepsy. For a subset of articles, all subsequent published follow-up case descriptions were identified and assessed in a similar manner. A modified Delphi approach was used to develop consensus reporting guidelines, with input from content experts across four countries. In total, 200 of 3243 screened publications met inclusion criteria. Relevant phenotypic details across each of the 6 domains were rated superficial or deficient in >87% of papers. For example, less than 10% of publications provided details regarding neuropsychiatric diagnoses and "behavioural issues", or about the type/nature of feeding problems. Follow-up reports (n = 95) rarely contributed this additional phenotype data. In summary, phenotype information relevant to clinical management, genetic counselling, and the stated priorities of patients and families is lacking for many newly described genetic diseases. The PHELIX (PHEnotype LIsting fiX) reporting guideline checklists were developed to improve phenotype reporting in the genomic era.

Impact of deep phenotyping: high diagnostic yield in a diverse pediatric population of 172 patients through clinical whole-genome sequencing at a single center

Background: Pediatric patients with undiagnosed conditions, particularly those suspected of having Mendelian genetic disorders, pose a significant challenge in healthcare. This study investigates the diagnostic yield of whole-genome sequencing (WGS) in a pediatric cohort with diverse phenotypes, particularly focusing on the role of clinical expertise in interpreting WGS results. Methods: A retrospective cohort study was conducted at Acibadem University's Maslak Hospital in Istanbul, Turkey, involving pediatric patients (0-18 years) who underwent diagnostic WGS testing. Clinical assessments, family histories, and previous laboratory and imaging studies were analyzed. Variants were classified and interpreted in conjunction with clinical findings. Results: The cohort comprised 172 pediatric patients, aged 0-5 years (62.8%). International patients (28.5%) were from 20 different countries. WGS was used as a first-tier approach in 61.6% of patients. The diagnostic yield of WGS reached 61.0%, enhanced by reclassification of variants of uncertain significance (VUS) through reverse phenotyping by an experienced clinical geneticist. Consanguinity was 18.6% of the overall cohort. Dual diagnoses were carried out for 8.5% of solved patients. Discussion: Our study particularly advocates for the selection of WGS as a first-tier testing approach in infants and children with rare diseases, who were under 5 years of age, thereby potentially shortening the duration of the diagnostic odyssey. The results also emphasize the critical role of a single clinical geneticist's expertise in deep phenotyping and reverse phenotyping, which contributed significantly to the high diagnostic yield.

Long read sequencing on its way to the routine diagnostics of genetic diseases

The clinical application of technological progress in the identification of DNA alterations has always led to improvements of diagnostic yields in genetic medicine. At chromosome side, from cytogenetic techniques evaluating number and gross structural defects to genomic microarrays detecting cryptic copy number variants, and at molecular level, from Sanger method studying the nucleotide sequence of single genes to the high-throughput next-generation sequencing (NGS) technologies, resolution and sensitivity progressively increased expanding considerably the range of detectable DNA anomalies and alongside of Mendelian disorders with known genetic causes. However, particular genomic regions (i.e., repetitive and GC-rich sequences) are inefficiently analyzed by standard genetic tests, still relying on laborious, time-consuming and low-sensitive approaches (i.e., southern-blot for repeat expansion or long-PCR for genes with highly homologous pseudogenes), accounting for at least part of the patients with undiagnosed genetic disorders. Third generation sequencing, generating long reads with improved mappability, is more suitable for the detection of structural alterations and defects in hardly accessible genomic regions. Although recently implemented and not yet clinically available, long read sequencing (LRS) technologies have already shown their potential in genetic medicine research that might greatly impact on diagnostic yield and reporting times, through their translation to clinical settings. The main investigated LRS application concerns the identification of structural variants and repeat expansions, probably because techniques for their detection have not evolved as rapidly as those dedicated to single nucleotide variants (SNV) identification: gold standard analyses are karyotyping and microarrays for balanced and unbalanced chromosome rearrangements, respectively, and southern blot and repeat-primed PCR for the amplification and sizing of expanded alleles, impaired by limited resolution and sensitivity that have not been significantly improved by the advent of NGS. Nevertheless, more recently, with the increased accuracy provided by the latest product releases, LRS has been tested also for SNV detection, especially in genes with highly homologous pseudogenes and for haplotype reconstruction to assess the parental origin of alleles with de novo pathogenic variants. We provide a review of relevant recent scientific papers exploring LRS potential in the diagnosis of genetic diseases and its potential future applications in routine genetic testing.

DAG1 haploinsufficiency is associated with sporadic and familial isolated or pauci-symptomatic hyperCKemia

DAG1 encodes for dystroglycan, a key component of the dystrophin-glycoprotein complex (DGC) with a pivotal role in skeletal muscle function and maintenance. Biallelic loss-of-function DAG1 variants cause severe muscular dystrophy and muscle-eye-brain disease. A possible contribution of DAG1 deficiency to milder muscular phenotypes has been suggested. We investigated the genetic background of twelve subjects with persistent mild-to-severe hyperCKemia to dissect the role of DAG1 in this condition. Genetic testing was performed through exome sequencing (ES) or custom NGS panels including various genes involved in a spectrum of muscular disorders. Histopathological and Western blot analyses were performed on muscle biopsy samples obtained from three patients. We identified seven novel heterozygous truncating variants in DAG1 segregating with isolated or pauci-symptomatic hyperCKemia in all families. The variants were rare and predicted to lead to nonsense-mediated mRNA decay or the formation of a truncated transcript. In four cases, DAG1 variants were inherited from similarly affected parents. Histopathological analysis revealed a decreased expression of dystroglycan subunits and Western blot confirmed a significantly reduced expression of beta-dystroglycan in muscle samples. This study supports the pathogenic role of DAG1 haploinsufficiency in isolated or pauci-symptomatic hyperCKemia, with implications for clinical management and genetic counseling.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

BACKGROUND

Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.

METHODS

By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.

RESULTS

A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.

CONCLUSIONS

The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for over 99.8% of all possible coding single nucleotide variants and resolved 310 clinically reported variants of uncertain significance with high confidence, enhancing clinical variant interpretation in dystroglycanopathies. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.

International Undiagnosed Diseases Programs (UDPs): components and outcomes

Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.

Skewed X-chromosome inactivation in unsolved neurodevelopmental disease cases can guide re-evaluation For X-linked genes

Despite major advances in genome technology and analysis, >50% of patients with a neurodevelopmental disorder (NDD) remain undiagnosed after extensive evaluation. A point in case is our clinically heterogeneous cohort of NDD patients that remained undiagnosed after FRAXA testing, chromosomal microarray analysis and trio exome sequencing (ES). In this study, we explored the frequency of non-random X chromosome inactivation (XCI) in the mothers of male patients and affected females, the rationale being that skewed XCI might be masking previously discarded genetic variants found on the X chromosome. A multiplex fluorescent PCR-based assay was used to analyse the pattern of XCI after digestion with HhaI methylation-sensitive restriction enzyme. In families with skewed XCI, we re-evaluated trio-based ES and identified pathogenic variants and a deletion on the X chromosome. Linkage analysis and RT-PCR were used to further study the inactive X chromosome allele, and Xdrop long-DNA technology was used to define chromosome deletion boundaries. We found skewed XCI (>90%) in 16/186 (8.6%) mothers of NDD males and in 12/90 (13.3%) NDD females, far beyond the expected rate of XCI in the normal population (3.6%, OR = 4.10; OR = 2.51). By re-analyzing ES and clinical data, we solved 7/28 cases (25%) with skewed XCI, identifying variants in KDM5C, PDZD4, PHF6, TAF1, OTUD5 and ZMYM3, and a deletion in ATRX. We conclude that XCI profiling is a simple assay that targets a subgroup of patients that can benefit from re-evaluation of X-linked variants, thus improving the diagnostic yield in NDD patients and identifying new X-linked disorders.

Exome and genome sequencing for rare genetic disease diagnosis: A scoping review and critical appraisal of clinical guidance documents produced by genetics professional organizations

PURPOSE

Exome and genome sequencing have rapidly transitioned from research methods to widely used clinical tests for diagnosing rare genetic diseases. We sought to synthesize the topics covered and appraise the development processes of clinical guidance documents generated by genetics professional organizations.

METHODS

We conducted a scoping review of guidance documents published since 2010, systematically identified in peer-reviewed and gray literature, using established methods and reporting guidelines. We coded verbatim recommendations by topic using content analysis and critically appraised documents using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool.

RESULTS

We identified 30 guidance documents produced by 8 organizations (2012-2022), yielding 611 recommendations covering 21 topics. The most common topic related to findings beyond the primary testing indication. Mean AGREE II scores were low across all 6 quality domains; scores for items related to rigor of development were among the lowest. More recently published documents generally received higher scores.

CONCLUSION

Guidance documents included a broad range of recommendations but were of low quality, particularly in their rigor of development. Developers should consider using tools such as AGREE II and basing recommendations on living knowledge syntheses to improve guidance development in this evolving space.

Genome sequencing and comprehensive rare-variant analysis of 465 families with neurodevelopmental disorders

Despite significant progress in unraveling the genetic causes of neurodevelopmental disorders (NDDs), a substantial proportion of individuals with NDDs remain without a genetic diagnosis after microarray and/or exome sequencing. Here, we aimed to assess the power of short-read genome sequencing (GS), complemented with long-read GS, to identify causal variants in participants with NDD from the National Institute for Health and Care Research (NIHR) BioResource project. Short-read GS was conducted on 692 individuals (489 affected and 203 unaffected relatives) from 465 families. Additionally, long-read GS was performed on five affected individuals who had structural variants (SVs) in technically challenging regions, had complex SVs, or required distal variant phasing. Causal variants were identified in 36% of affected individuals (177/489), and a further 23% (112/489) had a variant of uncertain significance after multiple rounds of re-analysis. Among all reported variants, 88% (333/380) were coding nuclear SNVs or insertions and deletions (indels), and the remainder were SVs, non-coding variants, and mitochondrial variants. Furthermore, long-read GS facilitated the resolution of challenging SVs and invalidated variants of difficult interpretation from short-read GS. This study demonstrates the value of short-read GS, complemented with long-read GS, in investigating the genetic causes of NDDs. GS provides a comprehensive and unbiased method of identifying all types of variants throughout the nuclear and mitochondrial genomes in individuals with NDD.

Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases

Whole-Exome Sequencing (WES) has proven valuable in the characterization of underlying genetic defects in most rare diseases (RDs). Copy Number Variants (CNVs) were initially thought to escape detection. Recent technological advances enabled CNV calling from WES data with the use of accurate and highly sensitive bioinformatic tools. Amongst 920 patients referred for WES, 454 unresolved cases were further analysed using the ExomeDepth algorithm. CNVs were called, evaluated and categorized according to ACMG/ClinGen recommendations. Causative CNVs were identified in 40 patients, increasing the diagnostic yield of WES from 50.7% (466/920) to 55% (506/920). Twenty-two CNVs were available for validation and were all confirmed; of these, five were novel. Implementation of the ExomeDepth tool promoted effective identification of phenotype-relevant and/or novel CNVs. Among the advantages of calling CNVs from WES data, characterization of complex genotypes comprising both CNVs and SNVs minimizes cost and time to final diagnosis, while allowing differentiation between true or false homozygosity, as well as compound heterozygosity of variants in AR genes. The use of a specific algorithm for calling CNVs from WES data enables ancillary detection of different types of causative genetic variants, making WES a critical first-tier diagnostic test for patients with RDs.

Thermally controlled intein splicing of engineered DNA polymerases provides a robust and generalizable solution for accurate and sensitive molecular diagnostics

DNA polymerases are essential for nucleic acid synthesis, cloning, sequencing and molecular diagnostics technologies. Conditional intein splicing is a powerful tool for controlling enzyme reactions. We have engineered a thermal switch into thermostable DNA polymerases from two structurally distinct polymerase families by inserting a thermally activated intein domain into a surface loop that is integral to the polymerase active site, thereby blocking DNA or RNA template access. The fusion proteins are inactive, but retain their structures, such that the intein excises during a heat pulse delivered at 70-80°C to generate spliced, active polymerases. This straightforward thermal activation step provides a highly effective, one-component 'hot-start' control of PCR reactions that enables accurate target amplification by minimizing unwanted by-products generated by off-target reactions. In one engineered enzyme, derived from Thermus aquaticus DNA polymerase, both DNA polymerase and reverse transcriptase activities are controlled by the intein, enabling single-reagent amplification of DNA and RNA under hot-start conditions. This engineered polymerase provides high-sensitivity detection for molecular diagnostics applications, amplifying 5-6 copies of the tested DNA and RNA targets with >95% certainty. The design principles used to engineer the inteins can be readily applied to construct other conditionally activated nucleic acid processing enzymes.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Towards the international interoperability of clinical research networks for rare diseases: recommendations from the IRDiRC Task Force

BACKGROUND

Many patients with rare diseases are still lacking a timely diagnosis and approved therapies for their condition despite the tremendous efforts of the research community, biopharmaceutical, medical device industries, and patient support groups. The development of clinical research networks for rare diseases offers a tremendous opportunity for patients and multi-disciplinary teams to collaborate, share expertise, gain better understanding on specific rare diseases, and accelerate clinical research and innovation. Clinical Research Networks have been developed at a national or continental level, but global collaborative efforts to connect them are still lacking. The International Rare Diseases Research Consortium set a Task Force on Clinical Research Networks for Rare Diseases with the objective to analyse the structure and attributes of these networks and to identify the barriers and needs preventing their international collaboration. The Task Force created a survey and sent it to pre-identified clinical research networks located worldwide.

RESULTS

A total of 34 responses were received. The survey analysis demonstrated that clinical research networks are diverse in their membership composition and emphasize community partnerships including patient groups, health care providers and researchers. The sustainability of the networks is mostly supported by public funding. Activities and research carried out at the networks span the research continuum from basic to clinical to translational research studies. Key elements and infrastructures conducive to collaboration are well adopted by the networks, but barriers to international interoperability are clearly identified. These hurdles can be grouped into five categories: funding limitation; lack of harmonization in regulatory and contracting process; need for common tools and data standards; need for a governance framework and coordination structures; and lack of awareness and robust interactions between networks.

CONCLUSIONS

Through this analysis, the Task Force identified key elements that should support both developing and established clinical research networks for rare diseases in implementing the appropriate structures to achieve international interoperability worldwide. A global roadmap of actions and a specific research agenda, as suggested by this group, provides a platform to identify common goals between these networks.

The Right to Ask, the Need to Answer-When Patients Meet Research: How to Cope with Time

Reaching a diagnosis and its communication are two of the most meaningful events in the physician-patient relationship. When facing a disease, most of the patients' expectations rely on the hope that their clinicians would be able to understand the cause of their illness and eventually end it. Rare diseases are a peculiar subset of conditions in which the search for a diagnosis might reveal a long and painful journey scattered by doubts and requiring, in most cases, a long waiting time. For many individuals affected by a rare disease, turning to research might represent their last chance to obtain an answer to their questions. Time is the worst enemy, threatening to disrupt the fragile balance among affected individuals, their referring physicians, and researchers. It is consuming at all levels, draining economic, emotional, and social resources, and triggering unpredictable reactions in each stakeholder group. Managing waiting time is one of the most burdensome tasks for all the parties playing a role in the search for a diagnosis: the patients and their referring physicians urge to obtain a diagnosis in order to know the condition they are dealing with and establish proper management, respectively. On the other hand, researchers need to be objective and scientifically act to give a rigorous answer to their demands. While moving towards the same goal, patients, clinicians, and researchers might have different expectations and perceive the same waiting time as differently hard or tolerable. The lack of information on mutual needs and the absence of effective communication among the parties are the most common mechanisms of the failure of the therapeutic alliance that risk compromising the common goal of a proper diagnosis. In the landscape of modern medicine that goes faster and claims high standards of cure, rare diseases represent an exception where physicians and researchers should learn to cope with time in order to care for patients.

Remote visualization of large-scale genomic alignments for collaborative clinical research and diagnosis of rare diseases

The Solve-RD project objectives include solving undiagnosed rare diseases (RD) through collaborative research on shared genome-phenome datasets. The RD-Connect Genome-Phenome Analysis Platform (GPAP), for data collation and analysis, and the European Genome-Phenome Archive (EGA), for file storage, are two key components of the Solve-RD infrastructure. Clinical researchers can identify candidate genetic variants within the RD-Connect GPAP and, thanks to the developments presented here as part of joint ELIXIR activities, are able to remotely visualize the corresponding alignments stored at the EGA. The Global Alliance for Genomics and Health (GA4GH) htsget streaming application programming interface (API) is used to retrieve alignment slices, which are rendered by an integrated genome viewer (IGV) instance embedded in the GPAP. As a result, it is no longer necessary for over 11,000 datasets to download large alignment files to visualize them locally. This work highlights the advantages, from both the user and infrastructure perspectives, of implementing interoperability standards for establishing federated genomics data networks.

Rare diseases of epigenetic origin: Challenges and opportunities

Rare diseases (RDs), more than 80% of which have a genetic origin, collectively affect approximately 350 million people worldwide. Progress in next-generation sequencing technology has both greatly accelerated the pace of discovery of novel RDs and provided more accurate means for their diagnosis. RDs that are driven by altered epigenetic regulation with an underlying genetic basis are referred to as rare diseases of epigenetic origin (RDEOs). These diseases pose unique challenges in research, as they often show complex genetic and clinical heterogeneity arising from unknown gene-disease mechanisms. Furthermore, multiple other factors, including cell type and developmental time point, can confound attempts to deconvolute the pathophysiology of these disorders. These challenges are further exacerbated by factors that contribute to epigenetic variability and the difficulty of collecting sufficient participant numbers in human studies. However, new molecular and bioinformatics techniques will provide insight into how these disorders manifest over time. This review highlights recent studies addressing these challenges with innovative solutions. Further research will elucidate the mechanisms of action underlying unique RDEOs and facilitate the discovery of treatments and diagnostic biomarkers for screening, thereby improving health trajectories and clinical outcomes of affected patients.

Stepwise use of genomics and transcriptomics technologies increases diagnostic yield in Mendelian disorders

Purpose: Multi-omics offer worthwhile and increasingly accessible technologies to diagnostic laboratories seeking potential second-tier strategies to help patients with unresolved rare diseases, especially patients clinically diagnosed with a rare OMIM (Online Mendelian Inheritance in Man) disease. However, no consensus exists regarding the optimal diagnostic care pathway to adopt after negative results with standard approaches. Methods: In 15 unsolved individuals clinically diagnosed with recognizable OMIM diseases but with negative or inconclusive first-line genetic results, we explored the utility of a multi-step approach using several novel omics technologies to establish a molecular diagnosis. Inclusion criteria included a clinical autosomal recessive disease diagnosis and single heterozygous pathogenic variant in the gene of interest identified by first-line analysis (60%-9/15) or a clinical diagnosis of an X-linked recessive or autosomal dominant disease with no causative variant identified (40%-6/15). We performed a multi-step analysis involving short-read genome sequencing (srGS) and complementary approaches such as mRNA sequencing (mRNA-seq), long-read genome sequencing (lrG), or optical genome mapping (oGM) selected according to the outcome of the GS analysis. Results: SrGS alone or in combination with additional genomic and/or transcriptomic technologies allowed us to resolve 87% of individuals by identifying single nucleotide variants/indels missed by first-line targeted tests, identifying variants affecting transcription, or structural variants sometimes requiring lrGS or oGM for their characterization. Conclusion: Hypothesis-driven implementation of combined omics technologies is particularly effective in identifying molecular etiologies. In this study, we detail our experience of the implementation of genomics and transcriptomics technologies in a pilot cohort of previously investigated patients with a typical clinical diagnosis without molecular etiology.

Long-term treatment of cancer-prone germline PTEN mutant mice with low-dose rapamycin extends lifespan and delays tumour development

PTEN is one of the most commonly inactivated tumour suppressor genes in sporadic cancer. Germline heterozygous PTEN gene alterations also underlie PTEN hamartoma tumour syndrome (PHTS), a rare human cancer-predisposition condition. A key feature of systemic PTEN deregulation is the inability to adequately dampen PI3-kinase (PI3K)/mTORC1 signalling. PI3K/mTORC1 pathway inhibitors such as rapamycin are therefore expected to neutralise the impact of PTEN loss, rendering this a more druggable context compared with those of other tumour suppressor pathways such as loss of TP53. However, this has not been explored in cancer prevention in a model of germline cancer predisposition, such as PHTS. Clinical trials of short-term treatment with rapamycin have recently been initiated for PHTS, focusing on cognition and colon polyposis. Here, we administered a low dose of rapamycin from the age of 6 weeks onwards to mice with heterozygous germline Pten loss, a mouse model that recapitulates most characteristics of human PHTS. Rapamycin was well tolerated and led to a highly significant improvement of survival in both male and female mice. This was accompanied by a delay in, but not full blockade of, the development of a range of proliferative lesions, including gastro-intestinal and thyroid tumours and endometrial hyperplasia, with no impact on mammary and prostate tumours, and no effect on brain overgrowth. Our data indicate that rapamycin may have cancer prevention potential in human PHTS. This might also be the case for sporadic cancers in which genetic PI3K pathway activation is an early event in tumour development, such as endometrial cancer and some breast cancers. To the best of our knowledge, this is the first report of a long-term treatment of a germline cancer predisposition model with a PI3K/mTOR pathway inhibitor. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Assessing the digenic model in rare disorders using population sequencing data

An important fraction of patients with rare disorders remains with no clear genetic diagnostic, even after whole-exome or whole-genome sequencing, posing a difficulty in giving adequate treatment and genetic counseling. The analysis of genomic data in rare disorders mostly considers the presence of single gene variants in coding regions that follow a concrete monogenic mode of inheritance. A digenic inheritance, with variants in two functionally-related genes in the same individual, is a plausible alternative that might explain the genetic basis of the disease in some cases. In this case, digenic disease combinations should be absent or underrepresented in healthy individuals. We develop a framework to evaluate the significance of digenic combinations and test its statistical power in different scenarios. We suggest that this approach will be relevant with the advent of new sequencing efforts including hundreds of thousands of samples.

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

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

OMIXCARE: OMICS technologies solved about 33% of the patients with heterogeneous rare neuro-developmental disorders and negative exome sequencing results and identified 13% additional candidate variants

Purpose: Patients with rare or ultra-rare genetic diseases, which affect 350 million people worldwide, may experience a diagnostic odyssey. High-throughput sequencing leads to an etiological diagnosis in up to 50% of individuals with heterogeneous neurodevelopmental or malformation disorders. There is a growing interest in additional omics technologies in translational research settings to examine the remaining unsolved cases. Methods: We gathered 30 individuals with malformation syndromes and/or severe neurodevelopmental disorders with negative trio exome sequencing and array comparative genomic hybridization results through a multicenter project. We applied short-read genome sequencing, total RNA sequencing, and DNA methylation analysis, in that order, as complementary translational research tools for a molecular diagnosis. Results: The cohort was mainly composed of pediatric individuals with a median age of 13.7 years (4 years and 6 months to 35 years and 1 month). Genome sequencing alone identified at least one variant with a high level of evidence of pathogenicity in 8/30 individuals (26.7%) and at least a candidate disease-causing variant in 7/30 other individuals (23.3%). RNA-seq data in 23 individuals allowed two additional individuals (8.7%) to be diagnosed, confirming the implication of two pathogenic variants (8.7%), and excluding one candidate variant (4.3%). Finally, DNA methylation analysis confirmed one diagnosis identified by genome sequencing (Kabuki syndrome) and identified an episignature compatible with a BAFopathy in a patient with a clinical diagnosis of Coffin-Siris with negative genome and RNA-seq results in blood. Conclusion: Overall, our integrated genome, transcriptome, and DNA methylation analysis solved 10/30 (33.3%) cases and identified a strong candidate gene in 4/30 (13.3%) of the patients with rare neurodevelopmental disorders and negative exome sequencing results.

Personalized structural biology reveals the molecular mechanisms underlying heterogeneous epileptic phenotypes caused by de novo KCNC2 variants

Whole-exome sequencing (WES) in the clinic has identified several rare monogenic developmental and epileptic encephalopathies (DEE) caused by ion channel variants. However, WES often fails to provide actionable insight for rare diseases, such as DEEs, due to the challenges of interpreting variants of unknown significance (VUS). Here, we describe a "personalized structural biology" (PSB) approach that leverages recent innovations in the analysis of protein 3D structures to address this challenge. We illustrate this approach in an Undiagnosed Diseases Network (UDN) individual with DEE symptoms and a de novo VUS in KCNC2 (p.V469L), the Kv3.2 voltage-gated potassium channel. A nearby KCNC2 variant (p.V471L) was recently suggested to cause DEE-like phenotypes. Computational structural modeling suggests that both affect protein function. However, despite their proximity, the p.V469L variant is likely to sterically block the channel pore, while the p.V471L variant is likely to stabilize the open state. Biochemical and electrophysiological analyses demonstrate heterogeneous loss-of-function and gain-of-function effects, as well as differential response to 4-aminopyridine treatment. Molecular dynamics simulations illustrate that the pore of the p.V469L variant is more constricted, increasing the energetic barrier for K+ permeation, whereas the p.V471L variant stabilizes the open conformation. Our results implicate variants in KCNC2 as causative for DEE and guide the interpretation of a UDN individual. They further delineate the molecular basis for the heterogeneous clinical phenotypes resulting from two proximal pathogenic variants. This demonstrates how the PSB approach can provide an analytical framework for individualized hypothesis-driven interpretation of protein-coding VUS.

Supporting undiagnosed participants when clinical genomics studies end

Many large research initiatives have cumulatively enrolled thousands of patients with a range of complex medical issues but no clear genetic etiology. However, it is unclear how researchers, institutions, and funders should manage the data and relationships with those participants who remain undiagnosed when these studies end. In this comment, we outline the current literature relevant to post-study obligations in clinical genomics research and discuss the application of current guidelines to research with undiagnosed participants.

Repeat expansions nested within tandem CNVs: a unique structural change in GLS exemplifies the diagnostic challenges of non-coding pathogenic variation

Glutaminase deficiency has recently been associated with ataxia and developmental delay due to repeat expansions in the 5'UTR of the glutaminase (GLS) gene. Patients with the described GLS repeat expansion may indeed remain undiagnosed due to the rarity of this variant, the challenge of its detection and the recency of its discovery. In this study, we combined advanced bioinformatics screening of ~3000 genomes and ~1500 exomes with optical genome mapping and long-read sequencing for confirmation studies. We identified two GLS families, previously intensely and unsuccessfully analyzed. One family carries an unusual and complex structural change involving a homozygous repeat expansion nested within a quadruplication event in the 5'UTR of GLS. Glutaminase deficiency and its metabolic consequences were validated by in-depth biochemical analysis. The identified GLS patients showed progressive early-onset ataxia, cognitive deficits, pyramidal tract damage and optic atrophy, thus demonstrating susceptibility of several specific neuron populations to glutaminase deficiency. This large-scale screening study demonstrates the ability of bioinformatics analysis-validated by latest state-of-the-art technologies (optical genome mapping and long-read sequencing)-to effectively flag complex repeat expansions using short-read datasets and thus facilitate diagnosis of ultra-rare disorders.

Variation among DNA banking consent forms: points for clinicians to bank on

Deoxyribonucleic acid (DNA) banking is an important laboratory service that preserves the option of future genetic testing. DNA bank consent forms are a critical tool to facilitate thorough and valid informed consent. The objectives of this study were to assess the level of consistency of current clinical DNA banking consent forms with the American Society of Human Genetics (ASHG) and the American College of Medical Genetics and Genomics (ACMG) guidance and to explore variation among the forms. The content analysis matrix included key points identified from the ASHG and ACMG documents (including benefits/risks, sample storage, access, disposition, and communication) and additional points beyond the ASHG and ACMG documents identified from the consent forms themselves during the analysis process. Forms were assessed for language addressing each point. Five consent forms were identified and analyzed for twelve key points and eight additional points. The average consistency for key points was 10.8/12 (range 8/12 to 12/12). The range for additional points was 1/8 to 5/8. There was variation across forms in the details provided related to key and additional points. Gaps in clinical DNA banking consent forms are barriers to achieving informed consent. Clinicians can consider the consent key and additional points discussed here to supplement and enrich their clinical DNA banking informed consent discussions, promote stewardship, and maximize downstream utility of banked DNA. The identification of multiple additional points beyond the ASHG and ACMG documents' key points indicates a need for this guidance to be updated.

Genetic Analysis Algorithm for the Study of Patients with Multiple Congenital Anomalies and Isolated Congenital Heart Disease

Congenital anomalies (CA) affect 3–5% of newborns, representing the second-leading cause of infant mortality in Argentina. Multiple congenital anomalies (MCA) have a prevalence of 2.26/1000 births in newborns, while congenital heart diseases (CHD) are the most frequent CA with a prevalence of 4.06/1000 births. The aim of this study was to identify the genetic causes in Argentinian patients with MCA and isolated CHD. We recruited 366 patients (172 with MCA and 194 with isolated CHD) born between June 2015 and August 2019 at public hospitals. DNA from peripheral blood was obtained from all patients, while karyotyping was performed in patients with MCA. Samples from patients presenting conotruncal CHD or DiGeorge phenotype (n = 137) were studied using MLPA. Ninety-three samples were studied by array-CGH and 18 by targeted or exome next-generation sequencing (NGS). A total of 240 patients were successfully studied using at least one technique. Cytogenetic abnormalities were observed in 13 patients, while 18 had clinically relevant imbalances detected by array-CGH. After MLPA, 26 patients presented 22q11 deletions or duplications and one presented a TBX1 gene deletion. Following NGS analysis, 12 patients presented pathogenic or likely pathogenic genetic variants, five of them, found in KAT6B, SHH, MYH11, MYH7 and EP300 genes, are novel. Using an algorithm that combines molecular techniques with clinical and genetic assessment, we determined the genetic contribution in 27.5% of the analyzed patients.

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein’s function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein’s loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.

PhenGenVar: A User-Friendly Genetic Variant Detection and Visualization Tool for Precision Medicine

Precision medicine has been revolutionized by the advent of high-throughput next-generation sequencing (NGS) technology and development of various bioinformatic analysis tools for large-scale NGS big data. At the population level, biomedical studies have identified human diseases and phenotype-associated genetic variations using NGS technology, such as whole-genome sequencing, exome sequencing, and gene panel sequencing. Furthermore, patients’ genetic variations related to a specific phenotype can also be identified by analyzing their genomic information. These breakthroughs paved the way for the clinical diagnosis and precise treatment of patients’ diseases. Although many bioinformatics tools have been developed to analyze the genetic variations from the individual patient’s NGS data, it is still challenging to develop user-friendly programs for clinical physicians who do not have bioinformatics programing skills to diagnose a patient’s disease using the genomic data. In response to this demand, we developed a Phenotype to Genotype Variation program (PhenGenVar), which is a user-friendly interface for monitoring the variations in a gene of interest for molecular diagnosis. This allows for flexible filtering and browsing of variants of the disease and phenotype-associated genes. To test this program, we analyzed the whole-genome sequencing data of an anonymous person from the 1000 human genome project data. As a result, we were able to identify several genomic variations, including single-nucleotide polymorphism, insertions, and deletions in specific gene regions. Therefore, PhenGenVar can be used to diagnose a patient’s disease. PhenGenVar is freely accessible and is available at our website.

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%).

Six years' accomplishment of the Initiative on Rare and Undiagnosed Diseases: nationwide project in Japan to discover causes, mechanisms, and cures

The identification of causative genetic variants for hereditary diseases has revolutionized clinical medicine and an extensive collaborative framework with international cooperation has become a global trend to understand rare disorders. The Initiative on Rare and Undiagnosed Diseases (IRUD) was established in Japan to provide accurate diagnosis, discover causes, and ultimately provide cures for rare and undiagnosed diseases. The fundamental IRUD system consists of three pillars: IRUD diagnostic coordination, analysis centers (IRUD-ACs), and a data center (IRUD-DC). IRUD diagnostic coordination consists of clinical centers (IRUD-CLs) and clinical specialty subgroups (IRUD-CSSs). In addition, the IRUD coordinating center (IRUD-CC) manages the entire IRUD system and temporarily operates the IRUD resource center (IRUD-RC). By the end of March 2021, 6301 pedigrees consisting of 18,136 individuals were registered in the IRUD. The whole-exome sequencing method was completed in 5136 pedigrees, and a final diagnosis was established in 2247 pedigrees (43.8%). The total number of aberrated genes and pathogenic variants was 657 and 1718, among which 1113 (64.8%) were novel. In addition, 39 novel disease entities or phenotypes with 41 aberrated genes were identified. The 6-year endeavor of IRUD has been an overwhelming success, establishing an all-Japan comprehensive diagnostic and research system covering all geographic areas and clinical specialties/subspecialties. IRUD has accurately diagnosed diseases, identified novel aberrated genes or disease entities, discovered many candidate genes, and enriched phenotypic and pathogenic variant databases. Further promotion of the IRUD is essential for determining causes and developing cures for rare and undiagnosed diseases.

Perfect and imperfect views of ultraconserved sequences

Across the human genome, there are nearly 500 'ultraconserved' elements: regions of at least 200 contiguous nucleotides that are perfectly conserved in both the mouse and rat genomes. Remarkably, the majority of these sequences are non-coding, and many can function as enhancers that activate tissue-specific gene expression during embryonic development. From their first description more than 15 years ago, their extreme conservation has both fascinated and perplexed researchers in genomics and evolutionary biology. The intrigue around ultraconserved elements only grew with the observation that they are dispensable for viability. Here, we review recent progress towards understanding the general importance and the specific functions of ultraconserved sequences in mammalian development and human disease and discuss possible explanations for their extreme conservation.

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

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

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.

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.

Opportunities and Challenges for Machine Learning in Rare Diseases

Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.

The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems

Background

Rare diseases (RD) are a diverse collection of more than 7–10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS).

Methodology

We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients.

Results

The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three–fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease

Conclusions

The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-02061-3.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

CNV Detection from Exome Sequencing Data in Routine Diagnostics of Rare Genetic Disorders: Opportunities and Limitations

To assess the potential of detecting copy number variations (CNVs) directly from exome sequencing (ES) data in diagnostic settings, we developed a CNV-detection pipeline based on ExomeDepth software and applied it to ES data of 450 individuals. Initially, only CNVs affecting genes in the requested diagnostic gene panels were scored and tested against arrayCGH results. Pathogenic CNVs were detected in 18 individuals. Most detected CNVs were larger than 400 kb (11/18), but three individuals had small CNVs impacting one or a few exons only and were thus not detectable by arrayCGH. Conversely, two pathogenic CNVs were initially missed, as they impacted genes not included in the original gene panel analysed, and a third one was missed as it was in a poorly covered region. The overall combined diagnostic rate (SNVs + CNVs) in our cohort was 36%, with wide differences between clinical domains. We conclude that (1) the ES-based CNV pipeline detects efficiently large and small pathogenic CNVs, (2) the detection of CNV relies on uniformity of sequencing and good coverage, and (3) in patients who remain unsolved by the gene panel analysis, CNV analysis should be extended to all captured genes, as diagnostically relevant CNVs may occur everywhere in the genome.

Whole-genome sequencing as a first-tier diagnostic framework for rare genetic diseases

Rare diseases affect nearly 300 million people globally with most patients aged five or less. Traditional diagnostic approaches have provided much of the diagnosis; however, there are limitations. For instance, simply inadequate and untimely diagnosis adversely affects both the patient and their families. This review advocates the use of whole genome sequencing in clinical settings for diagnosis of rare genetic diseases by showcasing five case studies. These examples specifically describe the utilization of whole genome sequencing, which helped in providing relief to patients via correct diagnosis followed by use of precision medicine.

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data

Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics.

A KCNK16 mutation causing TALK-1 gain of function is associated with maturity-onset diabetes of the young

Maturity-onset diabetes of the young (MODY) is a heterogeneous group of monogenic disorders of impaired pancreatic β cell function. The mechanisms underlying MODY include β cell KATP channel dysfunction (e.g., KCNJ11 [MODY13] or ABCC8 [MODY12] mutations); however, no other β cell channelopathies have been associated with MODY to date. Here, we have identified a nonsynonymous coding variant in KCNK16 (NM_001135105: c.341T>C, p.Leu114Pro) segregating with MODY. KCNK16 is the most abundant and β cell-restricted K+ channel transcript, encoding the two-pore-domain K+ channel TALK-1. Whole-cell K+ currents demonstrated a large gain of function with TALK-1 Leu114Pro compared with TALK-1 WT, due to greater single-channel activity. Glucose-stimulated membrane potential depolarization and Ca2+ influx were inhibited in mouse islets expressing TALK-1 Leu114Pro with less endoplasmic reticulum Ca2+ storage. TALK-1 Leu114Pro significantly blunted glucose-stimulated insulin secretion compared with TALK-1 WT in mouse and human islets. These data suggest that KCNK16 is a previously unreported gene for MODY.

Broadening the phenotypic spectrum of Beta3GalT6-associated phenotypes

Biallelic mutations in B3GALT6, coding for a galactosyltransferase involved in the synthesis of glycosaminoglycans (GAGs), have been associated with various clinical conditions, causing spondyloepimetaphyseal dysplasia with joint laxity type 1 (SEMDJL1 or SEMDJL Beighton type), Al-Gazali syndrome (ALGAZ), and a severe progeroid form of Ehlers-Danlos syndrome (EDSSPD2). In the 2017 Ehlers-Danlos syndrome (EDS) classification, Beta3GalT6-related disorders were grouped in the spondylodysplastic EDSs together with spondylodysplastic EDSs due to B4GALT7 and SLC39A13 mutations. Herein, we describe a patient with a previously unreported homozygous pathogenic B3GALT6 variant resulting in a complex phenotype more severe than spondyloepimetaphyseal dysplasia with joint laxity type 1, and having dural ectasia and aortic dilation as additionally associated features, further broadening the phenotypic spectrum of the Beta3GalT6-related syndromes. We also document the utility of repeating sequencing in patients with uninformative exomes, particularly when performed by using "first generations" enrichment capture methods.

Genome sequencing for detection of pathogenic deep intronic variation: A clinical case report illustrating opportunities and challenges

Variants in JAM3 have been reported in four families manifesting a severe autosomal recessive disorder characterized by hemorrhagic destruction of the brain, subependymal calcification, and cataracts. We describe a 7-year-old male with a similar presentation found by research-based quad genome sequencing to have two novel splicing variants in trans in JAM3, including one deep intronic variant (NM_032801.4: c.256+1260G>C) not detectable by standard exome sequencing. Targeted sequencing of RNA isolated from transformed lymphoblastoid cell lines confirmed that each of the two variants has a deleterious effect on JAM3 mRNA splicing. The role for genome sequencing as a clinical diagnostic test extends to those patients with phenotypes strongly suggestive of a specific Mendelian disorder, especially when the causal genetic variant(s) are not found by a more targeted approach. Barriers to diagnosis via identification of pathogenic deep intronic variation include lack of laboratory consensus regarding in silico splicing prediction tools and limited access to clinically validated confirmatory RNA experiments.