International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases

Exploring the journey to genomic testing and genetic services: A qualitative study of parental perspectives of children with rare conditions

Despite affecting a small portion of the population, rare conditions have a significant impact, collectively affecting around 300 million people worldwide. Historically, early diagnosis has been impeded by failure to recognize rare conditions and order/refer for appropriate genomic testing. The advancements in genome sequencing offer a more agnostic and accelerated approach to the identification and diagnosis of rare disorders, potentially improving health outcomes, reducing the impact of disability, and reducing financial and psychological burdens on families. Due to the complex nature of these conditions, early engagement with genomic testing and clinical genetics services is key to facilitating a diagnosis. This qualitative exploration aimed to understand the journey to genomic testing and services and identify the supports families need during the diagnostic period. We conducted semi-structured interviews with 24 parents of children with a rare condition. Interviews were analyzed using inductive reflexive thematic analysis. Three themes of the parent experience were identified (1) the need for a streamlined pathway through the healthcare system, (2) the value of healthcare professionals who listen to parents, believed them, and partnered with them, and (3) the power of accurate diagnosis. Our findings indicate that providing direct and timely access to genomic testing for patients with a suspected rare condition could alleviate psychological and financial stressors. Genetic counselors are adept at supporting families affected by rare conditions and are optimally placed to facilitate timely access to genomic testing. Improving timely access may be facilitated through educating primary care physicians and embedding genetic counselors in pediatric settings.

IEIVariantFilter: a bioinformatics tool to speed up genetic diagnosis of inborn errors of immunity patients

Severe infectious diseases remain the leading cause of death in children and young adults worldwide. Monogenic inborn errors of immunity (IEIs) are traditionally defined as a heterogeneous group of rare inborn genetic diseases affecting the functioning of the immune system. Greater awareness has led to the clinical definition of 485 monogenic IEIs and whole exome sequencing (WES) is becoming increasingly relevant for IEI genetic diagnosis. The current protocol for IEI genetic studies includes manual filtering of the list of genes obtained as a WES read-out providing a short list of candidate genes. This procedure is time-consuming and can produce mistakes due to human error in manual filtering. IEIVariantFilter is a new web-based bioinformatics tool to speed up and refine the genetic diagnosis of IEI patients oriented for users in the biomedical field without needing bioinformatics expertise. IEIVariantFilter prioritizes genetic variants based on ranges of zygosity, the quality of reads, the predicted variant effect, and genes related to immunity, considering a consanguineous hypothesis whenever necessary. IEIVariantFilter facilitates gene and variant list prioritization, speeding up the identification of candidate disease-causing variants for validation by experimental studies. The software improves the genetic diagnosis of patients, thereby facilitating precision medicine and fast and proper treatment.

AOPEP-related autosomal recessive dystonia: update on Zech-Boesch syndrome

Gene discovery efforts have contributed to a better understanding of the molecular causes of dystonia, but knowledge of the individual monogenic forms remains limited. This review seeks to summarise all available data on the recently identified autosomal recessive subtype of dystonia caused by variants in AOPEP, focusing on the geographical origins of affected families, mutational spectrum, phenotypic expressions and pathophysiology. AOPEP-related dystonia, documented as Zech-Boesch syndrome in the Online Mendelian Inheritance in Man database, has been diagnosed in cohorts around the globe including under-represented populations with increased rates of consanguinity. Predictably leading to loss of protein function, the majority (74%) of disease-associated AOPEP alleles are protein-truncating variants comprising homozygous and compound heterozygous stop-gain, frameshift and splice-site changes. The dystonic disorder shows onset from childhood to the fourth decade and generalises in a significant proportion of cases (60%). Variable expressivity and age-related penetrance are likely to play a role in manifestation of the condition, consistent with occasional occurrence of AOPEP homozygous pathogenic variants in subjects without a diagnosis of dystonia. AOPEP encodes aminopeptidase O, a proteolytic processing enzyme that is preferentially expressed in glia and potentially linked to endosomal-lysosomal pathways. AOPEP-related autosomal recessive Zech-Boesch syndrome is of worldwide relevance for the diagnosis of genetic dystonia. Future research focusing on AOPEP`s role in cellular protein metabolism may provide new insights into dystonia pathogenesis and yet-unidentified therapeutic targets.

Linking international registries to FHIR and Phenopackets with RareLink: a scalable REDCap-based framework for rare disease data interoperability

While Research Electronic Data Capture (REDCap) has been widely adopted in rare disease research, its unconstrained data format often leads to implementations that lack native interoperability with global health data standards, limiting secondary data use. To address this, we developed and validated RareLink, an open-source framework implementing our previously-published ontology-based rare disease common data model, enabling standardised data exchange between REDCap, international registries, and downstream analysis tools. Its preconfigured pipelines interact with the local REDCap application programming interface and enable semi-automatic import or export of data to the Global Alliance for Genomics and Health (GA4GH) Phenopackets and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) instances, conforming to the HL7 International Patient Summary and Genomics Reporting profiles. The framework was developed in three iterative phases using retrospective and prospective clinical data from patients with various rare metabolic and neuromuscular disorders, as well as inborn errors of immunity. Phase one involved deployment across four German university hospitals for registry and data analysis purposes. Phase two integrated RareLink with the Canadian Inborn Errors of Immunity National Registry, enhancing extensibility. Phase three focuses on international implementation in South Africa and Japan to assess global scalability. Implementation feedback was continuously incorporated to validate outputs and improve usability. For evaluation purposes, we defined a simulated Kabuki syndrome cohort based on published cases and demonstrated data export to both Phenopackets and FHIR instances. RareLink can enhance the clinical utility of REDCap by enabling structured data analysis and interoperability. Its global applicability and open-source nature can support equitable rare disease research with the ultimate goal to improve patient care. Broader adoption and coordination with entities such as HL7 and the European Reference Networks are thus essential to realise its full potential. The framework and its documentation are freely available through GitHub and Read the Docs, respectively.

Big data and transformative bioinformatics in genomic diagnostics and beyond

The current era of high-throughput analysis-driven research offers invaluable insights into disease etiologies, accurate diagnostics, pathogenesis, and personalized therapy. In the field of movement disorders, investigators are facing an increasing growth in the volume of produced patient-derived datasets, providing substantial opportunities for precision medicine approaches based on extensive information accessibility and advanced annotation practices. Integrating data from multiple sources, including phenomics, genomics, and multi-omics, is crucial for comprehensively understanding different types of movement disorders. Here, we explore formats and analytics of big data generated for patients with movement disorders, including strategies to meaningfully share the data for optimized patient benefit. We review computational methods that are essential to accelerate the process of evaluating the increasing amounts of specialized data collected. Based on concrete examples, we highlight how bioinformatic approaches facilitate the translation of multidimensional biological information into clinically relevant knowledge. Moreover, we outline the feasibility of computer-aided therapeutic target evaluation, and we discuss the importance of expanding the focus of big data research to understudied phenotypes such as dystonia.

Combining long-read DNA and RNA sequencing to enhance molecular understanding of structural variations leading to copy gains

Structural variants (SVs) significantly contribute to human disease, but their complexity often makes accurate characterization difficult with conventional methods. Advances in long-read sequencing (LRS) offer potential by spanning kilobases and directly resolving SVs. In this study, we examined two individuals with unresolved SVs. LRS on both DNA and cDNA provided single-base resolution of all breakpoint junctions, revealing detailed rearrangement structures and underlying mechanisms. Transcriptomic analyses identified abnormal fusion transcripts and clarified their functional consequences, including haploinsufficiency and potential dominant-negative effects. In one case, a triplication affecting the ZMYM2 gene was precisely mapped, revealing a truncated variant that may escape nonsense-mediated decay. In the second case, a highly complex reciprocal translocation involving RERE and FHAD1 disrupted RERE expression, with Hi-C data showing minimal impact on enhancer-promoter interactions. Due to their complexity, these SVs were not fully resolved by standard methods. By integrating LRS with transcriptomic and chromosomal conformation analyses, we provided a comprehensive understanding of SV formation and its pathogenic impact. Our findings emphasize the need for advanced genomic approaches to resolve complex SVs, enhance diagnostic accuracy, and inform clinical management.

Improved diagnosis of patients with rare diseases through the application of constrained coding region annotation and de novo status

PURPOSE

Identifying the pathogenic variant in a patient with rare disease (RD) is the first step in ending their diagnostic odyssey. De novo (Dn) variants affecting protein-coding DNA are a well-established cause of Mendelian disorders in patients with RD. Constrained coding regions (CCRs) are specific segments of coding DNA that are devoid of functional variants in healthy individuals.

METHODS

We evaluated the diagnostic utility of incorporating combined Dn/CCR status into the variant prioritization cascade for patients with RD that have undergone genomic sequencing. Using the Genomics England 100,000 Genomes Project v12, we selected 3090 trios that have undergone diagnostic evaluation and been analyzed with an advanced Dn identification pipeline.

RESULTS

Our analysis shows that the diagnostic rate increased from 71% in the full cohort to 87% for Dn/CCR variants. Of note, manual evaluation of the Dn/CCR variants from undiagnosed patients with clinical follow-up revealed a diagnosis for 13 further patients. This outcome increases the diagnostic rate for Dn/CCR variants to 91% and suggests that the application of this metric can prioritize diagnostic variants in undiagnosed patients.

CONCLUSION

We demonstrate the potential clinical utility of performing bespoke Dn analyses of patients with RD and for incorporating CCR information into the filtering cascade to prioritize pathogenic variants.

Benchmark of computational methods to detect digenism in sequencing data

Digenic inheritance is characterized by the combined alteration of two different genes leading to a disease. It could explain the etiology of many currently undiagnosed rare diseases. With the advent of next-generation sequencing technologies, the identification of digenic inheritance patterns has become more technically feasible, yet still poses significant challenges without any gold standard method. Here, we present a comprehensive overview of the existing methods developed to detect digenic inheritance in sequencing data and provide a classification in cohort-based and individual-based methods. The latter category of methods appeared the most applicable to rare diseases, especially the ones not needing patient phenotypic description as input. We discuss the availability of the different methods, their output and scalability to inform potential users. Focusing on methods to detect digenic inheritance in the case of very rare or heterogeneous diseases, we propose a benchmark using different real-life scenarios involving known digenic and putative neutral pairs of genes. Among these different methods, DiGePred stood out as the one giving the least number of false positives, ARBOCK as giving the greatest number of true positives, and DIEP as having the best balance between both. By synthesizing the state-of-the-art techniques and providing insights into their practical utility, this benchmark serves as a valuable resource for researchers and clinicians in selecting suitable methodologies for detecting digenic inheritance in a wide range of disorders using sequencing data.

Calcium level and autophagy defect in GNE mutants of rare neuromuscular disorder

Rare genetic disorders are low in prevalence and hence there is little or no attention paid to them in the mainstream medical industry. One of the ultra-rare neuromuscular disorders, GNE myopathy is caused due to biallelic mutations in the bifunctional enzyme, GNE (UDP N-acetylglucosamine-2-epimerase/N-acetylmannosamine kinase). It catalyses the rate-limiting step in sialic acid biosynthesis. There are no effective treatments for GNE myopathy as the pathomechanism is poorly understood. Pathologically, the disease is characterized by the formation of rimmed vacuoles that contain aggregates of β-amyloid, tau, presenilin etc proteins in muscle biopsy samples. Accumulation of aggregated proteins in the cells may occur due to the failure of the regulated autophagy phenomenon. In the present study, we aim to understand the effect of GNE mutations on autophagy. The cytosolic calcium levels in GNE mutant cells were found to be altered in a GNE mutation-specific manner. The chaperone levels, such as HSP70 and PDI, as well as autophagic markers (LC3II/I ratios) were altered in the GNE mutant cells. Treatment with BAPTA-AM, calcium chelator, significantly restored cytosolic calcium levels in some GNE mutant cells as well as autophagic marker levels and autophagic punctae formation. The effect on the calcium signalling cascade involving CaMKKβ/AMPK/mTOR was studied in the GNE mutant cells. Our study provides insights into the role of calcium in autophagic vacuole formation in the cells with GNE mutations that will have significance towards understanding the pathomechanism of GNE Myopathy and drug target identification for the rare disease.

Genetic Alterations in Atypical Cerebral Palsy Identified Through Chromosomal Microarray and Exome Sequencing

This study investigated the genetic causes of atypical cerebral palsy (CP) through chromosomal microarray (CMA) and exome sequencing (ES) in a cohort of 10 Korean patients to identify variants and expand the spectrum of mutations associated with atypical cerebral palsy. Whole ES and/or genome sequencing (GS) after routine karyotyping and CMA was performed to identify causative variants and expand the spectrum of mutations associated with atypical CP. In cases of atypical CP, scoliosis and/or kyphosis, ranging from mild to severe, were present in all patients. Epilepsy was a comorbidity in seven patients (70%), and intellectual disability (ID) was observed in varying degrees. This study identified three copy number variations (CNVs), including 15q11.2 microdeletion (n = 1), 17p11.2 duplication (n = 1), and 12p13.33p11.23 duplication/18p11.32 microdeletion (n = 1), and six likely pathogenic variants (LPVs) or pathogenic variants (PVs) detected in the SLC2A1, PLAA, CDC42BPB, CACNA1D, ALG12, and SACS genes (n = 6). These findings emphasize the significance of incorporating genetic testing into the diagnostic process for atypical CP to improve our understanding of its molecular basis and inform personalized treatment strategies. To further advance this research, future studies should focus on exploring genotype-phenotype correlations, assessing the functional impact of identified variants, and increasing the sample size to validate the observed patterns.

Use of Quantum Dots as Nanotheranostic Agents: Emerging Applications in Rare Genetic Diseases

Rare genetic diseases (RGDs) affect a small percentage of the global population but collectively have a substantial impact due to their diverse manifestations. Although the precise reasons behind these diseases remain unclear, roughly 80% of cases are genetically linked. Recent efforts focus on understanding pathology and developing new diagnostic and therapeutic approaches for RGDs. However, there persists a gap between fundamental research and clinical therapeutic approaches, where advancements in nanotechnology offer promising improvements. In this context, nanosized light-emitting quantum dots (QDs), ranging from 2-10 nm, are promising materials for diverse applications. Their size-tunable light emission, high quantum yield, and photostability allow for precise tracking of cargo. Additionally, QDs can be functionalized with therapeutic agents, antibodies, or peptides to target specific cellular pathways, enhancing treatment efficacy while minimizing side effects. By combining diagnostic and therapeutic capabilities in a single platform, QDs thus offer a versatile and powerful approach to tackle rare genetic disorders. Despite several reviews on various therapeutic applications of QDs, their utilization in the specific domain of RGDs is not well documented. This review highlight QDs' potential in diagnosing and treating certain RGDs and addresses the challenges limiting their application.

Omics and rare diseases: challenges, applications, and future perspectives

INTRODUCTION

Rare diseases (RDs) are a heterogeneous group of diseases recognized as a relevant global health priority but posing aspects of complexity, such as geographical scattering of affected individuals, improper/late diagnosis, limited awareness, difficult surveillance and monitoring, limited understanding of natural history, and lack of treatment. Usually, RDs have a pediatric onset and are life-long, multisystemic, and associated with a poor prognosis.

AREAS COVERED

In this work, we review how high-throughput omics technologies such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and other well-established omics, which are increasingly more affordable and efficient, can be applied to the study of RDs promoting diagnosis, understanding of pathological mechanisms, biomarker discovery, and identification of treatments.

EXPERT OPINION

RDs, despite their challenges, offer a niche where collaborative efforts and personalized treatment strategies might be feasible using omics technologies. Specialized consortia fostering multidisciplinary collaboration, data sharing, and the development of biobanks and registries can be built; multi-omics approaches, including so far less exploited omics technologies, along with the implementation of AI tools can be undertaken to deepen our understanding of RDs, driving biomarker discovery and clinical interventions. Nevertheless, technical, ethical, legal, and societal issues must be clearly defined and addressed.

Advancing Equity in Rare Disease Research: Insights From the Undiagnosed Disease Network

Rare diseases affect 6%-8% of the population and present diagnostic challenges, particularly for historically marginalized ethnic and racial groups. The Undiagnosed Diseases Network (UDN) aims to enhance diagnosis rates and research participation among such minoritized groups. A retrospective review was conducted from 2015 to 2023, analyzing 2235 UDN participants to evaluate its progress toward this objective. Data on demographics, disease phenotypes, diagnostic outcomes, and socioeconomic factors were collected and statistical analyses assessed differences among ethnic and racial groups. This demonstrated that Hispanic and Black non-Hispanic groups were underrepresented, while White non-Hispanic participants were overrepresented in the UDN compared to the US population. Individuals whose primary language was not English were also significantly underrepresented. Diagnosis rates varied, with the highest rates among Asian non-Hispanic (39.5%) and Hispanic (35.3%) groups and the lowest rate in the White non-Hispanic group (26.8%) (p < 0.001). Binomial logistic regression found, however, that only participant age and disease phenotype predicted the likelihood of receiving a diagnosis (p < 0.001). Persistent ethnic and racial disparities in UDN participation appear to be associated with major differences in application rates. Under-enrollment of historically marginalized ethnic and racial groups may be due to economic hardships and language barriers. No differences in the diagnostic yield among ethnic and racial groups were observed after controlling for other factors. This work highlights the value of comprehensive genetic evaluations for addressing healthcare disparities and suggests priorities for advancing inclusion in rare disease research.

High-throughput tracking enables systematic phenotyping and drug repurposing in C. elegans disease models

There are thousands of Mendelian diseases with more being discovered weekly and the majority have no approved treatments. To address this need, we require scalable approaches that are relatively inexpensive compared to traditional drug development. In the absence of a validated drug target, phenotypic screening in model organisms provides a route for identifying candidate treatments. Success requires a screenable phenotype. However, the right phenotype and assay may not be obvious for pleiotropic neuromuscular disorders. Here, we show that high-throughput imaging and quantitative phenotyping can be conducted systematically on a panel of C. elegans disease model strains. We used CRISPR genome-editing to create 25 worm models of human Mendelian diseases and phenotyped them using a single standardised assay. All but two strains were significantly different from wild-type controls in at least one feature. The observed phenotypes were diverse, but mutations of genes predicted to have related functions led to similar behavioural differences in worms. As a proof-of-concept, we performed a drug repurposing screen of an FDA-approved compound library, and identified two compounds that rescued the behavioural phenotype of a model of UNC80 deficiency. Our results show that a single assay to measure multiple phenotypes can be applied systematically to diverse Mendelian disease models. The relatively short time and low cost associated with creating and phenotyping multiple strains suggest that high-throughput worm tracking could provide a scalable approach to drug repurposing commensurate with the number of Mendelian diseases.

PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis

The introduction of Next-Generation Sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The Population Sampling Probability (PSAP) method was developed to meet this aim but only for coding variants in exome data. Here, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large data sets of exomes and genomes from the general population. PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than 50% of non-coding ClinVar variants were among the top 10 variants of the genome. On real sequencing data from six patients with Cerebral Small Vessel Disease and nine patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases.

Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization

PURPOSE

Clinical intuition is commonly incorporated into the differential diagnosis as an assessment of the likelihood of candidate diagnoses based either on the patient population being seen in a specific clinic or on the signs and symptoms of the initial presentation. Algorithms to support diagnostic sequencing in individuals with a suspected rare genetic disease do not yet incorporate intuition and instead assume that each Mendelian disease has an equal pretest probability.

METHODS

The LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) algorithm calculates the likelihood ratio of clinical manifestations represented by Human Phenotype Ontology terms to rank candidate diagnoses. The initial version of LIRICAL assumed an equal pretest probability for each disease in its calculation of the posttest probability (where the test is diagnostic exome or genome sequencing). We introduce Clinical Intuition for Likelihood Ratios (ClintLR), an extension of the LIRICAL algorithm that boosts the pretest probability of groups of related diseases deemed to be more likely.

RESULTS

The average rank of the correct diagnosis in simulations using ClintLR showed a statistically significant improvement over a range of adjustment factors.

CONCLUSION

ClintLR successfully encodes clinical intuition to improve ranking of rare diseases in diagnostic sequencing. ClintLR is freely available at https://github.com/TheJacksonLaboratory/ClintLR.

Characterization of patients treated at a rare disease referral service: a descriptive study, 2016-2021

OBJECTIVE

To analyze the first referral service for rare diseases accredited by the Brazilian Ministry of Health, focusing on referral from the primary healthcare network through to diagnosis.

METHODS

This is a descriptive study with patients treated between 2016 and 2021 at a referral hospital service located in Curitiba, Paraná, Brazil. Clinical and epidemiological data were obtained from medical records, as were the results of genetic tests at the hospital's clinical analysis laboratory. Qualitative data were expressed as absolute and relative frequencies, while quantitative data were expressed as medians and interquartile ranges and compared using the Kruskal-Wallis test.

RESULTS

The study included 1,751 cases, 34.1% were diagnosed with rare diseases, with average time until diagnosis being 3.0 years, whereby mucopolysaccharidosis type II (4.0%) and tuberous sclerosis (3.9%) were the most common. Greater length of time for obtaining diagnosis (p-value 0.004) and receiving specialized care (p-value<0.001) was found in patients from the interior region of Paraná state, compared to those residing in Curitiba city and its metropolitan region.

CONCLUSION

Diagnosis of rare diseases occurred in approximately one third of cases. The average time until diagnosis suggests a possible positive impact of implementing the referral service. The longer time until diagnosis and specialized care found among patients from the interior region of Paraná represent challenges regarding adequate referral to specialized services.

A novel homozygous intronic variant in CDT1 that alters splicing causes Meier-Gorlin syndrome, and a review of published mutations and growth hormone treatments

BACKGROUND

Meier-Gorlin syndrome (MGORS) is a rare autosomal inherited form of primordial dwarfism. Pathogenic variants in 13 genes involved in DNA replication initiation have been identified in this disease, but homozygous intronic variants have never been reported. Additionally, whether growth hormone (GH) treatment can increase the height of children with MGORS is unclear.

METHODS

The medical history data of a young girl were collected and reviewed. Whole-exome sequencing (WES) and bioinformatic analysis were performed to identify any variants and predict their pathogenicity. Minigene constructs were generated and transfected into HEK-293T cells for in vitro splicing assays. The literature was reviewed to explore the mutational spectrum and efficacy of GH treatment for this disease.

RESULTS

A girl with microtia, hypoplastic patellae, and severe growth retardation carried a novel homozygous intronic variant (NM_030928.4: exon 3: c.352-30 A > C) in CDT1. The variant was predicted to break a branch point and alter splicing, and the minigene assay confirmed abnormal splicing with exon 3 skipping. The patient was treated with GH for 5 years, with an increase in growth velocity from 4.0 cm/year to an average of 6.2 cm/year. A literature review revealed that the most common variant type and inheritance state were missense and compound heterozygous, respectively. Additionally, the vast majority of children with MGORS treated with GH had normal insulin-like growth factor 1 (IGF-1) levels, and half of them responded positively to GH therapy.

CONCLUSIONS

We reported a novel pathogenic homozygous intronic variant (c.352-30 A > C) of CDT1 in a girl with MGORS, and this mutation extended the genetic spectrum of the disease. GH therapy may be beneficial for height outcomes in children with MGORS with normal IGF-1 levels.

Privacy-by-Design with Federated Learning will drive future Rare Disease Research

Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are heavily underserved. Most RD studies are chronically underfunded, and research faces inherent difficulties in analyzing scarce data. Furthermore, the creation and analysis of representative datasets are often constrained by stringent data protection regulations, such as the EU General Data Protection Regulation. This review examines the potential of federated learning (FL) as a privacy-by-design approach to training machine learning on distributed datasets while ensuring data privacy by maintaining the local patient data and only sharing model parameters, which is particularly beneficial in the context of sensitive data that cannot be collected in a centralized manner. FL enhances model accuracy by leveraging diverse datasets without compromising data privacy. This is particularly relevant in rare diseases, where heterogeneity and small sample sizes impede the development of robust models. FL further has the potential to enable the discovery of novel biomarkers, enhance patient stratification, and facilitate the development of personalized treatment plans. This review illustrates how FL can facilitate large-scale, cross-institutional collaboration, thereby enabling the development of more accurate and generalizable models for improved diagnosis and treatment of rare diseases. However, challenges such as non-independently distributed data and significant computational and bandwidth requirements still need to be addressed. Future research must focus on applying FL technology for rare disease datasets while exploring standardized protocols for cross-border collaborations that can ultimately pave the way for a new era of privacy-preserving and distributed data-driven rare disease research.

Estimating the Number of Polygenic Diseases Among Six Mutually Exclusive Entities of Non-Tumors and Cancer

In the era of precision medicine with increasing amounts of sequenced cancer and non-cancer genomes of different ancestries, we here enumerate the resulting polygenic disease entities. Based on the cell number status, we first identified six fundamental types of polygenic illnesses, five of which are non-cancerous. Like complex, non-tumor disorders, neoplasms normally carry alterations in multiple genes, including in 'Drivers' and 'Passengers'. However, tumors also lack certain genetic alterations/epigenetic changes, recently named 'Goners', which are toxic for the neoplasm and potentially constitute therapeutic targets. Drivers are considered essential for malignant transformation, whereas environmental influences vary considerably among both types of polygenic diseases. For each form, hyper-rare disorders, defined as affecting <1/108 individuals, likely represent the largest number of disease entities. Loss of redundant tumor-suppressor genes exemplifies such a profoundly rare mutational event. For non-tumor, polygenic diseases, pathway-centered taxonomies seem preferable. This classification is not readily feasible in cancer, but the inclusion of Drivers and possibly also of epigenetic changes to the existing nomenclature might serve as initial steps in this direction. Based on the detailed genetic alterations, the number of polygenic diseases is essentially countless, but different forms of nosologies may be used to restrict the number.

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

Decoding complex inherited phenotypes in rare disorders: the DECIPHERD initiative for rare undiagnosed diseases in Chile

Rare diseases affect millions of people worldwide, and most have a genetic etiology. The incorporation of next-generation sequencing into clinical settings, particularly exome and genome sequencing, has resulted in an unprecedented improvement in diagnosis and discovery in the past decade. Nevertheless, these tools are unavailable in many countries, increasing health care gaps between high- and low-and-middle-income countries and prolonging the "diagnostic odyssey" for patients. To advance genomic diagnoses in a setting of limited genomic resources, we developed DECIPHERD, an undiagnosed diseases program in Chile. DECIPHERD was implemented in two phases: training and local development. The training phase relied on international collaboration with Baylor College of Medicine, and the local development was structured as a hybrid model, where clinical and bioinformatics analysis were performed in-house and sequencing outsourced abroad, due to lack of high-throughput equipment in Chile. We describe the implementation process and findings of the first 103 patients. They had heterogeneous phenotypes, including congenital anomalies, intellectual disabilities and/or immune system dysfunction. Patients underwent clinical exome or research exome sequencing, as solo cases or with parents using a trio design. We identified pathogenic, likely pathogenic or variants of unknown significance in genes related to the patients´ phenotypes in 47 (45.6%) of them. Half were de novo informative variants, and half of the identified variants have not been previously reported in public databases. DECIPHERD ended the diagnostic odyssey for many participants. This hybrid strategy may be useful for settings of similarly limited genomic resources and lead to discoveries in understudied populations.

The Role of Patient Organizations in Shaping Research, Health Policies, and Health Services for Rare Genetic Diseases: The Dutch Experience

In 2023, the genetics scientific community celebrated two special anniversaries: the discovery of the double helix structure of DNA was published in 1953 and in 2003 the Human Genome Project was declared completed and made publicly available. To this day, genetics and genomics research is continuing to evolve at high pace and is identifying a steadily increasing number of genes as causal for distinct genetic diseases. The success story of genetics and genomics would not be complete without taking due account of the role of patient advocacy organizations in this process. This paper is based on the personal narrative (oral history) of a father whose daughter was born with a rare genetic disease (RGD) in the 1960s. The first-hand experience of living as a family with an RGD in those days made him a leading pioneer not only in the foundation of patient organizations at national, pan-European, and international levels but also in the development of multi-stakeholder co-operation and networking. Today, patient advocacy organizations play an active role in shaping health and research policies at national, EU, and international levels to ensure that their needs in regard to advancing RGD diagnostics, care, and treatment are addressed.

Bayesian Estimation of Allele-Specific Expression in the Presence of Phasing Uncertainty

Motivation

Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors.

Results

We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways.

Availability and Implementation

The software is freely available from https://github.com/x811zou/BEASTIE . BEASTIE is available as Python source code and as a Docker image.

Supplementary information

Additional information is available online.

The clinical and genetic spectrum of inherited glycosylphosphatidylinositol deficiency disorders

Inherited glycosylphosphatidylinositol deficiency disorders (IGDs) are a group of rare multisystem disorders arising from pathogenic variants in glycosylphosphatidylinositol anchor pathway (GPI-AP) genes. Despite associating 24 of at least 31 GPI-AP genes with human neurogenetic disease, prior reports are limited to single genes without consideration of the GPI-AP as a whole and with limited natural history data. In this multinational retrospective observational study, we systematically analyse the molecular spectrum, phenotypic characteristics and natural history of 83 individuals from 75 unique families with IGDs, including 70 newly reported individuals; the largest single cohort to date. Core clinical features were developmental delay or intellectual disability (DD/ID, 90%), seizures (83%), hypotonia (72%) and motor symptoms (64%). Prognostic and biologically significant neuroimaging features included cerebral atrophy (75%), cerebellar atrophy (60%), callosal anomalies (57%) and symmetric restricted diffusion of the central tegmental tracts (60%). Sixty-one individuals had multisystem involvement including gastrointestinal (66%), cardiac (19%) and renal (14%) anomalies. Though dysmorphic features were appreciated in 82%, no single dysmorphic feature had a prevalence >30%, indicating substantial phenotypic heterogeneity. Follow-up data were available for all individuals, 15 of whom were deceased at the time of writing. Median age at seizure onset was 6 months. Individuals with variants in synthesis stage genes of the GPI-AP exhibited a significantly shorter time to seizure onset than individuals with variants in transamidase and remodelling stage genes of the GPI-AP (P = 0.046). Forty individuals had intractable epilepsy. The majority of individuals experienced delayed or absent speech (95%), motor delay with non-ambulance (64%), and severe-to-profound DD/ID (59%). Individuals with a developmental epileptic encephalopathy (51%) were at greater risk of intractable epilepsy (P = 0.003), non-ambulance (P = 0.035), ongoing enteral feeds (P < 0.001) and cortical visual impairment (P = 0.007). Serial neuroimaging showed progressive cerebral volume loss in 87.5% and progressive cerebellar atrophy in 70.8%, indicating a neurodegenerative process. Genetic analyses identified 93 unique variants (106 total), including 22 novel variants. Exploratory analyses of genotype-phenotype correlations using unsupervised hierarchical clustering identified novel genotypic predictors of clinical phenotype and long-term outcome with meaningful implications for management. In summary, we expand both the mild and severe phenotypic extremities of the IGDs, provide insights into their neurological basis, and vitally, enable meaningful genetic counselling for affected individuals and their families.

Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings

Individuals with ultrarare disorders pose a structural challenge for healthcare systems since expert clinical knowledge is required to establish diagnoses. In TRANSLATE NAMSE, a 3-year prospective study, we evaluated a novel diagnostic concept based on multidisciplinary expertise in Germany. Here we present the systematic investigation of the phenotypic and molecular genetic data of 1,577 patients who had undergone exome sequencing and were partially analyzed with next-generation phenotyping approaches. Molecular genetic diagnoses were established in 32% of the patients totaling 370 distinct molecular genetic causes, most with prevalence below 1:50,000. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were identified, mainly in individuals with neurodevelopmental disorders. Sequencing data of the subcohort that consented to computer-assisted analysis of their facial images with GestaltMatcher could be prioritized more efficiently compared with approaches based solely on clinical features and molecular scores. Our study demonstrates the synergy of using next-generation sequencing and phenotyping for diagnosing ultrarare diseases in routine healthcare and discovering novel etiologies by multidisciplinary teams.

Characterization of patients with aHUS and associated triggers or clinical conditions: A Global aHUS Registry analysis

INTRODUCTION

Atypical haemolytic uremic syndrome (aHUS) is a rare form of thrombotic microangiopathy (TMA) associated with complement dysregulation; aHUS may be associated with other 'triggers' or 'clinical conditions'. This study aimed to characterize this patient population using data from the Global aHUS Registry, the largest collection of real-world data on patients with aHUS.

METHODS

Patients enrolled in the Global aHUS Registry between April 2012 and June 2021 and with recorded aHUS-associated triggers or clinical conditions prior/up to aHUS onset were analysed. aHUS was diagnosed by the treating physician. Data were classified by age at onset of aHUS (< or ≥18 years) and additionally by the presence/absence of identified pathogenic complement genetic variant(s) and/or anti-complement factor H (CFH) antibodies. Genetically/immunologically untested patients were excluded.

RESULTS

1947 patients were enrolled in the Global aHUS Registry by June 2021, and 349 (17.9%) met inclusion criteria. 307/349 patients (88.0%) had a single associated trigger or clinical condition and were included in the primary analysis. Malignancy was most common (58/307, 18.9%), followed by pregnancy and acute infections (both 53/307, 17.3%). Patients with an associated trigger or clinical condition were generally more likely to be adults at aHUS onset.

CONCLUSION

Our analysis suggests that aHUS-associated triggers or clinical conditions may be organized into clinically relevant categories, and their presence does not exclude the concurrent presence of pathogenic complement genetic variants and/or anti-CFH antibodies. Considering a diagnosis of aHUS with associated triggers or clinical conditions in patients presenting with TMA may allow faster and more appropriate treatment.

Severity of effect considerations regarding the use of mutation as a toxicological endpoint for risk assessment: A report from the 8th International Workshop on Genotoxicity Testing (IWGT)

Exposure levels without appreciable human health risk may be determined by dividing a point of departure on a dose-response curve (e.g., benchmark dose) by a composite adjustment factor (AF). An "effect severity" AF (ESAF) is employed in some regulatory contexts. An ESAF of 10 may be incorporated in the derivation of a health-based guidance value (HBGV) when a "severe" toxicological endpoint, such as teratogenicity, irreversible reproductive effects, neurotoxicity, or cancer was observed in the reference study. Although mutation data have been used historically for hazard identification, this endpoint is suitable for quantitative dose-response modeling and risk assessment. As part of the 8th International Workshops on Genotoxicity Testing, a sub-group of the Quantitative Analysis Work Group (WG) explored how the concept of effect severity could be applied to mutation. To approach this question, the WG reviewed the prevailing regulatory guidance on how an ESAF is incorporated into risk assessments, evaluated current knowledge of associations between germline or somatic mutation and severe disease risk, and mined available data on the fraction of human germline mutations expected to cause severe disease. Based on this review and given that mutations are irreversible and some cause severe human disease, in regulatory settings where an ESAF is used, a majority of the WG recommends applying an ESAF value between 2 and 10 when deriving a HBGV from mutation data. This recommendation may need to be revisited in the future if direct measurement of disease-causing mutations by error-corrected next generation sequencing clarifies selection of ESAF values.

Unveiling the genetic tapestry: Rare disease genomics of spinal muscular atrophy and phenylketonuria proteins

Rare diseases, defined by their low prevalence, present significant challenges, including delayed detection, expensive treatments, and limited research. This study delves into the genetic basis of two noteworthy rare diseases in Saudi Arabia: Phenylketonuria (PKU) and Spinal Muscular Atrophy (SMA). PKU, resulting from mutations in the phenylalanine hydroxylase (PAH) gene, exhibits geographical variability and impacts intellectual abilities. SMA, characterized by motor neuron loss, is linked to mutations in the survival of motor neuron 1 (SMN1) gene. Recognizing the importance of unveiling signature genomics in rare diseases, we conducted a quantitative study on PAH and SMN1 proteins of multiple organisms by employing various quantitative techniques to assess genetic variations. The derived signature-genomics contributes to a deeper understanding of these critical genes, paving the way for enhanced diagnostics for disorders associated with PAH and SMN1.

Exome and genome sequencing in a heterogeneous population of patients with rare disease: Identifying predictors of a diagnosis

PURPOSE

Exome (ES) and genome sequencing (GS) are increasingly being utilized for individuals with rare and undiagnosed diseases; however, guidelines on their use remain limited. This study aimed to identify factors associated with diagnosis by ES and/or GS in a heterogeneous population of patients with rare and undiagnosed diseases.

METHODS

In this case control study, we reviewed data from 400 diagnosed and 400 undiagnosed randomly selected participants in the Undiagnosed Diseases Network, all of whom had undergone ES and/or GS. We analyzed factors associated with receiving a diagnosis by ES and/or GS.

RESULTS

Factors associated with a decreased odds of being diagnosed included adult symptom onset, singleton sequencing, and having undergone ES and/or GS before acceptance to the Undiagnosed Diseases Network (48%, 51%, and 32% lower odds, respectively). Factors that increased the odds of being diagnosed by ES and/or GS included having primarily neurological symptoms and having undergone prior chromosomal microarray testing (44% and 59% higher odds, respectively).

CONCLUSION

We identified several factors that were associated with receiving a diagnosis by ES and/or GS. This will ideally inform the utilization of ES and/or GS and help manage expectations of individuals and families undergoing these tests.

mRNA therapies: Pioneering a new era in rare genetic disease treatment

Rare genetic diseases, often referred to as orphan diseases due to their low prevalence and limited treatment options, have long posed significant challenges to our medical system. In recent years, Messenger RNA (mRNA) therapy has emerged as a highly promising treatment approach for various diseases caused by genetic mutations. Chemically modified mRNA is introduced into cells using carriers like lipid-based nanoparticles (LNPs), producing functional proteins that compensate for genetic deficiencies. Given the advantages of precise dosing, biocompatibility, transient expression, and minimal risk of genomic integration, mRNA therapies can safely and effectively correct genetic defects in rare diseases and improve symptoms. Currently, dozens of mRNA drugs targeting rare diseases are undergoing clinical trials. This comprehensive review summarizes the progress of mRNA therapy in treating rare genetic diseases. It introduces the development, molecular design, and delivery systems of mRNA therapy, highlighting their research progress in rare genetic diseases based on protein replacement and gene editing. The review also summarizes research progress in various rare disease models and clinical trials. Additionally, it discusses the challenges and future prospects of mRNA therapy. Researchers are encouraged to join this field and collaborate to advance the clinical translation of mRNA therapy, bringing hope to patients with rare genetic diseases.

Functional prediction of the potential NGLY1 mutations associated with rare disease CDG

Genetic diseases are currently diagnosed by functional mutations. However, only some mutations are associated with disease. It is necessary to establish a quick prediction model for clinical screening. Pathogenic mutations in NGLY1 cause a rare autosomal recessive disease known as congenital disorder of deglycosylation (NGLY1-CDDG). Although NGLY1-CDDG can be diagnosed through gene sequencing, clinical relevance of a detected mutation in NGLY1 needs to be further confirmed. In this study, taken NGLY1-CDDG as an example, a comprehensive and practical predictive model for pathogenic mutations on NGLY1 through an NGLY1/Glycopeptide complex model was constructed, the binding sites of NGLY1 and glycopeptides were simulated, and an in vitro enzymatic assay system was established to facilitate quick clinical decisions for NGLY1-CDDG patients. The docking model covers 42 % of reported NGLY1-CDDG missense mutations (5/12). All reported mutations were subjected to in vitro enzymatic assay in which 18 mutations were dysfunctional (18/30). In addition, a full spectrum of functional R328 mutations was assayed and 11 mutations were dysfunctional (11/19). In this study, a model of NGLY1 and glycopeptides was built for potential functional mutations in NGLY1. In addition, the effect of potential regulatory compounds, including N-acetyl-l-cysteine and dithiothreitol, on NGLY1 was examined. The established in vitro assay may serve as a standard protocol to facilitate rapid diagnosis of all mutations in NGLY1-CDDG. This method could also be applied as a comprehensive and practical predictive model for the other rare genetic diseases.

Burden of Mendelian disorders in a large Middle Eastern biobank

BACKGROUND

Genome sequencing of large biobanks from under-represented ancestries provides a valuable resource for the interrogation of Mendelian disease burden at world population level, complementing small-scale familial studies.

METHODS

Here, we interrogate 6045 whole genomes from Qatar-a Middle Eastern population with high consanguinity and understudied mutational burden-enrolled at the national Biobank and phenotyped for 58 clinically-relevant quantitative traits. We examine a curated set of 2648 Mendelian genes from 20 panels, annotating known and novel pathogenic variants and assessing their penetrance and impact on the measured traits.

RESULTS

We find that 62.5% of participants are carriers of at least 1 known pathogenic variant relating to recessive conditions, with homozygosity observed in 1 in 150 subjects (0.6%) for which Peninsular Arabs are particularly enriched versus other ancestries (5.8-fold). On average, 52.3 loss-of-function variants were found per genome, 6.5 of which affect a known Mendelian gene. Several variants annotated in ClinVar/HGMD as pathogenic appeared at intermediate frequencies in this cohort (1-3%), highlighting Arab founder effect, while others have exceedingly high frequencies (> 5%) prompting reconsideration as benign. Furthermore, cumulative gene burden analysis revealed 56 genes having gene carrier frequency > 1/50, including 5 ACMG Tier 3 panel genes which would be candidates for adding to newborn screening in the country. Additionally, leveraging 58 biobank traits, we systematically assess the impact of novel/rare variants on phenotypes and discover 39 candidate large-effect variants associating with extreme quantitative traits. Furthermore, through rare variant burden testing, we discover 13 genes with high mutational load, including 5 with impact on traits relevant to disease conditions, including metabolic disorder and type 2 diabetes, consistent with the high prevalence of these conditions in the region.

CONCLUSIONS

This study on the first phase of the growing Qatar Genome Program cohort provides a comprehensive resource from a Middle Eastern population to understand the global mutational burden in Mendelian genes and their impact on traits in seemingly healthy individuals in high consanguinity settings.

Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield

PURPOSE

Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching.

METHODS

Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim.

RESULTS

33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3.

CONCLUSION

Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

Prioritization of oligogenic variant combinations in whole exomes

MOTIVATION

Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion.

RESULTS

We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores.

AVAILABILITY AND IMPLEMENTATION

Hop is available at https://github.com/oligogenic/HOP.

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

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

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

BACKGROUND

Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest.

RESULTS

GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed.

CONCLUSIONS

GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

Drug Repurposing and Lysosomal Storage Disorders: A Trick to Treat

Rare diseases, or orphan diseases, are defined as diseases affecting a small number of people compared to the general population. Among these, we find lysosomal storage disorders (LSDs), a cluster of rare metabolic diseases characterized by enzyme mutations causing abnormal glycolipid storage. Drug repositioning involves repurposing existing approved drugs for new therapeutic applications, offering advantages in cost, time savings, and a lower risk of failure. We present a comprehensive analysis of existing drugs, their repurposing potential, and their clinical implications in the context of LSDs, highlighting the necessity of mutation-specific approaches. Our review systematically explores the landscape of drug repositioning as a means to enhance LSDs therapies. The findings advocate for the strategic repositioning of drugs, accentuating its role in expediting the discovery of effective treatments. We conclude that drug repurposing represents a viable pathway for accelerating therapeutic discovery for LSDs, emphasizing the need for the careful evaluation of drug efficacy and toxicity in disease-specific contexts.

RDmaster: A novel phenotype-oriented dialogue system supporting differential diagnosis of rare disease

BACKGROUND

Clinicians often lack the necessary expertise to differentially diagnose multiple underlying rare diseases (RDs) due to their complex and overlapping clinical features, leading to misdiagnoses and delayed treatments. The aim of this study is to develop a novel electronic differential diagnostic support system for RDs.

METHOD

Through integrating two Bayesian diagnostic methods, a candidate list was generated with enhance clinical interpretability for the further Q&A based differential diagnosis (DDX). To achieve an efficient Q&A dialogue strategy, we introduce a novel metric named the adaptive information gain and Gini index (AIGGI) to evaluate the expected gain of interrogated phenotypes within real-time diagnostic states.

RESULTS

This DDX tool called RDmaster has been implemented as a web-based platform (http://rdmaster.nbscn.org/). A diagnostic trial involving 238 published RD patients revealed that RDmaster outperformed existing RD diagnostic tools, as well as ChatGPT, and was shown to enhance the diagnostic accuracy through its Q&A system.

CONCLUSIONS

The RDmaster offers an effective multi-omics differential diagnostic technique and outperforms existing tools and popular large language models, particularly enhancing differential diagnosis in collecting diagnostically beneficial phenotypes.

Next-generation sequencing and bioinformatics in rare movement disorders

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

Coding undiagnosed rare disease patients in health information systems: recommendations from the RD-CODE project

BACKGROUND

In European Union countries, any disease affecting less than 5 people in 10,000 is considered rare. As expertise is scarce and rare diseases (RD) are complex, RD patients can remain undiagnosed for many years. The period of searching for a diagnosis, called diagnostic delay, sometimes leads to a diagnostic dead end when the patient's disease is impossible to diagnose after undergoing all available investigations. In recent years, extensive efforts have been made to support the implementation of ORPHA nomenclature in health information systems (HIS) so as to allow RD coding. Until recently, the nomenclature only encompassed codes for specific RD. Persons suffering from a suspected RD who could not be diagnosed even after full investigation, could not be coded with ORPHAcodes. The recognition of the RD status is necessary for patients, even if they do not have a precise diagnosis. It can facilitate reimbursement of care, be socially and psychologically empowering, and grant them access to scientific advances.

RESULTS

The RD-CODE project aimed at making those patients identifiable in HIS in order to produce crucial epidemiological data. Undiagnosed patients were defined as patients for whom no clinically-known disorder could be confirmed by an expert center after all reasonable efforts to obtain a diagnosis according to the state-of-the-art and diagnostic capabilities available. Three recommendations for the coding of undiagnosed RD patients were produced by a multi-stakeholder panel of experts: 1/ Capture the diagnostic ascertainment for all rare disease cases; 2/ Use the newly created ORPHAcode (ORPHA:616874 "Rare disorder without a determined diagnosis after full investigation"), available in the Orphanet nomenclature: as the code is new, guidelines are essential to ensure its correct and homogeneous use for undiagnosed patients' identification in Europe and beyond; 3/ Use additional descriptors in registries.

CONCLUSIONS

The recommendations can now be implemented in HIS (electronic health records and/or registries) and could be a game-changer for patients, clinicians and researchers in the field, enabling assessment of the RD population, including undiagnosed patients, adaptation of policy measures including financing for care and research programs, and to improved access of undiagnosed patients to research programs.

Gene Therapy for Genetic Syndromes: Understanding the Current State to Guide Future Care

Gene therapy holds promise as a life-changing option for individuals with genetic variants that give rise to disease. FDA-approved gene therapies for Spinal Muscular Atrophy (SMA), cerebral adrenoleukodystrophy, β-Thalassemia, hemophilia A/B, retinal dystrophy, and Duchenne Muscular Dystrophy have generated buzz around the ability to change the course of genetic syndromes. However, this excitement risks over-expansion into areas of genetic disease that may not fit the current state of gene therapy. While in situ (targeted to an area) and ex vivo (removal of cells, delivery, and administration of cells) approaches show promise, they have a limited target ability. Broader in vivo gene therapy trials have shown various continued challenges, including immune response, use of immune suppressants correlating to secondary infections, unknown outcomes of overexpression, and challenges in driving tissue-specific corrections. Viral delivery systems can be associated with adverse outcomes such as hepatotoxicity and lethality if uncontrolled. In some cases, these risks are far outweighed by the potentially lethal syndromes for which these systems are being developed. Therefore, it is critical to evaluate the field of genetic diseases to perform cost-benefit analyses for gene therapy. In this work, we present the current state while setting forth tools and resources to guide informed directions to avoid foreseeable issues in gene therapy that could prevent the field from continued success.

Rationale and protocol paper for the Asia Pacific Network for inherited eye diseases

PURPOSE

There are major gaps in our knowledge of hereditary ocular conditions in the Asia-Pacific population, which comprises approximately 60% of the world's population. Therefore, a concerted regional effort is urgently needed to close this critical knowledge gap and apply precision medicine technology to improve the quality of lives of these patients in the Asia-Pacific region.

DESIGN

Multi-national, multi-center collaborative network.

METHODS

The Research Standing Committee of the Asia-Pacific Academy of Ophthalmology and the Asia-Pacific Society of Eye Genetics fostered this research collaboration, which brings together renowned institutions and experts for inherited eye diseases in the Asia-Pacific region. The immediate priority of the network will be inherited retinal diseases (IRDs), where there is a lack of detailed characterization of these conditions and in the number of established registries.

RESULTS

The network comprises 55 members from 35 centers, spanning 12 countries and regions, including Australia, China, India, Indonesia, Japan, South Korea, Malaysia, Nepal, Philippines, Singapore, Taiwan, and Thailand. The steering committee comprises ophthalmologists with experience in consortia for eye diseases in the Asia-Pacific region, leading ophthalmologists and vision scientists in the field of IRDs internationally, and ophthalmic geneticists.

CONCLUSIONS

The Asia Pacific Inherited Eye Disease (APIED) network aims to (1) improve genotyping capabilities and expertise to increase early and accurate genetic diagnosis of IRDs, (2) harmonise deep phenotyping practices and utilization of ontological terms, and (3) establish high-quality, multi-user, federated disease registries that will facilitate patient care, genetic counseling, and research of IRDs regionally and internationally.

Spinocerebellar ataxia 27B: A novel, frequent and potentially treatable ataxia

Hereditary ataxias, especially when presenting sporadically in adulthood, present a particular diagnostic challenge owing to their great clinical and genetic heterogeneity. Currently, up to 75% of such patients remain without a genetic diagnosis. In an era of emerging disease-modifying gene-stratified therapies, the identification of causative alleles has become increasingly important. Over the past few years, the implementation of advanced bioinformatics tools and long-read sequencing has allowed the identification of a number of novel repeat expansion disorders, such as the recently described spinocerebellar ataxia 27B (SCA27B) caused by a (GAA)•(TTC) repeat expansion in intron 1 of the fibroblast growth factor 14 (FGF14) gene. SCA27B is rapidly gaining recognition as one of the most common forms of adult-onset hereditary ataxia, with several studies showing that it accounts for a substantial number (9-61%) of previously undiagnosed cases from different cohorts. First natural history studies and multiple reports have already outlined the progression and core phenotype of this novel disease, which consists of a late-onset slowly progressive pan-cerebellar syndrome that is frequently associated with cerebellar oculomotor signs, such as downbeat nystagmus, and episodic symptoms. Furthermore, preliminary studies in patients with SCA27B have shown promising symptomatic benefits of 4-aminopyridine, an already marketed drug. This review describes the current knowledge of the genetic and molecular basis, epidemiology, clinical features and prospective treatment strategies in SCA27B.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Prenatal and postnatal genetic testing toward personalized care: The non-invasive perinatal testing

This article investigates how non-invasive prenatal testing and the incorporation of genomic sequencing into newborn screening postnatally are transforming perinatal care. They improve the accuracy of prenatal and neonatal screening, allowing for early interventions and personalized therapies. Non-invasive prenatal testing before birth and saliva-sample-based newborn genomic sequencing after birth can be collectively referred to as non-invasive perinatal testing. Non-invasive prenatal testing is particularly useful for aneuploidy, whereas performance markers worsen as DNA abnormalities shrink in size. Screening for clinically actionable diseases in childhood would be crucial to personalized medical therapy, as the postnatal period remains appropriate for screening for the great majority of monogenic disorders. While genomic data can help diagnose uncommon diseases, challenges like ethics and equity necessitate joint approaches for appropriate integration in this revolutionary journey toward personalized care.

Overlap in synaptic neurological condition susceptibility pathways and the neural pannexin 1 interactome revealed by bioinformatics analyses

Many neurological conditions exhibit synaptic impairments, suggesting mechanistic convergence. Additionally, the pannexin 1 (PANX1) channel and signaling scaffold is linked to several of these neurological conditions and is an emerging regulator of synaptic development and plasticity; however, its synaptic pathogenic contributions are relatively unexplored. To this end, we explored connections between synaptic neurodevelopmental disorder and neurodegenerative disease susceptibility genes discovered by genome-wide association studies (GWASs), and the neural PANX1 interactome (483 proteins) identified from mouse Neuro2a (N2a) cells. To identify shared susceptibility genes, we compared synaptic suggestive GWAS candidate genes amongst autism spectrum disorders, schizophrenia, Parkinson's disease, and Alzheimer's disease. To further probe PANX1 signaling pathways at the synapse, we used bioinformatics tools to identify PANX1 interactome signaling pathways and protein-protein interaction clusters. To shed light on synaptic disease mechanisms potentially linking PANX1 and these four neurological conditions, we performed additional cross-analyses between gene ontologies enriched for the PANX1 synaptic and disease-susceptibility gene sets. Finally, to explore the regional specificity of synaptic PANX1-neurological condition connections, we identified brain region-specific elevations of synaptic PANX1 interactome and GWAS candidate gene set transcripts. Our results confirm considerable overlap in risk genes for autism spectrum disorders and schizophrenia and identify potential commonalities in genetic susceptibility for neurodevelopmental disorders and neurodegenerative diseases. Our findings also pinpointed novel putative PANX1 links to synaptic disease-associated pathways, such as regulation of vesicular trafficking and proteostasis, warranting further validation.

SLC6A1 variant pathogenicity, molecular function and phenotype: a genetic and clinical analysis

Genetic variants in the SLC6A1 gene can cause a broad phenotypic disease spectrum by altering the protein function. Thus, systematically curated clinically relevant genotype-phenotype associations are needed to understand the disease mechanism and improve therapeutic decision-making. We aggregated genetic and clinical data from 172 individuals with likely pathogenic/pathogenic (lp/p) SLC6A1 variants and functional data for 184 variants (14.1% lp/p). Clinical and functional data were available for a subset of 126 individuals. We explored the potential associations of variant positions on the GAT1 3D structure with variant pathogenicity, altered molecular function and phenotype severity using bioinformatic approaches. The GAT1 transmembrane domains 1, 6 and extracellular loop 4 (EL4) were enriched for patient over population variants. Across functionally tested missense variants (n = 156), the spatial proximity from the ligand was associated with loss-of-function in the GAT1 transporter activity. For variants with complete loss of in vitro GABA uptake, we found a 4.6-fold enrichment in patients having severe disease versus non-severe disease (P = 2.9 × 10-3, 95% confidence interval: 1.5-15.3). In summary, we delineated associations between the 3D structure and variant pathogenicity, variant function and phenotype in SLC6A1-related disorders. This knowledge supports biology-informed variant interpretation and research on GAT1 function. All our data can be interactively explored in the SLC6A1 portal (https://slc6a1-portal.broadinstitute.org/).

Prioritization of research engaged with rare disease stakeholders: a systematic review and thematic analysis

BACKGROUND

Although rare diseases (RD) are increasingly becoming a priority for healthcare activities and services around the world, developing research policy for investigating RD in public settings proves challenging due to the limited nature of existing evidence. Rare conditions require the involvement of a wide range of stakeholders in order to promote general awareness and garner political support. Consequently, it is critically important to identify trends in the various types of research focusing on rare disease stakeholders, including the specific topics or issues to be included in surveys and studies focused on RD stakeholders. This systematic review and thematic analysis analyses the existing literature based on RD surveys, including the stakeholders involved, and proposes potential research priorities and initiatives for policy-making related to RD.

METHODS

Articles were downloaded and analyzed from across five electronic databases (PubMed, EMBASE, Cochrane Central, Web of Science, and CINHAL) and 115 studies were included.

RESULTS

Across 115 studies, the main research participants were patients and/or caregivers (n = 77, 67.0%), health professionals (n = 18, 15.7%), and the public (n = 7, 6.1%). The studies discussed RDs in general (n = 46, 40.0%), endocrine, nutritional, and metabolic diseases (n = 20, 17.4%) and other RDs. Experiences with RD were examined by more than half of the selected studies (n = 74, 64.3%), followed by the opinions of stakeholders (n = 24, 20.9%). Most of the studies used surveys in order to collect relevant data (n = 114, 99.1%). Additionally, the majority of the studies were conducted in high-income countries (n = 92, 80.0%) and rarely in middle and low-income countries (n = 12, 13.8%).

CONCLUSION

Stakeholder research on RD reveals that there are significant instances of unmet needs and various challenges faced by the medical system in dealing with RDs. Furthermore, public awareness and support is critical to ensuring political feasibility of increasing national-level investments for RDs and development of medical products and treatment.