Rare genetic diseases (RDs), though individually uncommon, collectively impose a substantial global burden with significant social, emotional and economic implications. Understanding the lived experiences of RD patients, caregivers and service providers is essential to fully address the challenges they face. This study presents a narrative synthesis of original qualitative research on RDs published between 2004 and 2024, identifying 317 studies across multiple databases. Reflexive thematic analysis was used to synthesise and interpret the findings, allowing for an integrative understanding of both commonalities and disparities in experiences and research focus globally. While studies from Europe (45%) and North America (32%) dominated the field, markedly fewer included participants from Africa (3%), Asia (11%) and South America (2%), particularly from low- to middle-income countries. Across studies, recurring themes included navigating emotional resilience; the redefinition of identity in the face of RD; the social experience of illness; healthcare experiences, including access to and quality of healthcare services; the financial and logistical burden of care; the experience of research and new technologies; and the influence of society, culture and power structures. The pronounced underrepresentation of LMIC settings, persisting despite targeted search efforts, is itself a substantive finding, raising critical questions about where rare disease knowledge is produced and whose experiences are considered worthy of formal documentation. The synthesis underscores the urgent need for geographically inclusive, methodologically diverse and community-engaged approaches to qualitative RD research.

Face2Gene is a clinical tool that leverages facial features to aid genetic diagnosis. The DeepGestalt application suggests potential diagnoses based on facial similarity, while the D-Score evaluates likelihood of an individual having dysmorphic features suggestive of a possible genetic diagnosis. Given performance variability across populations and limited data from South Africa, this study assessed clinical utility in South African children with neurodevelopmental disorders (NDDs). Facial photographs from 301 children were analysed. The cohort comprised three groups: 36 children with NDDs with confirmed molecular diagnoses, 176 with NDDs without molecular diagnoses and 89 unaffected children. Diagnostic (recognition) accuracy was measured by whether the confirmed diagnosis appeared in the top-1 or top-10 ranked algorithm-generated suggestions (DeepGestalt). D-Scores were extracted to calculate group differences. Among children with confirmed molecular diagnoses, accuracy was 19% (95% CI: 9-35%) (top-1) and 34% (95% CI: 20-52%) (top-10), improving to 33% (95% CI: 16-56%) and 61% (95% CI: 39-80%) when limited to conditions included in the DeepGestalt training set. One-way ANOVA revealed differences between participants with and without significant dysmorphic features, as assessed by clinicians. The D-Score demonstrated moderate sensitivity (78%, [95% CI: 0.64, 0.88]) and low specificity (42%, [95% CI: 0.38, 0.50]), but high negative predictive value (91%, [95% CI: 0.84, 0.95]), suggesting it may be more useful for ruling out dysmorphism; however, the low specificity indicates a high rate of false positives, even among clinically non-dysmorphic children. These findings suggest that under-representation of African populations may limit clinical performance and equity of AI-based facial phenotyping tools.

Arboleda-Tham Syndrome (ARTHS), caused by truncating variants in KAT6A, is currently diagnosed as a single neurodevelopmental syndrome with variable severity of intellectual disability and multi-system findings. Here, we reveal that this clinical stratification reflects fundamentally distinct molecular mechanisms driven by variant position in the gene. Using patient-derived iPSCs and multi-omics profiling, we demonstrate that early-truncating variants (exons 1-15) cause loss-of-function via nonsense-mediated decay (NMD), while late-truncating variants (exons 16-17) that escape NMD cause gain-of-function effects. These opposite mechanisms are reflected in distinctive facial gestalt features and DNA-methylation episignatures and invert the direction of change across neuronal gene regulation, metabolism, and mitochondrial physiology. This mechanistic distinction enables precision therapeutics: late-truncating variants are amenable to KAT6A inhibition, while early-truncating variants require loss-of-function rescue. Variant-level stratification is therefore essential: mechanistic understanding, not gene-level diagnosis alone, is prerequisite for developing rational therapeutic strategies in rare Mendelian disease. ### Competing Interest Statement The authors have declared no competing interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Institutional Review Board (IRB) and the Embryonic Stem Cell Research Oversight (ESCRO) committees at UCLA. All biological samples were collected after informed consent (IRB#11-001087). Patients were recruited under IRB approvals at UCLA, the Sick Kids Hopital, and the University of Bonn; the study conformed to the Declaration of Helsinki, and all participants or their legal guardians gave written informed consent, including explicit consent for publication of identifiable facial photographs. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Sequencing data is deposited in the Gene Expression Omnibus (GEO) and will be made public upon publication. Raw proteomics mass spectrometry data is deposited in the MassIVE repository and will be made publica upon publication. The raw individual-level clinical data reported in this study cannot be deposited in a public repository due to privacy, legal, and ethical restrictions/regulations. Requests for additional information, data, resources, and/or reagents should be directed to, and will be fulfilled by, the corresponding author. National Institute of Neurological Disorders and Stroke, F31NS141668 Eli and Edythe Broad Stem Cell Training Fellowship W. M. Keck Foundation, https://ror.org/000dswa46, Junior Faculty Award Rose Hills Foundation, https://ror.org/01nebar93 California Institute for Regenerative Medicine, DISC0-14519 KAT6 Foundation

Rare diseases are a group of chronic conditions with low individual prevalence. These conditions predominantly affect children and severely impact their lives. Oftentimes, children with rare diseases receive care from family caregivers, who experience several challenges in their roles and regularly struggle to cope with caregiving demands, with consequences for their physical and psychological well-being. Therefore, enhanced understanding of caregivers' unique challenges and coping is required. This phenomenologically situated, exploratory qualitative study explored the caregiving experiences of 10 South African parents (aged 22-54) whose children have rare diseases and receive care at a public hospital in Cape Town, South Africa. These parents were recruited using a convenience strategy and interviewed thereafter. Interview transcripts were analyzed using reflexive thematic analysis to develop themes representing patterns of shared meaning across participants' narratives. Seven themes were developed, representing diverse dimensions of caregiving. First, uncertainty was a challenge that some addressed by seeking knowledge. Second, experiences in healthcare settings reflected how healthcare providers' knowledge and communication shaped caregivers' sense of support or marginalization. Third, caregivers described the all-encompassing nature of meeting children's holistic needs. Fourth, acceptance, frequently aided by normalization, was challenging yet central to coping. Fifth, caregivers' coping was shaped by structural inequities and contextual constraints. Sixth, religious beliefs, positivity, and love for their children ascribed meaning to caregiving, enabling perseverance amid adversity. Finally, caregiving was experienced as both depleting and inevitable; participants described significant physical and psychological tolls yet expressed a sense of duty that compelled them to continue. When interpreted alongside international literature, these themes suggest that while caregivers in South Africa negotiate similar meaning patterns to those reported elsewhere, their experiences are further shaped by context-specific limitations in support and healthcare infrastructure. It is vital that family caregivers receive improved support and consideration in South Africa.

The facial gestalt (overall facial morphology) is a characteristic clinical feature in many genetic disorders that is often essential for suspecting and establishing a specific diagnosis. Therefore, publishing images of individuals affected by pathogenic variants in disease-associated genes has been an important part of scientific communication. Furthermore, medical imaging data is also crucial for teaching and training deep-learning models such as GestaltMatcher. However, medical data is often sparsely available, and sharing patient images involves risks related to privacy and re-identification. Therefore, we explored whether generative neural networks can be used to synthesize accurate portraits for rare disorders. We modified a StyleGAN architecture and trained it to produce artificial condition-specific portraits for multiple disorders. In addition, we present a technique that generates a sharp and detailed average patient portrait for a given disorder. We trained our GestaltGAN on the 20 most frequent disorders from the GestaltMatcher database. We used REAL-ESRGAN to increase the resolution of portraits from the training data with low-quality and colorized black-and-white images. To augment the model’s understanding of human facial features, an unaffected class was introduced to the training data. We tested the validity of our generated portraits with 63 human experts. Our findings demonstrate the model’s proficiency in generating photorealistic portraits that capture the characteristic features of a disorder while preserving patient privacy. Overall, the output from our approach holds promise for various applications, including visualizations for publications and educational materials and augmenting training data for deep learning.

Purpose: The Clinical Genome Resource (ClinGen) Gene Curation Expert Panels have historically focused on specific organ systems or phenotypes; thus, the ClinGen Syndromic Disorders Gene Curation Expert Panel (SD-GCEP) was formed to address an unmet need. Methods: The SD-GCEP applied ClinGen’s framework to evaluate the clinical validity of genes associated with rare syndromic disorders. A total of 111 gene-disease relationships (GDRs) associated with 100 genes spanning the clinical spectrum of syndromic disorders were curated. Results: From April 2020 through March 2024, 38 precurations were performed on genes with multiple disease relationships and were reviewed to determine if the disorders were part of a spectrum or distinct entities. A total of 14 genes were lumped into a single disease entity, and 24 were split into separate entities, of which 11 were curated by the SD-GCEP. A full review of 111 GDRs for 100 genes followed, with 78 classified as Definitive, 9 as Strong, 15 as Moderate, and 9 as Limited, highlighting cases in which further data are needed. All diseases involved 2 or more organ systems, whereas the majority (88/111 GDRs, 79.2%) had 5 or more organ systems affected. Conclusion: The SD-GCEP addresses a critical gap in gene curation efforts, enabling inclusion of genes for syndromic disorders in clinical testing and contributing to keeping pace with the rapid discovery of new genetic syndromes.

While Research Electronic Data Capture (REDCap) is widely adopted in rare disease research, its unconstrained data format often lacks native interoperability with global health standards, limiting secondary use. We developed RareLink, an open-source framework implementing our published ontology-based rare disease common data model. It enables standardised data exchange between REDCap, international registries, and downstream analysis tools by linking Global Alliance for Genomics and Health Phenopackets and Health Level 7 Fast Healthcare Interoperability Resources (FHIR) instances conforming to International Patient Summary and Genomics Reporting profiles. RareLink was developed in three phases across Germany, Canada, South Africa, and Japan for registry and data analysis purposes. We defined a simulated Kabuki syndrome cohort and demonstrated data export to Phenopackets and FHIR. RareLink can enhance the clinical utility of REDCap through its global applicability, supporting equitable rare disease research. Broader adoption and coordination with international entities are thus essential to realise its full potential.

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.

Background Rare bone diseases (RBDs) are an important group of conditions characterized by abnormalities in bone and cartilage. Their large number, individual rarity, and heterogeneity make accurate and timely diagnosis challenging. Establishing correlations between genotype and phenotype (mainly via imaging) is critical for diagnosing RBDs. Image recognition artificial intelligence (AI) has the potential to significantly improve the diagnostic process by assisting healthcare providers to identify and differentiate imaging patterns associated with various RBDs. This survey study sought to assess the interest of various healthcare providers worldwide in utilizing an AI-based assistant tool for the differential diagnosis of RBDs. Method Survey data were collected from March to September 2024. The survey was performed online and the link was disseminated via direct email, newsletters, and flyers at scientific talks and conferences. Results We received 103 completed surveys, representing respondents from 27 different countries covering most global regions, but mostly from Europe, the United States, and Canada. The majority of the participants are physicians (n = 92, 89%) and primarily work at academic medical centers (n = 84, 81%). While each participant could select multiple specialties, the most frequent clinician types were medical geneticists, pediatricians, and endocrinologists, accounting for 71 (69%) of the respondents. Ninety-four (91%) of the respondents find imaging to be very or extremely important, and the majority (n = 84, 81%) consider X-rays to be the most important imaging modality. Although around half of the participants (n = 45) have concerns about AI-related errors and consider the explainability of AI algorithms to be very (42/103) or extremely (9/103) important, 81% of the respondents report that they are somewhat (n = 39) or extremely (n = 45) likely to consider integrating image recognition AI into their current diagnostic workflow. Conclusions Most survey participants are open to integrating image recognition AI into their RBD diagnostic workflow. However, concerns about AI-related errors, privacy, and model interpretability highlight the importance of transparent collaboration between developers and healthcare professionals throughout the development process to ensure that such technologies are clinically trustworthy and practically adoptable.

Next generation sequencing (NGS) based tests have become first-line investigative modalities in adult neurogenetic clinics. Studies in high-income countries (HICs) show that NGS is cost-effective and reliable in diagnosing adult neurogenetic disorders (NGDs). African populations harbour vast genomic diversity, but there is limited knowledge on the molecular basis of NGDs affecting these populations due to lack of access to the necessary technology. The primary objective of this retrospective study was to describe the clinical utility of NGS panels in an African low-middle income country (LMIC). It included data of 74 adult participants seen at the multidisciplinary neurogenetic clinic at Tygerberg Hospital, South Africa, over a 4 – year period. Forty-three symptomatic index cases underwent NGS panel testing, while 31 relatives received targeted familial variant testing based on specific indications relevant to each case. Twenty-two different disease group-specific NGS panels were requested, spanning the NGD phenotypic spectrum. The diagnostic yield (DY) in index cases was 39.5% (17/43). Four relatives were clinically affected, and all tested positive for the familial-specific variant. This study demonstrated the DY achieved with NGS testing in an LMIC adult neurogenetic cohort, was comparable to DYs previously reported in HICs. These results argue for the use of NGS panels as first-tier testing in resource constrained LMICs, to limit lengthy diagnostic odysseys and unnecessary investigations. A definitive molecular diagnosis enables evidence-based management, surveillance, genetic counselling, and familial variant screening for relatives. Lastly, it assists with enrolment into clinical trials focussed on the development of precision medicine.

Next-generation phenotyping (NGP) can be used to compute the similarity of dysmorphic patients to known syndromic diseases. So far, the technology has been evaluated in variant prioritization and classification, providing evidence for pathogenicity if the phenotype matched with other patients with a confirmed molecular diagnosis. In a Nigerian cohort of individuals with facial dysmorphism, we used the NGP tool GestaltMatcher to screen portraits prior to genetic testing and subjected individuals with high similarity scores to exome sequencing (ES). Here, we report on two individuals with global developmental delay, pulmonary artery stenosis, and genital and limb malformations for whom GestaltMatcher yielded Cornelia de Lange syndrome (CdLS) as the top hit. ES revealed a known pathogenic nonsense variant, NM_133433.4: c.598C>T; p.(Gln200*), as well as a novel frameshift variant c.7948dup; p.(Ile2650Asnfs*11) in NIPBL. Our results suggest that NGP can be used as a screening tool and thresholds could be defined for achieving high diagnostic yields in ES. Training the artificial intelligence (AI) with additional cases of the same ethnicity might further increase the positive predictive value of GestaltMatcher.

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.

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.

Shprintzen-Goldberg-syndrome (SGS) is caused by pathogenic exon 1 variants of SKI. Symptoms include dysmorphic features, skeletal and cardiovascular comorbidities, and cognitive and developmental impairments. We delineated the neurodevelopmental and behavioral features of SGS, as they are not well-documented. We collected physician-reported data of people with molecularly confirmed SGS through an international collaboration. We identified and deep-phenotyped the neurodevelopmental and behavioral features in four patients. Within our cohort, all exhibited developmental delays in motor skills and/or speech, with the average age of first words at 2 years and 6 months and independent walking at 3 years and 5 months. All four had learning disabilities and difficulties regulating emotions and behavior. Intellectual disability, ranging from borderline to moderate, was present in all four participants. Moreover, we reviewed the literature and identified 52 additional people with SGS, and summarized the features across both datasets. Mean age was 23 years (9-48 years). When combining our cohort and reported cases, we found that 80% (45/56) had developmental and/or cognitive impairment, with the remainder having normal intelligence. Our study elucidates the developmental, cognitive, and behavioral features in participants with SGS and contributes to a better understanding of this rare condition.