IntroductionThe promise of precision medicine lies in its ability to provide greater diagnostic accuracy and customized therapy by filtering out patients less likely to benefit from it. Our study focuses on the importance of reducing uncertainty in interpretation of individuals 3D facial data to support more equitable precision medicine applications. The Human Genome Project and subsequent advances in sequencing have led to the creation of vast genetic datasets, predominantly representing individuals of European origin. However, there is a significant underrepresentation of individuals of African, Asian, and Indigenous ancestries.MethodsThe study involved 1,218 participants from various genetic ancestries backgrounds, with a focus on the paediatric population of Chinese genetic ancestry. The study subjects underwent 3D facial photogrammetry in outpatient department setting and with the aid of Cliniface software growth curves were obtained to produce reference statistics of 3D facial norms.ResultsThe results showed measurable and distinct facial differences in children with Chinese genetic ancestry when compared with other groups representing different genetic ancestries highlighting the need for population diversity and inclusion enrichment in genetic databases. Also, these facial differences and markers are uniquely poised to be correlated in clinic as disease specific digital biomarkers with further investigation and validation in conditions such as hereditary angioedema.DiscussionThe study underscores the importance of creating larger datasets involving more diverse genetic ancestry groups to enhance the evidence base for advanced and equitable disease diagnosis, treatment monitoring, prognostication and customized drug development.

The aim of this study was to identify and prioritise the ten most important unanswered themes in rare disease research in Australia by integrating perspectives of key stakeholders, including people living with rare disease, parents/carers, health professionals, and rare disease community advocates. We conducted a Priority Setting Partnership project based on a modified James Lind Alliance Priority Setting Partnership methodology. The process involved an online elicitation survey, review of existing literature, and consensus workshops with Australian rare disease stakeholders. In the elicitation survey (n = 185), 585 questions/comments were coded, and 19 overarching themes identified. No themes were completely addressed by existing literature. In the consensus workshops (n = 34), the 19 themes were refined, and the Top 10 priorities determined. The top three priorities were (1) development of, and access to, the best treatments and cures, (2) awareness and education for health professionals and service providers, and (3) diagnosis, including screening. The Top 10 priorities span a wide range of health research domains, including access to effective diagnosis and treatment, healthcare system capacity and expertise, and support for patients, families, and carers. A Project Advisory Group and Steering Committee were established to guide this study. The Project Advisory Group met four times across the duration of the project, while the Steering Committee met three times to provide feedback and support recruitment and translation of findings.

In this EURORDIS paper, we explore the importance of psychological support to live will with a rare disease. The literature review highlights the importance of integrating psychological and psychosocial support into rare disease care pathways, particularly at key stages such as diagnosis and disease progression, when emotional distress and uncertainty are often highest. The findings demonstrate that family-centred and community-based approaches improve wellbeing. Interventions that strengthen coping strategies, such as acceptance, resilience, and self-efficacy, are associated with better mental health outcomes and quality of life. Access to peer support, social networks, and clear, reliable information also plays a key role in helping individuals and families manage the challenges of living with a rare condition. The paper calls for more holistic, multidisciplinary and psychologically informed care models, with psychological support embedded throughout care pathways. It also calls for stronger policy action to address gaps in provision and ensure equitable access to mental health and psychosocial support across rare disease systems.

The International Rare Diseases Research Consortium (IRDiRC) Telehealth (TH) Task Force explored the use of TH for improving diagnosis, care, research, and education for rare diseases (RDs) worldwide. The Task Force members interviewed 23 key opinion leaders (KOLs), providing perspectives from experts in the use of TH for the diagnosis, treatment, and prevention of RDs (10 KOLs); for research and evaluation in RDs (7); and for the continuing education of health care providers (HCPs) in RDs (6). The KOLs represented a broad array of diverse perspectives with regard to both geographic regions, including Europe, United States, Sub-Saharan Africa, and Asia, and professional expertise, including rare disease patients and family members, RD association spokespersons, TH association representatives, physicians, researchers, and regulatory authorities. The Task Force solicited KOL opinions to identify factors that influence TH in improving access to diagnosis, care, prevention, and research experiences for RD patients and providers as well as continuing education and peer mentoring for HCPs. This manuscript represents a synthesis of those interviews and some common themes that emerged, along with identification of evidence and knowledge gaps that will benefit from future research efforts to help advance and expand the use of TH for RD care, research, and education. KOLs agreed on the unique elements of RD medical care that could benefit from TH approaches and recognized the increasing role that remote assessments can play in supporting RD research. They identified models for health care provider education afforded by TH that can enhance care for RD patients and broaden the pool of experts in these conditions. While recognizing that barriers to broad implementation exist, they agreed that TH provides a unique tool to provide greater access to care for RD patients worldwide.

Multi-omics in combination with advanced computational methodologies synthesizes diverse omics data to provide deeper insights into molecular interactions and offers transformative potential for unravelling phenomenon behind disease complexities, improving diagnostics, disease prevention, and personalized treatments. This integrative strategy enables our understanding of gene-environment relationships, chronic disease progression, and the intricate molecular pathways involved in health. Effective multi-omics analyses require robust data sharing, accessibility, interoperability, and governance, which are critical for linking genomic elements to phenotypic traits. The Global Alliance for Genomics and Health advocates for responsible data-sharing practices, by promoting key principles such as transparency and equity. By emphasizing a collaborative approach to data utilization, our proposed framework seeks to advance improved disease prevention and treatment strategies. Multi-disciplinary collaboration, encompassing researchers, clinicians, policy makers, and patient representatives, is pivotal for driving innovation and addressing rare disease diagnostics. The success of multi-omics applications hinges on the establishment of comprehensive datasets, understanding the functional implications of multi-omic variation, adherence to findable, accessible, interoperable, reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles, and the strengthening of global genomic commons, benefiting scientific research, drug development, and broader health initiatives. Our review highlights essential components of multi-omics integration, underscoring its potential to transform the landscape of precision medicine and improved patient outcomes worldwide.

Importance: People living with rare diseases (PLWRD) often face significant challenges in receiving timely and accurate diagnoses, leading to what is known as a diagnostic odyssey. Digital phenotyping (DP) offers a promising solution by leveraging advanced technology, such as 3D facial photography, to capture unique digital signatures associated with various rare diseases. This innovative approach not only aids in the identification of these conditions but also facilitates the detection of digital biomarkers (DBM). These biomarkers enable healthcare providers to monitor the progression of the disease over time, enhancing patient care and potentially shortening the duration of the diagnostic odyssey. By utilizing DP and DBMs, we can improve both the diagnosis and management of RDs, ultimately leading to better health outcomes for affected participants. Objective: To identify whether DBMs can be identified by DP utilizing 3D facial imaging techniques in outpatient settings in participants with RDs.   The primary objective of this study was to determine if specific facial measurements in participants with RD who experience transient episodes of facial swelling (oedema) differ from established ethnically matched norms. The secondary objective was to assess peri-orbital and/or facial swelling as a potential biomarker for identifying flare-ups in hereditary angioedema (HAE). Design, setting, and participants: This multicentre observational study was conducted in 3 hospitals in Singapore. The eligible participants were male and female RD participants of various age groups. The study duration was 4 years and 8 months.     Interventions: Twenty participants of Chinese genetic ancestry were photographed using a 3D camera. Additionally, two participants with hereditary angioedema (HAE) were photographed during acute stages of disease flare-ups.   Main outcomes and measures: The obtained facial scans of participants (that included participants with HAE in non-acute phase) were plotted using Artificial Intelligence-powered software - Cliniface. The growth curves and facial landmarks obtained were compared against the growth curves of normal RD-unaffected individuals of Chinese genetic ancestry. The two participants with HAE were photographed qualitatively over a longer period of time, and their scans were plotted, yielding growth curves.   Results: Distinct facial markers such as periorbital swelling were identified in two qualitatively assessed HAE participants during flare-up stages. This provides an opportunity to explore and validate further if these facial signatures in a disease condition can be assigned as DBM for HAE.    Conclusions and relevance: This study explores the utility of 3D facial analysis as a DBM in rare diseases such as HAE. Applying non-invasive signals coupled with AI may open new vistas for precision medicine in real-world settings.While DP's diagnostic capabilities may be limited, it successfully identified DBM, which could facilitate disease monitoring in conditions such as HAE. ### Competing Interest Statement RP (under PH and GB supervision) is developer of Cliniface software. SL, CBG are employees of Takeda Pharmaceuticals (Asia Pacific) Pte Ltd. ### Funding Statement Yes ### 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: Studt approved by the KKH Women and Children's Hospital, Singapore 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 Participant-level data on which the analysis was based will not be shared to protect participant anonymity.

BACKGROUND AND OBJECTIVES:Severe microcephaly, or head circumference at least 3 standard deviations below the mean for age and sex, is a rare condition with diverse etiology, making diagnosis challenging. Following the 2015 to 2016 Zika virus outbreak, surveillance studies in Australia, Canada, New Zealand, and the United Kingdom and Ireland were conducted to monitor severe microcephaly. We describe the etiology, clinical features, and diagnostic investigations of severe microcephaly among children aged younger than 1 year. METHODS:We pooled reports of patients with severe microcephaly detected through 4 national active surveillance studies, through the International Network of Paediatric Surveillance Units. Incident cases were reported voluntarily between June 2016 and October 2018 by networks of pediatricians totaling more than 8000 members. Etiology was categorized as genetic (confirmed/suspected), acquired (infection, ischemia/hypoxia, prenatal alcohol exposure, placental insufficiency), or unknown. Anonymized data were pooled and analyzed using descriptive statistics. RESULTS:Overall, the cases of 118 patients with severe microcephaly were analyzed, including 59 from the United Kingdom and Ireland, 34 from Canada, and 25 from Australia (n < 5 cases from New Zealand were not analyzed). Median age at diagnosis was 17 days (IQR 1-119), and mean head circumference-for-age Z-score was -4.0 (SD 1.1). Genetic causes were determined for 50% (n = 59) vs 18% acquired (n = 21) and 32% unknown (n = 38). Common investigations included brain magnetic resonance imaging (70%), DNA microarray (69%), brain ultrasonography (53%), and cytomegalovirus screening (48%). CONCLUSIONS:At least one-half of severe microcephaly cases are attributable to genetic causes. One-third had unknown etiology, highlighting a need for a systematic approach to diagnostic investigation, including genomic sequencing and brain imaging for all children with severe microcephaly.

Abstract Background As part of Singapore’s effort towards precision medicine tailored to Asian diversity, we describe the implementation of a nationwide reproductive carrier screening program. Using a customised 112-gene panel, incorporating population-specific recessive genetic diseases, we outline the overall program design, and initial efforts of community and stakeholder engagement, to inform culturally appropriate implementation. Methods Participants receive culturally tailored online education regarding our reproductive screening program and are provided results with genetic counselling and reproductive options. Community and stakeholder perspectives were assessed through questionnaires and consultations with religious leaders. Results Recruitment is nation-wide, and since initiation of our pilot phase in September 2024, 1,619 couples have registered interest, with 60% uptake of those deemed eligible. Among the 456 couples that have received results to date, four couples (0.9%) were identified to be at increased risk. Community questionnaire responses (n=1002), involving couples who participated in the program as well as the general public, indicated interest is high (59%) across the cohort but awareness, intent to participate and implications for reproductive options differed by sociodemographic factors such as ancestry and religion. Healthcare professional respondents (n=113) acknowledged carrier screening will be routine in medical care, but report limited confidence and resources. Engagement with religious leaders indicated support for the program. Conclusion These early program outcomes and community engagement are guiding the implementation of expanding population-based carrier screening in Singapore, contingent on addressing practical challenges through equitable outreach and professional training.

BACKGROUND:Patients with congenital heart disease are identified in 1% of live births. Improved surgical intervention means many patients now survive to adulthood, the corollary of which is increased mortality in the over-65-year-old congenital heart disease (CHD) population. In the clinic, genetic sequencing increasingly identifies novel genetic variants in genes related to CHD. Traditional assays for interpreting novel genetic variants are often limited by gene-specificity, whereas animal models are cumbersome and may not accurately reflect human disease. This study investigates CRISPR gene editing in induced pluripotent stem cells and cardiomyocyte-directed differentiation as a human disease model to investigate novel genetic variants identified in association with CHD. METHODS AND RESULTS:We identified a GATA4 p.Arg284His genetic variant in a paediatric patient. This genetic variant was introduced into induced pluripotent stem cells (iPSCs) using CRISPR gene editing with homology-directed-repair. GATA4 genetic variant and isogenic control iPSCs were selected and differentiated into cardiomyocytes. Expression of the GATA4 p.Arg284His variant resulted in altered calcium transients, indicative of CHD and consistent with the patient's clinical phenotype. Transcriptomics revealed cellular pathway changes in cardiac development, calcium handling, and energy metabolism that contribute to disease aetiology, mechanism and identification of potential treatments. CONCLUSION:Directed differentiation of iPSCs harbouring the GATA4 p.Arg284His genetic variant recapitulated the CHD phenotype, indicated disease mechanisms, and pointed to potential sites for targeting with therapy. The study highlights the utility of transcriptomics for the functional interpretation of cardiac genetic variants and is an exemplar for precision medicine approaches for the investigation of CHD.

Background: An estimated 1 in 12 individuals across the world have a rare disease. The Undiagnosed Diseases Network International (UDNI) recommend Undiagnosed Disease Programs (UDPs) as the best approach to facilitate diagnosis and support for those affected. The Australian Undiagnosed Disease Network (UDN-Aus) is the first National Australian UDP initiative funded by the Medical Research Future Fund’s Genomic Health Futures Mission (GNT2007567). Aims: UDN-Aus brings together an unprecedented national collaborative network for rare disease, aiming to improve the rate of genomic diagnoses for those with undiagnosed rare genetic conditions, enabling precise, personalised care to individuals throughout Australia. Methods: The research program recruited Australians who have been seen through a clinical genetics service and remained undiagnosed following clinically available genomic testing. This paper outlines the approach taken to establish a national research project at 12 clinical recruitment sites. The methods detail the funding, aims, governance, study design, population and participation process, health economic research and preliminary results, and recommendations for future sustainability and implementation. Results: The study was approved by the Royal Children’s Hospital Human Research Ethics Committee on 19 November 2021 (RCH79712), with relevant site-specific approvals at local recruitment sites. Key benefits and barriers in the establishment of UDN-Aus are outlined. Conclusion: The successful establishment of this program required several components, including meaningful and ongoing community and stakeholder engagement, strategic appointment of key operational staff, and a tailored approach to facilitate more equitable enrolment. It also highlighted several imperative areas for consideration to ensure future sustainable implementation of a national UDP. These include continued investment in Australia’s national genomic data transfer policy and infrastructure, and research ethics and governance procedures is imperative for the sustainable delivery of genomic research for rare disease.

In this literature review conducted by EURORDIS, we examine the psychological and psychosocial needs of people living with a rare condition and their families. The findings highlight that people living with a rare condition and their families face common and persistent challenges. These include chronic uncertainty, limited access to reliable information, stigma, social isolation, and difficulties navigating fragmented healthcare systems. Many individuals and caregivers also take on the role of coordinating care themselves, often without adequate support. The report emphasises that psychological needs evolve over time, particularly at key stages such as diagnosis, disease progression, and transition to adult care. Despite this, psychological and psychosocial support remains a major unmet need across the rare disease community. In response, the report calls for more integrated, family-centred and multidisciplinary care, with psychological support embedded throughout the care pathway and stronger links with patient organisations to better support individuals and families.

Artificial intelligence (AI) can transform rare disease care when organized around the patient journey. We outline a patient-clinician-AI triad spanning early detection, diagnosis, clinical trials, and individualized therapies.

Rare diseases affect millions of individuals worldwide, yet timely diagnosis remains a major public health challenge due to scarcity of specialized clinical expertise. While large language models (LLMs) show promise to support rare disease diagnosis, current models are constrained by insufficient clinical deployability, limited clinically grounded evidence, and scarcity of training data. Here we present RaDaR (Rare Disease navigatoR), an open-source, compact reasoning LLM (32B parameters) for rare disease diagnosis. RaDaR was trained with 49,170 publicly available free-text cases and 104,666 synthetic cases with reasoning-enhanced training. RaDaR showed the strongest performance among evaluated open-source models, including the 671B DeepSeek-R1, across public benchmarks and four external validation centers. In a retrospective cohort, RaDaR prioritized the final diagnosis before documented clinical suspicion in 61.06 percent of cases, corresponding to a potential lead time of 1.87 months and 50.18 percent of the within-center interval. In a randomized physician-assistance trial, RaDaR assistance improved physicians' rare-disease diagnostic accuracy by 21.44 percentage points compared with internet search alone. Synthetic-data ablations suggested that phenotype-anchored narratives provide useful training signal for long-tail rare diseases, with a monotonic scaling trend within the tested data range. Together, RaDaR and its development and validation framework provide a deployable rare-disease reasoning model and a reproducible development framework for diagnostic AI under data scarcity.

This is the largest study to date to investigate the acquisition, retention and loss of functional skills in MECP2 duplication syndrome (MDS). Females were more likely than males to acquire gross and fine motor skills. Use of words was the most common parent-reported skill regression. Those with seizures had lower functional ability than those without seizures. There is a need for better understanding of the role of interventional therapy for functional skill retention in MDS. MECP2 duplication syndrome (MDS) is an ultrarare, X-linked neurodevelopmental disorder that is poorly understood in terms of its natural history and phenotypic variability. There is limited information on how individuals with MDS acquire, retain or lose fundamental functional skills (gross motor, purposeful hand function and communication) – that of which this study aimed to better characterise in the largest case series to date. For 160 individuals with MDS (median age 9.06 y, range: 0.57–51.63 y; 84

BACKGROUND:Using genomic sequencing technology at population scale as a screening test holds the promise of improving outcomes for individuals with rare diseases through early detection and timely access to precision medicine. However, the incorporation of genomics into established newborn screening programmes raises many challenges, ranging from technical feasibility and scalability through to ethical concerns regarding consent and data management. Empirical evidence and implementation experience from large-scale studies are required to guide future policy. METHODS:We provide a narrative summary of genomic newborn screening studies currently underway in Australia. FINDINGS:We summarise six research studies currently underway in Australia, which explore the application of genomic technologies in the newborn screening context. These studies have taken varying approaches to generating evidence about the implementation of genomic newborn screening and have formed a national consortium, the Genomic Screening Consortium for Australian Newborns (GenSCAN), with the aim of sharing experiences and enabling collective learning. CONCLUSIONS:Over the next decade, we can expect substantial evidence to be generated nationally and internationally to inform future policy decisions on whether to incorporate genomic sequencing into newborn screening programmes.

Large language models (LLMs) perform well on general medical benchmarks, but their ability to reason about rare diseases (RDs) remains unclear. Rather than challenge LLMs to diagnose a limited number of cases that are unlikely to represent all RDs or RD-associated genes, we instead sought to comprehensively probe LLM understanding of RD-associated genes and phenotypes. We systematically evaluated six leading general-domain LLMs (GPT-4, Claude 3.7, Llama-3.3 70B, Gemma-2 27B, Llama-3.2, and Phi-4) for their ability to generate core phenotypic features and causal genes required to support reasoning for 10,892 Orphanet diseases. Outputs were mapped to Human Phenotype Ontology (HPO) terms and HGNC gene symbols and compared with curated references using set overlap, semantic similarity, and disease ranking via the likelihood ratio interpretation of clinical abnormality (LIRICAL) framework applied to 8,000 patient Phenopackets. LLM recall of curated RD knowledge was generally low, with gene associations retrieved more accurately than phenotypes. Commercial models, particularly GPT-4 and Claude, achieved over 60% recall for gene associations but struggled with precise phenotype recovery. Despite low exact overlaps, moderate semantic similarity scores indicated partial alignment with curated data. When used in LIRICAL, LLM-derived phenotypic profiles yielded ranking performance close to that of gold standard profiles, although direct diagnostic accuracy remained limited. Interestingly, convergent non-curated terms across models suggest potential for hypothesis generation. Current generalist LLMs lack the precision to replace curated RD knowledge bases but offer complementary, semantically relevant information. Our results support hybrid approaches that combine expert curation with selectively integrated LLM outputs to enhance and scale ontology-driven RD diagnostics.