Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠-including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology-a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene-disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

PurposeMedications are commonly used during pregnancy to manage pre-existing conditions and conditions that arise during pregnancy. However, not all medications are safe to use in pregnancy. This study utilized privacy-preserving record linkage (PPRL) to examine medications dispensed under the national Pharmaceutical Benefits Scheme (PBS) to pregnant women in Western Australia (WA) overall and by medication safety category.MethodsIn this retrospective, cross-sectional, population-based study, state perinatal records (Midwives Notification Scheme) were linked with national PBS dispensing data using PPRL. Live and stillborn neonates born between 2012 and 2019 in WA were included. The proportion of pregnancies during which the mother was dispensed a PBS medication was calculated, overall and by medication safety category. Factors associated with PBS medication dispensing were examined using logistic regression.ResultsPPRL linkage identified matching records for 97.4% of women with perinatal records. A total of 271 739 pregnancies were identified, with 158 585 (58.4%) pregnancies involving the dispensing of at least one PBS medication. Category A medications (those considered safe in pregnancy) were the most commonly dispensed (n = 119 126, 43.8%) followed by B3 (n = 51 135, 18.8%) and B1 (n = 42 388, 15.6%) medication (those with unknown safety). Over the study period, the dispensing of PBS medications in pregnancy increased (OR: 1.06, 95%CI: 1.06, 1.07). The strongest predictor of medication dispensing in pregnancy was pre-pregnancy dispensing (OR: 3.61, 95%CI: 3.54, 3.68). Other factors associated with medication use in pregnancy were smoking, older maternal age, obesity, and prior pregnancies.ConclusionPrivacy preserving record linkage provides a way to link cross-jurisdictional data while preserving patient confidentiality and data security. The dispensing of PBS medication in pregnancy was common and increased over time, with approximately 60% of women dispensed at least one medication during pregnancy.

Motivation Human Phenotype Ontology (HPO)-based phenotype concept recognition (CR) underpins a faster and more effective mechanism to create patient phenotype profiles or to document novel phenotype-centred knowledge statements. While the increasing adoption of large language models (LLMs) for natural language understanding has led to several LLM-based solutions, we argue that their intrinsic resource-intensive nature is not suitable for realistic management of the phenotype CR lifecycle. Consequently, we propose to go back to the basics and adopt a dictionary-based approach that enables both an immediate refresh of the ontological concepts as well as efficient re-analysis of past data.Results We developed a dictionary-based approach using a pre-built large collection of clusters of morphologically equivalent tokens-to address lexical variability and a more effective CR step by reducing the entity boundary detection strictly to candidates consisting of tokens belonging to ontology concepts. Our method achieves state-of-the-art results (0.76 F1 on the GSC+ corpus) and a processing efficiency of 10 000 publication abstracts in 5 s.Availability and implementation FastHPOCR is available as a Python package installable via pip. The source code is available at https://github.com/tudorgroza/fast_hpo_cr. A Java implementation of FastHPOCR will be made available as part of the Fenominal Java library available at https://github.com/monarch-initiative/fenominal. The up-to-date GCS-2024 corpus is available at https://github.com/tudorgroza/code-for-papers/tree/main/gsc-2024.

ObjectiveClinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation.Materials and methodsThe experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations.ResultsThe best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches.ConclusionOur experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

The diagnostic odyssey for people living with rare diseases (PLWRD) is often prolonged for myriad reasons including an initial failure to consider rare disease and challenges to systemically and systematically identifying and tracking undiagnosed diseases across the diagnostic journey. This often results in isolation, uncertainty, a delay to targeted treatments and increase in risk of complications with significant consequences for patient and family wellbeing. This article aims to highlight key time points to consider a rare disease diagnosis along with elements to consider in the potential operational classification for undiagnosed rare diseases during the diagnostic odyssey. We discuss the need to create a coding framework that traverses all stages of the diagnostic odyssey for PLWRD along with the potential benefits this will have to PLWRD and the wider community.

There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The Monarch Initiative aims to integrate genotype, phenotype, and disease knowledge from a large variety of sources in support of improved diagnostics and mechanism discovery through various algorithms and tools. However, the lack of a unified system for managing disease entities poses a major challenge for both machines and humans to predict causes and treatments for disease. The multitude of disease resources have not been well coordinated nor computationally integrated. Furthermore, the classification of phenotypes and their association with diseases is another source of disagreement across sources. The Human Phenotype Ontology has helped to standardize phenotypic features across knowledge sources, but there was no equivalent computationally-harmonized disease ontology. To address these problems, a community of disease resources worked together to create the Mondo Disease Ontology as an open, community-driven ontology that integrates key medical and biomedical terminologies and is iteratively and regularly updated via manual curation and through synchronization with external sources using a Bayesian algorithm. Mondo supports disease data integration to improve diagnosis, treatment, and translational research. It records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge. Evidence before this study Many disease terminologies currently exist, but there is not a definitive standard for encoding diseases while addressing requirements for information exchange. Existing sources of disease definitions include the National Cancer Institute Thesaurus (NCIt), the Online Mendelian Inheritance in Man (OMIM), Orphanet, SNOMED CT, Disease Ontology (DO), ICD-10, MedGen, and numerous others. Each of these is designed for a particular purpose, and as such has different strengths. However, these standards only partially overlap and often conflict in the classification or mapping approach, making it difficult to align them with each other and/or with other knowledge sources. This need to integrate information has resulted in a proliferation of mappings between disease entries in different resources; these mappings lack completeness, accuracy, and precision, and are often inconsistent between resources. Added value of this study In order to computationally leverage the available knowledge sources for diagnostics and to reveal underlying mechanisms of diseases, we need to understand which terms are meaningfully equivalent across different resources. This will allow integration of associated information, such as treatments, genetics, phenotypes, etc. We therefore created the Mondo Disease Ontology to provide a logic-based structure for unifying multiple disease resources. Implications of all the available evidence Mondo can be leveraged by researchers and clinicians for disease annotations and data integration to aid in clinical diagnosis, treatment and advancement of human health care. Mondo is a freely available, open terminology that contains over 20,000 disease classes. Mondo is iteratively developed with contributions from the intended community and is under continuous revision, with future plans to further revise the top-level classes. Recently, efforts to classify rare diseases have centered on retrieving terms from various sources to provide a unified resource. Mondo can be explored using any of a variety of ontology browsers such as the Ontology Lookup Service (OLS) (ebi.ac.uk/ols/ontologies/mondo), and the ontology files and current releases are available on GitHub ([github.com/monarch-initiative/mondo][1]). ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Mondo is generously supported by the NIH National Human Genome Research Institute Phenomics First Resource, NIH-NHGRI # 1 RM1 HG010860-01, a Center of Excellence in Genomic Science; and an NIH Office of the Director Grant #5R24OD011883 for the Monarch Initiative. Additional support for this research/work was supported in part by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes 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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at <https://github.com/monarch-initiative/mondo>. <https://github.com/monarch-initiative/mondo> [1]: http://github.com/monarch-initiative/mondo

A case of a missense RBM10 variant in an adult with mild to moderate intellectual disability.

Rare diseases are increasingly recognised as a global public health priority and contribute to significant and disproportionately high health system impacts. Accordingly, they present clinical and public health challenges, as well as opportunities for digital health solutions across the lifespan, including improved diagnosis, treatment, navigation and care coordination, and integration and coordination for broader societal and patient wellbeing. People living with rare diseases, individually and cumulatively, are digital disruptors. In this manuscript the authors describe some of the unique dynamics of the rare disease domain as they currently, or have the potential to in the future, apply to digital health; highlight some recent international rare diseases digital health initiatives; and touch upon implications for those with more common disorders.

Objective The study is aimed at widening the clinical and genetic spectrum and at assessing genotype-phenotype associations in QARS encephalopathy. Methods Through diagnostic gene panel screening in an epilepsy cohort, and recruiting through GeneMatcher and our international network, we collected 10 patients with biallelic QARS variants. In addition, we collected data on 12 patients described in the literature to further delineate the associated phenotype in a total cohort of 22 patients. Computer modeling was used to assess changes on protein folding. Results Biallelic pathogenic variants in QARS cause a triad of progressive microcephaly, moderate to severe developmental delay, and early-onset epilepsy. Microcephaly was present at birth in 65%, and in all patients at follow-up. Moderate (14%) or severe (73%) developmental delay was characteristic, with no achievement of sitting (85%), walking (86%), or talking (90%). Additional features included irritability (91%), hypertonia/spasticity (75%), hypotonia (83%), stereotypic movements (75%), and short stature (56%). Seventy-nine percent had pharmacoresistant epilepsy with mainly neonatal onset. Characteristic cranial MRI findings include early-onset progressive atrophy of cerebral cortex (89%) and cerebellum (61%), enlargement of ventricles (95%), and age-dependent delayed myelination (88%). A small subset of patients displayed a less severe phenotype. Conclusions These data revealed first genotype-phenotype associations and may serve for improved interpretation of new QARS variants and well-founded genetic counseling.

Tricho-hepatic-enteric syndrome (THES) is a genetically heterogeneous rare syndrome (OMIM: 222470 (THES1) and 614602 (THES2)) that typically presents in the neonatal period with intractable diarrhoea, intra-uterine growth retardation (IUGR), facial dysmorphism, and hair and skin changes. THES is associated with pathogenic variants in either TTC37 or SKIV2L; both are components of the human SKI complex, an RNA exosome cofactor. We report an 8 year old girl who was diagnosed with THES by the Undiagnosed Disease Program-WA with compound heterozygous pathogenic variants in SKIV2L. While THES was considered in the differential diagnosis, the absence of protracted diarrhoea delayed definitive diagnosis. We therefore suggest that SKIV2L testing should be considered in cases otherwise suggestive of THES, but without the characteristic diarrhoea. We expand the phenotypic spectrum while reviewing the current knowledge on SKIV2L.

Autosomal recessive congenital ichthyosis is a genetically and phenotypically heterogenous group of scaling skin disorders. We describe a patient with ARCI caused by homozygous variants in NIPAL4, in whom the dermatologic phenotype and an associated arthropathy, markedly improved with ustekinumab.

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

Silver‐Russell syndrome (SRS OMIM 180860) is a rare, albeit well‐recognized disorder characterized by severe intrauterine and postnatal growth retardation. It remains a clinical diagnosis with a molecular cause identifiable in approximately 60%–70% of patients.We report a 4‐year‐old Australian Aboriginal girl who was born at 32 weeks gestation with features strongly suggestive of SRS, after extensive investigation she was referred to our undiagnosed disease program (UDP). Genomic sequencing was performed which identified a heterozygous splice site variant in IGF2 which is predicted to be pathogenic by in‐silico studies, paternal allelic origin, de novo status, and RNA studies on fibroblasts. We compare clinical findings with reported patients to add to the knowledge base on IGF2 variants and to promote the engagement of other Australian Aboriginal families in genomic medicine.