Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology

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.

TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature

Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature.

Variants in mitochondrial disease genes are common causes of inherited peripheral neuropathies

BACKGROUND

Peripheral neuropathies in mitochondrial disease are caused by mutations in nuclear genes encoding mitochondrial proteins, or in the mitochondrial genome. Whole exome or genome sequencing enable parallel testing of nuclear and mtDNA genes, and it has significantly advanced the genetic diagnosis of inherited diseases. Despite this, approximately 40% of all Charcot-Marie-Tooth (CMT) cases remain undiagnosed.

METHODS

The genome-phenome analysis platform (GPAP) in RD-Connect was utilised to create a cohort of 2087 patients with at least one Human Phenotype Ontology (HPO) term suggestive of a peripheral neuropathy, from a total of 10,935 patients. These patients' genetic data were then analysed and searched for variants in known mitochondrial disease genes.

RESULTS

A total of 1,379 rare variants were identified, 44 of which were included in this study as either reported pathogenic or likely causative in 42 patients from 36 families. The most common genes found to be likely causative for an autosomal dominant neuropathy were GDAP1 and GARS1. We also detected heterozygous likely pathogenic variants in DNA2, MFN2, DNM2, PDHA1, SDHA, and UCHL1. Biallelic variants in SACS, SPG7, GDAP1, C12orf65, UCHL1, NDUFS6, ETFDH and DARS2 and variants in the mitochondrial DNA (mtDNA)-encoded MT-ATP6 and MT-TK were also causative for mitochondrial CMT. Only 50% of these variants were already reported as solved in GPAP.

CONCLUSION

Variants in mitochondrial disease genes are frequent in patients with inherited peripheral neuropathies. Due to the clinical overlap between mitochondrial disease and CMT, agnostic exome or genome sequencing have better diagnostic yields than targeted gene panels.

Rare Genetic Developmental Disabilities: Mabry Syndrome (MIM 239300) Index Cases and Glycophosphatidylinositol (GPI) Disorders

The case report by Mabry et al. (1970) of a family with four children with elevated tissue non-specific alkaline phosphatase, seizures and profound developmental disability, became the basis for phenotyping children with the features that became known as Mabry syndrome. Aside from improvements in the services available to patients and families, however, the diagnosis and treatment of this, and many other developmental disabilities, did not change significantly until the advent of massively parallel sequencing. As more patients with features of the Mabry syndrome were identified, exome and genome sequencing were used to identify the glycophosphatidylinositol (GPI) biosynthesis disorders (GPIBDs) as a group of congenital disorders of glycosylation (CDG). Biallelic variants of the phosphatidylinositol glycan (PIG) biosynthesis, type V (PIGV) gene identified in Mabry syndrome became evidence of the first in a phenotypic series that is numbered HPMRS1-6 in the order of discovery. HPMRS1 [MIM: 239300] is the phenotype resulting from inheritance of biallelic PIGV variants. Similarly, HPMRS2 (MIM 614749), HPMRS5 (MIM 616025) and HPMRS6 (MIM 616809) result from disruption of the PIGO, PIGW and PIGY genes expressed in the endoplasmic reticulum. By contrast, HPMRS3 (MIM 614207) and HPMRS4 (MIM 615716) result from disruption of post attachment to proteins PGAP2 (HPMRS3) and PGAP3 (HPMRS4). The GPI biosynthesis disorders (GPIBDs) are currently numbered GPIBD1-21. Working with Dr. Mabry, in 2020, we were able to use improved laboratory diagnostics to complete the molecular diagnosis of patients he had originally described in 1970. We identified biallelic variants of the PGAP2 gene in the first reported HPMRS patients. We discuss the longevity of the Mabry syndrome index patients in the context of the utility of pyridoxine treatment of seizures and evidence for putative glycolipid storage in patients with HPMRS3. From the perspective of the laboratory innovations made that enabled the identification of the HPMRS phenotype in Dr. Mabry's patients, the need for treatment innovations that will benefit patients and families affected by developmental disabilities is clear.

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

MOTIVATION

Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability.

RESULTS

We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information.

AVAILABILITY AND IMPLEMENTATION

EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.

Advancing the allergenicity assessment of new proteins using a text mining resource

With a society increasingly demanding alternative protein food sources, new strategies for evaluating protein safety issues, such as allergenic potential, are needed. Large-scale and systemic studies on allergenic proteins are hindered by the limited and non-harmonized clinical information available for these substances in dedicated databases. A missing key information is that representing the symptomatology of the allergens, especially given in terms of standard vocabularies, that would allow connecting with other biomedical resources to carry out different studies related to human health. In this work, we have generated the first resource with a comprehensive annotation of allergens' symptomatology, using a text-mining approach that extracts significant co-mentions between these entities from the scientific literature (PubMed, ∼36 million abstracts). The method identifies statistically significant co-mentions between the textual descriptions of the two types of entities in the literature as indication of relationship. 1,180 clinical signs extracted from the Human Phenotype Ontology, the Medical Subject Heading terms of PubMed together with other allergen-specific symptoms, were linked to 1,036 unique allergens annotated in two main allergen-related public databases via 14,009 relationships. This novel resource, publicly available through an interactive web interface, could serve as a starting point for future manually curated compilation of allergen symptomatology.

Mechanisms of cone sensitivity loss in retinitis pigmentosa

PURPOSE

To explore the mechanisms of cone sensitivity loss in retinitis pigmentosa by combining two-colour perimetry with threshold versus intensity (tvi) testing.

METHODS

Seven subjects with autosomal recessive retinitis pigmentosa and 10 normal subjects were recruited and underwent perimetric testing of one eye using 480- and 640-nm Goldman size V targets presented under scotopic conditions (no background illumination) and against a white background ranging in luminance from -1.5 to 2 log cd m-2 in 0.5 log cd m-2 steps. Data were fitted with tvi functions of the form logT = logT0 + log ((A + A0)/A0)n, where T is the threshold, T0 is the absolute threshold, A is the background intensity, A0 is the 'dark-light' constant and n is a gain constant.

RESULTS

Reliable tvi functions could not be obtained within the region of the visual field corresponding to loss of the ellipsoid zone on optical coherence tomography. At fixation, changes in both T0 and A0 were observed, consistent with a d1 mechanism loss, which resulted in an upwards and rightwards shift of the tvi function. Losses at [±3°, ±3°] demonstrated changes in T0, consistent with a d3 mechanism loss, resulting in an upwards translation of the tvi curve.

CONCLUSIONS

Although the absolute cone threshold was elevated at each location, shifts in the tvi function (so-called d1 mechanism loss) at fixation minimise threshold elevation in the presence of white adapting backgrounds, such as those typically employed in standard two-colour perimetry. At more peripheral testing locations, changes in threshold occurred independent of background luminance (so-called d3 mechanism loss). These findings suggest that backgrounds which selectively adapt rods while maintaining cones at, or near, absolute threshold may be preferable to conventional two-colour perimetry for assessing loss of cone sensitivity, especially at the point of fixation.

Novel Variants Linked to the Prodromal Stage of Parkinson's Disease (PD) Patients

BACKGROUND AND OBJECTIVE

The symptoms of most neurodegenerative diseases, including Parkinson's disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD.

MATERIALS AND METHODS

Data were obtained from the Parkinson's Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis.

RESULTS

Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD.

CONCLUSIONS

Genetic biomarkers are crucial for the early detection of Parkinson's disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. Their phenotypic impacts and evolutionary pattern over time, however, remain elusive in humans due to the technical and ethical complexities in functional studies. By combining human gene age dating and Mendelian disease phenotyping, our research reveals a gradual increase in disease gene proportions with gene age. Logistic regression modeling indicates that this increase could be related to longer protein lengths and higher burdens of deleterious de novo germline variants (DNVs) for older genes. We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (~0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Notably, young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes due to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potentials of phenotypic innovation for young genes than for older genes, a process subject to natural selection. Our study demonstrates that evolutionary new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Current genetic diagnostics in inborn errors of immunity

New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.

EURO-NMD registry: federated FAIR infrastructure, innovative technologies and concepts of a patient-centred registry for rare neuromuscular disorders

BACKGROUND

The EURO-NMD Registry collects data from all neuromuscular patients seen at EURO-NMD's expert centres. In-kind contributions from three patient organisations have ensured that the registry is patient-centred, meaningful, and impactful. The consenting process covers other uses, such as research, cohort finding and trial readiness.

RESULTS

The registry has three-layered datasets, with European Commission-mandated data elements (EU-CDEs), a set of cross-neuromuscular data elements (NMD-CDEs) and a dataset of disease-specific data elements that function modularly (DS-DEs). The registry captures clinical, neuromuscular imaging, neuromuscular histopathology, biological and genetic data and patient-reported outcomes in a computer-interpretable format using selected ontologies and classifications. The EURO-NMD registry is connected to the EURO-NMD Registry Hub through an interoperability layer. The Hub provides an entry point to other neuromuscular registries that follow the FAIR data stewardship principles and enable GDPR-compliant information exchange. Four national or disease-specific patient registries are interoperable with the EURO-NMD Registry, allowing for federated analysis across these different resources.

CONCLUSIONS

Collectively, the Registry Hub brings together data that are currently siloed and fragmented to improve healthcare and advance research for neuromuscular diseases.

Genetic architecture and biology of youth-onset type 2 diabetes

The prevalence of youth-onset type 2 diabetes (T2D) and childhood obesity has been rising steadily1, producing a growing public health concern1 that disproportionately affects minority groups2. The genetic basis of youth-onset T2D and its relationship to other forms of diabetes are unclear3. Here we report a detailed genetic characterization of youth-onset T2D by analysing exome sequences and common variant associations for 3,005 individuals with youth-onset T2D and 9,777 adult control participants matched for ancestry, including both males and females. We identify monogenic diabetes variants in 2.4% of individuals and three exome-wide significant (P < 2.6 × 10-6) gene-level associations (HNF1A, MC4R, ATXN2L). Furthermore, we report rare variant association enrichments within 25 gene sets related to obesity, monogenic diabetes and β-cell function. Many youth-onset T2D associations are shared with adult-onset T2D, but genetic risk factors of all frequencies-and rare variants in particular-are enriched within youth-onset T2D cases (5.0-fold increase in the rare variant and 3.4-fold increase in common variant genetic liability relative to adult-onset cases). The clinical presentation of participants with youth-onset T2D is influenced in part by the frequency of genetic risk factors within each individual. These findings portray youth-onset T2D as a heterogeneous disease situated on a spectrum between monogenic diabetes and adult-onset T2D.

Genetic Risk Factors for Early-Onset Merkel Cell Carcinoma

Importance

Merkel cell carcinoma (MCC) is a rare, aggressive neuroendocrine skin cancer. Of the patients who develop MCC annually, only 4% are younger than 50 years.

Objective

To identify genetic risk factors for early-onset MCC via genomic sequencing.

Design, Setting, and Participants

The study represents a multicenter collaboration between the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Institute of Allergy and Infectious Diseases (NIAID), and the University of Washington. Participants with early-onset and later-onset MCC were prospectively enrolled in an institutional review board-approved study at the University of Washington between January 2003 and May 2019. Unrelated controls were enrolled in the NIAID Centralized Sequencing Program (CSP) between September 2017 and September 2021. Analysis was performed from September 2021 and March 2023. Early-onset MCC was defined as disease occurrence in individuals younger than 50 years. Later-onset MCC was defined as disease occurrence at age 50 years or older. Unrelated controls were evaluated by the NIAID CSP for reasons other than familial cancer syndromes, including immunological, neurological, and psychiatric disorders.

Results

This case-control analysis included 1012 participants: 37 with early-onset MCC, 45 with later-onset MCC, and 930 unrelated controls. Among 37 patients with early-onset MCC, 7 (19%) had well-described variants in genes associated with cancer predisposition. Six patients had variants associated with hereditary cancer syndromes (ATM = 2, BRCA1 = 2, BRCA2 = 1, and TP53 = 1) and 1 patient had a variant associated with immunodeficiency and lymphoma (MAGT1). Compared with 930 unrelated controls, the early-onset MCC cohort was significantly enriched for cancer-predisposing pathogenic or likely pathogenic variants in these 5 genes (odds ratio, 30.35; 95% CI, 8.89-106.30; P < .001). No germline disease variants in these genes were identified in 45 patients with later-onset MCC. Additional variants in DNA repair genes were also identified among patients with MCC.

Conclusions and Relevance

Because variants in certain DNA repair and cancer predisposition genes are associated with early-onset MCC, genetic counseling and testing should be considered for patients presenting at younger than 50 years.

Improved phenotypic classification of male infertility to promote discovery of genetic causes

An increasing number of genes are being described in the context of non-syndromic male infertility. Linking the underlying genetic causes of non-syndromic male infertility with clinical data from patients is important to establish new genotype-phenotype correlations. This process can be facilitated by using universal nomenclature, but no standardized vocabulary is available in the field of non-syndromic male infertility. The International Male Infertility Genomics Consortium aimed at filling this gap, providing a standardized vocabulary containing nomenclature based on the Human Phenotype Ontology (HPO). The "HPO tree" was substantially revised compared with the previous version and is based on the clinical work-up of infertile men, including physical examination and hormonal assessment. Some causes of male infertility can already be suspected based on the patient's clinical history, whereas in other instances, a testicular biopsy is needed for diagnosis. We assembled 49 HPO terms that are linked in a logical hierarchy and showed examples of morphological features of spermatozoa and testicular histology of infertile men with identified genetic diagnoses to describe the phenotypes. This work will help to record patients' phenotypes systematically and facilitate communication between geneticists and andrologists. Collaboration across institutions will improve the identification of patients with the same phenotypes, which will promote the discovery of novel genetic causes for non-syndromic male infertility.

An evaluation of GPT models for phenotype concept recognition

OBJECTIVE

Clinical 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 METHODS

The 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.

RESULTS

The 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.

CONCLUSION

Our 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.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models-PhenoBCBERT and PhenoGPT-for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models to automate the detection of phenotype terms, including those not in the current HPO. We compare these models with PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also show strong performance in case studies on biomedical literature. We evaluate the strengths and weaknesses of BERT- and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Established and Evolving Roles of the Multifunctional Non-POU Domain-Containing Octamer-Binding Protein (NonO) and Splicing Factor Proline- and Glutamine-Rich (SFPQ)

It has been more than three decades since the discovery of multifunctional factors, the Non-POU-Domain-Containing Octamer-Binding Protein, NonO, and the Splicing Factor Proline- and Glutamine-Rich, SFPQ. Some of their functions, including their participation in transcriptional and posttranscriptional regulation as well as their contribution to paraspeckle subnuclear body organization, have been well documented. In this review, we focus on several other established roles of NonO and SFPQ, including their participation in the cell cycle, nonhomologous end-joining (NHEJ), homologous recombination (HR), telomere stability, childhood birth defects and cancer. In each of these contexts, the absence or malfunction of either or both NonO and SFPQ leads to either genome instability, tumor development or mental impairment.

Gastrulation-stage alcohol exposure induces similar rates of craniofacial malformations in male and female C57BL/6J mice

BACKGROUND

Prenatal alcohol exposure during gastrulation (embryonic day [E] 7 in mice, ~3rd week of human pregnancy) impairs eye, facial, and cortical development, recapitulating birth defects characteristic of Fetal Alcohol Syndrome (FAS). However, it is not known whether the prevalence or severity of craniofacial features associated with FAS is affected by biological sex.

METHODS

The current study administered either alcohol (2.9 g/kg, two i.p. doses, 4 hr apart) or vehicle to pregnant C57BL/6J females on E7, prior to gonadal sex differentiation, and assessed fetal morphology at E17.

RESULTS

Whereas sex did not affect fetal size in controls, alcohol-exposed females were smaller than both control females and alcohol-treated males. Alcohol exposure increased the incidence of eye defects to a similar degree in males and females. Together, these data suggest that females might be more sensitive to the general developmental effects of alcohol, but not effects specific to the craniofacies. Whole transcriptomic analysis of untreated E7 embryos found 214 differentially expressed genes in females vs. males, including those in pathways related to cilia and mitochondria, histone demethylase activity, and pluripotency.

CONCLUSION

Gastrulation-stage alcohol induces craniofacial malformations in male and female mouse fetuses at similar rates and severity, though growth deficits are more prevalent females. These findings support the investigation of biological sex as a contributing factor in prenatal alcohol studies.

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease

BACKGROUND

Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.

METHODS

MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology.

FINDINGS

MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases.

CONCLUSIONS

MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

FUNDING

NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.

Why do we keep ignoring sex in kidney disease?

Throughout the history of nephrology, little attention has been paid to the sex and gender differences in kidney disease. This lack of awareness prevents optimal diagnosis and management of kidney disease. In today's world of precision medicine, it is imperative to appreciate the differential factors regarding gender and kidney disease. This editorial summarizes the up-to-date literature regarding sex and gender differences in kidney disease and considers areas where knowledge is incomplete and where further research is needed. We address sex-specific effects on chronic kidney disease epidemiology; risks of dialysis underdosing and medication overdosing in women; unexplained loss of female sex advantage in life expectancy during dialysis, and impact of sex on diagnosis and management of genetic kidney disease. We also aim to highlight the impact of gender on kidney health and raise awareness of disparities that may be faced by women, and transgender and gender-diverse persons when a male-model approach is used by healthcare systems. By understanding the link between sex and kidney disease, kidney specialists can improve the care and outcomes of their patients. In addition, research on this topic can inform the development of targeted prevention and intervention strategies that address the specific needs and risk factors of different populations.

Neuromuscular disease genetics in under-represented populations: increasing data diversity

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are structural novelties pivotal in mammalian evolution. Their phenotypic impact on humans, however, remains elusive due to the technical and ethical complexities in functional studies. Through combining gene age dating with Mendelian disease phenotyping, our research reveals that new genes associated with disease phenotypes steadily integrate into the human genome at a rate of ~ 0.07% every million years over macroevolutionary timescales. Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures between young and old genes. Notably, young genes show significant enrichment in the male reproductive system, indicating strong sexual selection. Young genes also exhibit functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, bipedal locomotion, and color vision. Our findings further reveal increasing levels of pleiotropy over evolutionary time, which accompanies stronger selective constraints. We propose a "pleiotropy-barrier" model that delineates different potentials for phenotypic innovation between young and older genes subject to natural selection. Our study demonstrates that evolutionary new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT

To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models - PhenoBCBERT and PhenoGPT - for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes, due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models (LLMs) to automate the detection of phenotype terms, including those not in the current HPO. We compared these models to PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also showed strong performance in case studies on biomedical literature. We evaluated the strengths and weaknesses of BERT-based and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.

Elucidating the clinical and molecular spectrum of SMARCC2-associated NDD in a cohort of 65 affected individuals

PURPOSE

Coffin-Siris and Nicolaides-Baraitser syndromes are recognizable neurodevelopmental disorders caused by germline variants in BAF complex subunits. The SMARCC2 BAFopathy was recently reported. Herein, we present clinical and molecular data on a large cohort.

METHODS

Clinical symptoms for 41 novel and 24 previously published affected individuals were analyzed using the Human Phenotype Ontology. For genotype-phenotype correlations, molecular data were standardized and grouped into non-truncating and likely gene-disrupting (LGD) variants. Missense variant protein expression and BAF-subunit interactions were examined using 3D protein modeling, co-immunoprecipitation, and proximity-ligation assays.

RESULTS

Neurodevelopmental delay with intellectual disability, muscular hypotonia, and behavioral disorders were the major manifestations. Clinical hallmarks of BAFopathies were rare. Clinical presentation differed significantly, with LGD variants being predominantly inherited and associated with mildly reduced or normal cognitive development, whereas non-truncating variants were mostly de novo and presented with severe developmental delay. These distinct manifestations and non-truncating variant clustering in functional domains suggest different pathomechanisms. In vitro testing showed decreased protein expression for N-terminal missense variants similar to LGD.

CONCLUSION

This study improved SMARCC2 variant classification and identified discernible SMARCC2-associated phenotypes for LGD and non-truncating variants, which were distinct from other BAFopathies. The pathomechanism of most non-truncating variants has yet to be investigated.

De Novo Variants in RAB11B Cause Various Degrees of Global Developmental Delay and Intellectual Disability in Children

BACKGROUND

RAB11B was described previously once with a severe form of intellectual disability. We aim at validation and delineation of the role of RAB11B in neurodevelopmental disorders.

METHODS

We present seven novel individuals with disease-associated variants in RAB11B when compared with the six cases described in the literature. We performed a cross-sectional analysis to identify the clinical spectrum and the core phenotype. Additionally, structural effects of the variants were assessed by molecular modeling.

RESULTS

Seven distinct de novo missense variants were identified, three of them recurrent (p.(Gly21Arg), p.(Val22Met), and p.(Ala68Thr)). Molecular modeling suggests that those variants either affect the nucleotide binding (at amino acid positions 21, 22, 33, 68) or the interaction with effector molecules (at positions 72 and 75). Our data confirmed the main manifestations as neurodevelopmental disorder with intellectual disability (85%), muscular hypotonia (83%), structural brain anomalies (77%), and visual impairment (70%). Combined analysis indicates a genotype-phenotype correlation; variants impacting the nucleotide binding cause a severe phenotype with intellectual disability, and variants outside the binding pocket lead to a milder phenotype with epilepsy.

CONCLUSIONS

We confirm that disease-associated missense variants in RAB11B cause a neurodevelopmental disorder and suggest a genotype-phenotype correlation based on the impact on nucleotide binding functionality of RAB11B.

KidneyNetwork: using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It uses expression patterns and established gene-phenotype associations to predict which genes could be related to what (disease) phenotypes in an unbiased manner. We applied KidneyNetwork to rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis to prioritize candidate genes. KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. The intersection of prioritized genes with genes carrying rare variants in a patient with kidney and liver cysts identified ALG6 as plausible candidate gene. We strengthen this plausibility by identifying ALG6 variants in several cystic kidney and liver disease cases without alternative genetic explanation. We present KidneyNetwork, a publicly available kidney-specific co-expression network with optimized gene-phenotype predictions for kidney disease phenotypes. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research. TRANSLATIONAL STATEMENT: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.

Simulation of undiagnosed patients with novel genetic conditions

Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.

TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively. However, there are still two challenges in applying AIDD to guide TCM drug discovery: the lack of a large amount of standardized TCM-related information and AIDD is prone to pathological failures in out-of-domain data. We have released TCM Database@Taiwan in 2011, and it has been widely disseminated and used. Now, we developed TCMBank, the largest systematic free TCM database, which is an extension of TCM Database@Taiwan. TCMBank contains 9192 herbs, 61 966 ingredients (unduplicated), 15 179 targets, 32 529 diseases, and their pairwise relationships. By integrating multiple data sources, TCMBank provides 3D structure information of ingredients and provides a standard list and detailed information on herbs, ingredients, targets and diseases. TCMBank has an intelligent document identification module that continuously adds TCM-related information retrieved from the literature in PubChem. In addition, driven by TCMBank big data, we developed an ensemble learning-based drug discovery protocol for identifying potential leads and drug repurposing. We take colorectal cancer and Alzheimer's disease as examples to demonstrate how to accelerate drug discovery by artificial intelligence. Using TCMBank, researchers can view literature-driven relationship mapping between herbs/ingredients and genes/diseases, allowing the understanding of molecular action mechanisms for ingredients and identification of new potentially effective treatments. TCMBank is available at https://TCMBank.CN/.

European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation

Atopic dermatitis (AD) is a common inflammatory skin condition and prior genome-wide association studies (GWAS) have identified 71 associated loci. In the current study we conducted the largest AD GWAS to date (discovery N = 1,086,394, replication N = 3,604,027), combining previously reported cohorts with additional available data. We identified 81 loci (29 novel) in the European-only analysis (which all replicated in a separate European analysis) and 10 additional loci in the multi-ancestry analysis (3 novel). Eight variants from the multi-ancestry analysis replicated in at least one of the populations tested (European, Latino or African), while two may be specific to individuals of Japanese ancestry. AD loci showed enrichment for DNAse I hypersensitivity and eQTL associations in blood. At each locus we prioritised candidate genes by integrating multi-omic data. The implicated genes are predominantly in immune pathways of relevance to atopic inflammation and some offer drug repurposing opportunities.

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.

An Integrative Disease Information Network Approach to Similar Disease Detection

Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g., disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrative approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established first. Then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.

RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding

Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.

Usefulness of automated image analysis for recognition of the fragile X syndrome gestalt in Congolese subjects

BACKGROUND

Computer-aided software such as the facial image diagnostic aid (FIDA) and Face2Gene has been developed to perform pattern recognition of facial features with promising clinical results. The aim of this pilot study was to test Face2Gene's recognition performance on Bantu Congolese subjects with Fragile X syndrome (FXS) as compared to Congolese subjects with intellectual disability but without FXS (non-FXS).

METHOD

Frontal facial photograph from 156 participants (14 patients with FXS and 142 controls) predominantly young-adults to adults, median age 18.9 age range 4-39yo, were uploaded. Automated face analysis was conducted by using the technology used in proprietary software tools called Face2Gene CLINIC and Face2Gene RESEARCH (version 17.6.2). To estimate the statistical power of the Face2Gene technology in distinguishing affected individuals from controls, a cross validation scheme was used.

RESULTS

The similarity seen in the upper facial region (of males and females) is greater than the similarity seen in other parts of the face. Binary comparison of subjects with FXS versus non-FXS and subjects with FXS versus subjects with Down syndrome reveal an area under the curve values of 0.955 (p = 0.002) and 0.986 (p = 0.003).

CONCLUSION

The Face2Gene algorithm is separating well between FXS and Non-FXS subjects.

Improving the classification of cardinality phenotypes using collections

MOTIVATION

Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena.

RESULTS

We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.

CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs)

In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.

Advancing Understanding of Inequities in Rare Disease Genomics

PURPOSE

Advances in genomic research have facilitated rare disease diagnosis for thousands of individuals. Unfortunately, the benefits of advanced genetic diagnostic technology are not distributed equitably among the population, as has been seen in many other health care contexts. Quantifying and describing inequities in genetic diagnostic yield is inherently challenging due to barriers to both clinical and research genetic testing. We therefore present an implementation protocol developed to expand access to our rare disease genomic research study and to further understand existing inequities.

METHODS AND FINDINGS

The Rare Genomes Project (RGP) at the Broad Institute of MIT and Harvard offers research genome sequencing to individuals with rare disease who remain genetically undiagnosed through direct interaction with the individual or family. This presents an opportunity for diagnosis beyond the clinical context, thus eliminating many barriers to access. An initial goal of RGP was to equalize access to genomic sequencing by decoupling testing access from proximity to a major medical center and physician referral. However, study participants over the initial 3 years of this project were predominantly white and well resourced. To further understand and address the lack of diversity within RGP, we developed a novel protocol embedded within the larger RGP study, in an approach informed by an implementation science framework. The aims of this protocol were: (1) to diversify recruitment and enrollment within RGP; (2) understand the process and context of implementing genomic medicine for rare disease diagnosis; and (3) investigate the value of a diagnosis for underserved populations.

IMPLICATIONS

Improved understanding of existing inequities and potential strategies to address them are needed to advance equity in rare disease genetic diagnosis and research. In addition to the moral imperative of equity in genomic medicine, this approach is critical in order to fully understand the genomic underpinnings of rare disease.

Starvar: symptom-based tool for automatic ranking of variants using evidence from literature and genomes

BACKGROUND

Identifying variants associated with diseases is a challenging task in medical genetics research. Current studies that prioritize variants within individual genomes generally rely on known variants, evidence from literature and genomes, and patient symptoms and clinical signs. The functionalities of the existing tools, which rank variants based on given patient symptoms and clinical signs, are restricted to the coverage of ontologies such as the Human Phenotype Ontology (HPO). However, most clinicians do not limit themselves to HPO while describing patient symptoms/signs and their associated variants/genes. There is thus a need for an automated tool that can prioritize variants based on freely expressed patient symptoms and clinical signs.

RESULTS

STARVar is a Symptom-based Tool for Automatic Ranking of Variants using evidence from literature and genomes. STARVar uses patient symptoms and clinical signs, either linked to HPO or expressed in free text format. It returns a ranked list of variants based on a combined score from two classifiers utilizing evidence from genomics and literature. STARVar improves over related tools on a set of synthetic patients. In addition, we demonstrated its distinct contribution to the domain on another synthetic dataset covering publicly available clinical genotype-phenotype associations by using symptoms and clinical signs expressed in free text format.

CONCLUSIONS

STARVar stands as a unique and efficient tool that has the advantage of ranking variants with flexibly expressed patient symptoms in free-form text. Therefore, STARVar can be easily integrated into bioinformatics workflows designed to analyze disease-associated genomes.

AVAILABILITY

STARVar is freely available from https://github.com/bio-ontology-research-group/STARVar .

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice.

A genotype-to-phenotype approach suggests under-reporting of single nucleotide variants in nephrocystin-1 (NPHP1) related disease (UK 100,000 Genomes Project)

Autosomal recessive whole gene deletions of nephrocystin-1 (NPHP1) result in abnormal structure and function of the primary cilia. These deletions can result in a tubulointerstitial kidney disease known as nephronophthisis and retinal (Senior-Løken syndrome) and neurological (Joubert syndrome) diseases. Nephronophthisis is a common cause of end-stage kidney disease (ESKD) in children and up to 1% of adult onset ESKD. Single nucleotide variants (SNVs) and small insertions and deletions (Indels) have been less well characterised. We used a gene pathogenicity scoring system (GenePy) and a genotype-to-phenotype approach on individuals recruited to the UK Genomics England (GEL) 100,000 Genomes Project (100kGP) (n = 78,050). This approach identified all participants with NPHP1-related diseases reported by NHS Genomics Medical Centres and an additional eight participants. Extreme NPHP1 gene scores, often underpinned by clear recessive inheritance, were observed in patients from diverse recruitment categories, including cancer, suggesting the possibility of a more widespread disease than previously appreciated. In total, ten participants had homozygous CNV deletions with eight homozygous or compound heterozygous with SNVs. Our data also reveals strong in-silico evidence that approximately 44% of NPHP1 related disease may be due to SNVs with AlphaFold structural modelling evidence for a significant impact on protein structure. This study suggests historical under-reporting of SNVS in NPHP1 related diseases compared with CNVs.

The AnnotSV webserver in 2023: updated visualization and ranking

Much of the human genetics variant repertoire is composed of single nucleotide variants (SNV) and small insertion/deletions (indel) but structural variants (SV) remain a major part of our modified DNA. SV detection has often been a complex question to answer either because of the necessity to use different technologies (array CGH, SNP array, Karyotype, Optical Genome Mapping…) to detect each category of SV or to get an appropriate resolution (Whole Genome Sequencing). Thanks to the deluge of pangenomic analysis, Human geneticists are accumulating SV and their interpretation remains time consuming and challenging. The AnnotSV webserver (https://www.lbgi.fr/AnnotSV/) aims at being an efficient tool to (i) annotate and interpret SV potential pathogenicity in the context of human diseases, (ii) recognize potential false positive variants from all the SV identified and (iii) visualize the patient variants repertoire. The most recent developments in the AnnotSV webserver are: (i) updated annotations sources and ranking, (ii) three novel output formats to allow diverse utilization (analysis, pipelines), as well as (iii) two novel user interfaces including an interactive circos view.

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

Insights from rare variants into the genetic architecture and biology of youth-onset type 2 diabetes

Youth-onset type 2 diabetes (T2D) is a growing public health concern. Its genetic basis and relationship to other forms of diabetes are largely unknown. To gain insight into the genetic architecture and biology of youth-onset T2D, we analyzed exome sequences of 3,005 youth-onset T2D cases and 9,777 ancestry matched adult controls. We identified (a) monogenic diabetes variants in 2.1% of individuals; (b) two exome-wide significant (P < 4.3×10-7) common coding variant associations (in WFS1 and SLC30A8); (c) three exome-wide significant (P < 2.5×10-6) rare variant gene-level associations (HNF1A, MC4R, ATX2NL); and (d) rare variant association enrichments within 25 gene sets broadly related to obesity, monogenic diabetes, and β-cell function. Many association signals were shared between youth-onset and adult-onset T2D but had larger effects for youth-onset T2D risk (1.18-fold increase for common variants and 2.86-fold increase for rare variants). Both common and rare variant associations contributed more to youth-onset T2D liability variance than they did to adult-onset T2D, but the relative increase was larger for rare variant associations (5.0-fold) than for common variant associations (3.4-fold). Youth-onset T2D cases showed phenotypic differences depending on whether their genetic risk was driven by common variants (primarily related to insulin resistance) or rare variants (primarily related to β-cell dysfunction). These data paint a picture of youth-onset T2D as a disease genetically similar to both monogenic diabetes and adult-onset T2D, in which genetic heterogeneity might be used to sub-classify patients for different treatment strategies.

Chromosomal microarray analysis supplements exome sequencing to diagnose children with suspected inborn errors of immunity

Purpose

Though copy number variants (CNVs) have been suggested to play a significant role in inborn errors of immunity (IEI), the precise nature of this role remains largely unexplored. We sought to determine the diagnostic contribution of CNVs using genome-wide chromosomal microarray analysis (CMA) in children with IEI.

Methods

We performed exome sequencing (ES) and CMA for 332 unrelated pediatric probands referred for evaluation of IEI. The analysis included primary, secondary, and incidental findings.

Results

Of the 332 probands, 134 (40.4%) received molecular diagnoses. Of these, 116/134 (86.6%) were diagnosed by ES alone. An additional 15/134 (11.2%) were diagnosed by CMA alone, including two likely de novo changes. Three (2.2%) participants had diagnostic molecular findings from both ES and CMA, including two compound heterozygotes and one participant with two distinct diagnoses. Half of the participants with CMA contribution to diagnosis had CNVs in at least one non-immune gene, highlighting the clinical complexity of these cases. Overall, CMA contributed to 18/134 diagnoses (13.4%), increasing the overall diagnostic yield by 15.5% beyond ES alone.

Conclusion

Pairing ES and CMA can provide a comprehensive evaluation to clarify the complex factors that contribute to both immune and non-immune phenotypes. Such a combined approach to genetic testing helps untangle complex phenotypes, not only by clarifying the differential diagnosis, but in some cases by identifying multiple diagnoses contributing to the overall clinical presentation.

Population-specific facial traits and diagnosis accuracy of genetic and rare diseases in an admixed Colombian population

Up to 40% of rare disorders (RD) present facial dysmorphologies, and visual assessment is commonly used for clinical diagnosis. Quantitative approaches are more objective, but mostly rely on European descent populations, disregarding diverse population ancestry. Here, we assessed the facial phenotypes of Down (DS), Morquio (MS), Noonan (NS) and Neurofibromatosis type 1 (NF1) syndromes in a Latino-American population, recording the coordinates of 18 landmarks in 2D images from 79 controls and 51 patients. We quantified facial differences using Euclidean Distance Matrix Analysis, and assessed the diagnostic accuracy of Face2Gene, an automatic deep-learning algorithm. Individuals diagnosed with DS and MS presented severe phenotypes, with 58.2% and 65.4% of significantly different facial traits. The phenotype was milder in NS (47.7%) and non-significant in NF1 (11.4%). Each syndrome presented a characteristic dysmorphology pattern, supporting the diagnostic potential of facial biomarkers. However, population-specific traits were detected in the Colombian population. Diagnostic accuracy was 100% in DS, moderate in NS (66.7%) but lower in comparison to a European population (100%), and below 10% in MS and NF1. Moreover, admixed individuals showed lower facial gestalt similarities. Our results underscore that incorporating populations with Amerindian, African and European ancestry is crucial to improve diagnostic methods of rare disorders.

High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population

Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.