Rare disorders have many faces: in silico characterization of rare disorder spectrum

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems

It is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.

Genetic basis of osteogenesis imperfecta from a single tertiary centre in South Africa

Osteogenesis imperfecta (OI) is a clinically heterogeneous disorder characterised by skeletal fragility and an increased fracture incidence. It occurs in approximately one in every 15-20,000 births and is known to vary considerably in its severity. This report aimed to use next-generation sequencing (NGS) technology to identify disease genes and causal variants in South African patients with clinical-radiological features of OI. A total of 50 affected individuals were recruited at Tygerberg Hospital's Medical Genetics clinic. Patients were selected for a gene panel test (n = 39), a single variant test (n = 1) or exome sequencing (ES) (n = 12, 7 singletons, 1 affected duo, and 1 trio), depending on funding eligibility. An in-house genomic bioinformatics pipeline was developed for the ES samples using open-source software and tools. This study's 100% diagnostic yield was largely attributable to an accurate clinical diagnosis. A causal variant in COL1A1 or COL1A2 was identified in 94% of this patient cohort, which is in line with previous studies. Interestingly, this study was the first to identify the common South African pathogenic FKBP10 variant in a patient of mixed ancestry, adding to what was previously known about this variant in our population. Additionally, a recurrent variant in COL1A2: c.1892G>T was discovered in 27 individuals (25 from three large unrelated families and two further individuals), facilitating the establishment of local testing for this variant in South African patients.

Rule of Prevention: a potential framework to evaluate preventive interventions for rare diseases

Background: The benefits of preventive interventions lack comprehensive evaluation in standard health technology assessments (HTA), particularly for rare and transmissible diseases. Objective: To identify possible considerations for future HTA using analogies between the treatment and prevention of rare diseases. Study design: An Expert panel meeting assessed whether one HTA assessment framework can be applied to assess both rare disease treatments and preventive interventions. Experts also evaluated the range of value elements currently included in HTAs and their applicability to rare, transmissible, and/or preventable diseases. Results: A broad range of value should be considered when assessing rare, transmissible disease prevention. Although standard HTA can be applied to transmissible diseases, the risk of local outbreaks and the need for large-scale prevention programs suggest a modified assessment framework, capable of incorporating prevention-specific value elements in HTAs. A 'Rule of Prevention' framework was proposed to allow broader value considerations anchored to severity, equity, and prevention benefits in decision-making for preventive interventions for rare transmissible diseases. Conclusion: The proposed prevention framework introduces an explicit initial approach to consistently assess rare transmissible diseases, and to incorporate the broader value of preventive interventions compared with treatment.

The Power of Clinical Diagnosis for Deciphering Complex Genetic Mechanisms in Rare Diseases

Complex genetic disease mechanisms, such as structural or non-coding variants, currently pose a substantial difficulty in frontline diagnostic tests. They thus may account for most unsolved rare disease patients regardless of the clinical phenotype. However, the clinical diagnosis can narrow the genetic focus to just a couple of genes for patients with well-established syndromes defined by prominent physical and/or unique biochemical phenotypes, allowing deeper analyses to consider complex genetic origin. Then, clinical-diagnosis-driven genome sequencing strategies may expedite the development of testing and analytical methods to account for complex disease mechanisms as well as to advance functional assays for the confirmation of complex variants, clinical management, and the development of new therapies.