Abstract
DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/.
Highlights
Rare diseases are rare conditions that occur only in a precious few people
To promote effective diagnosis of rare diseases in a clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases using the above four diagnostic models
The results showed that the PICS model achieved the best performance among the four models, with only one rare disease as the outcome of the prediction (Figure 2A), but in real application, the diagnosis result is barely satisfactory
Summary
There is no unified, widely accepted definition for rare diseases (Jia and Shi, 2017). Knowledge sharing and coordinated orphan drug development across national borders, the World Health Organization (WHO) defines rare diseases as a prevalence >6.5–10 in 10,000 (Franco, 2013), which we adopted as the definition of rare diseases in this article. About 80% of rare diseases are the consequence of genetic defects, but >5% of rare diseases can be effectively interfered with or treated. Nowadays, screening and diagnostic rates of rare diseases are constantly improved with the progress of molecular biology and cytogenetics (Ekins, 2017). Whole-exome sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, opening up new ways for disease diagnostics, especially for OMIM
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