The challenge of diagnosing rare muscle diseases
Imagine visiting a doctor for unexplained difficulty of walking only to be told that, despite advanced genetic testing, the cause remains unknown. This is the reality for many patients with rare neuromuscular diseases (NMDs) – a group of conditions affecting the muscles and nerves. Scientists have linked more than 680 genes to these diseases, yet many patients still do not receive a clear diagnosis.
One challenge lies in the complexity of genetic data analysis. With the current approach of whole exome sequencing (known as WES) – which focuses on the protein-coding regions of the genome – scientists must sift through thousands of DNA variants (small changes in genetic code) to pinpoint the one responsible for the disease. This process is time-consuming, and manually analyzing such vast amounts of data increases the risk of human error. Furthermore, if a patient remains undiagnosed, their data must be revisited, which can be difficult to prioritize as new patients continuously enter the diagnostic pipeline. To address this, bioinformatic tools have been developed to streamline analysis and minimize errors.
How automated tools are changing the game
To make this process more efficient, researchers use bioinformatics tools like Exomiser, which helps prioritize genetic variants most likely responsible for a patient’s disease. Exomiser achieves this by integrating a patient’s phenotype (observable disease characteristics) with information from various databases, including animal models, protein interaction models, and medical literature. It also utilizes machine learning and AI-based variant consequence prediction tools to enhance accuracy.
The effectiveness of Exomiser in identifying disease-causing variants in myopathy patients was demonstrated in a recent study, where we successfully pinpointed the correct variant nearly 100% of the time, provided the researcher examined the top 10 most probable genetic variants listed by the tool.
Why this matters for patients
- More efficient analysis and re-analysis – Prioritization tools, such as Exomiser streamline genetic data analysis, making it easier to prioritize potential disease-causing variants. This allows for periodic re-evaluation of an unsolved patient’s variants, increasing the chances of identifying a disease-causing variant as scientific knowledge advances.
- Consistent approach – By applying a standardized method, prioritization tools reduce variability and human errors in genetic data interpretation.
- Potential for new insights – While primarily designed to rank known disease genes, prioritization tools may also help identify previously unrecognized genes causing NMDs.
The challenges of automated analysis
While tools like Exomiser are incredibly useful, they also come with limitations:
- Data quality matters – If genetic data is not properly cleaned, prioritization tools may highlight false-positive variants, which appear harmful due to DNA sequencing errors but are not biologically relevant.
- Not all diseases have a known genetic cause – Some conditions are caused by genes with no database supporting association to disease, meaning bioinformatic tools may not identify them as relevant.
- It’s a tool, not a replacement for experts – Geneticists and medical doctors must still carefully interpret the results to ensure accuracy and clinical relevance.
The future: more data, better diagnoses
Gene prioritization tools are evolving to consider advances in DNA sequencing, which has expanded from WES to whole genome sequencing (WGS), which includes non-coding regions that may regulate gene expression. This broader approach provides deeper insights into genetic variations beyond protein-coding genes.
Additionally, as genetic research progresses, new tools with enhanced predictive capabilities are emerging. By streamlining genetic analysis, reducing manual workload, and minimizing errors, these tools provide answers for patients who have been searching for years. While they are not without limitations, they represent a significant step toward faster, more accurate, and more accessible genetic diagnoses, offering hope to individuals with rare diseases.
Beyond mendelian genetics
The large number of unsolved myopathy cases and the frequent divergence between identified genetic defects and observed phenotypes suggest the need for more comprehensive tools capable of investigating non-monogenic conditions (i.e., those not caused by a single gene). Digenic (caused by two genes) or oligogenic (caused by several genes) inheritance patterns, as well as modifier genes, are often overlooked factors complicating the diagnostic process. These complexities highlight the urgent need for advanced diagnostic approaches that can accurately capture the multifactorial nature of myopathies, ultimately improving patient outcomes.
