Solving the unsolved: Efficient reinterpretation of rare disease cases using Exomiser
Recent Monarch Initiative research led by Prof. Damian Smedley and Dr. Letizia Vestito at Queen Mary University of London has demonstrated how Exomiser has the potential to revolutionize the reinterpretation of genetic data for rare disease diagnoses. Published in Nature Partner Journals Genomic Medicine, the study highlights Exomiser’s role in uncovering previously missed diagnoses, offering new hope to patients and their families.
The Challenge: the reinterpretation of the genomes of undiagnosed patients is essential but time-consuming
The advent of cost-effective, high-throughput sequencing has enabled the integration of whole-exome and whole-genome sequencing (WES/WGS) in clinical practice and the advent of large sequencing projects for rare Mendelian diseases. Thanks to significant progress, WES/WGS analyses have helped diagnose many rare disease patients, though 50–80% of rare disease patients remain undiagnosed after initial WES/WGS analyses. A key reason for this is that the causative variant may lie in a gene not yet linked to the patient’s condition at the time of testing.
With hundreds of new disease-gene associations discovered every year, the periodic reinterpretation of genetic data for undiagnosed individuals has become essential. This practice not only improves the diagnostic yield but also significantly shortens the diagnostic odyssey for patients, bridging the gap between rapidly evolving scientific knowledge and clinical practice. However, the reinterpretation process is time-consuming and often beyond the capacity of busy genetic testing hubs.
Hundreds of patients diagnosed after using Exomiser for reanalysis
Exomiser, a phenotype-driven variant prioritisation tool, integrates genomic data with patient phenotypes to annotate, filter and prioritise likely causative variants. To assess the use of Exomiser for reinterpretation, incorporating the latest knowledge of diseases, genes and phenotypes, a large-scale reanalysis of 24,015 unsolved cases from the 100,000 Genomes Project was performed. A conservative candidate selection procedure was used to identify the 725 most likely diagnoses. These were independently reviewed by the Genomics England clinical genetics team, composed of clinical scientists and geneticists, resulting in new diagnoses for 463 patients.
Prof Smedley said: “This study has shown we can now perform scalable, periodic reanalysis of undiagnosed cases, ensuring timely incorporation of emerging evidence into clinical practice.”
Optimization of Exomiser reanalysis
Exomiser was very successful at finding new variant candidates that could be responsible for the undiagnosed patients’ diseases. However, extensive manual interpretation of these candidates was required to identify the most likely diagnoses. This emphasized the need for an optimized reanalysis strategy to highlight candidates only when new disease-gene discoveries or pathogenic/likely pathogenic variant reclassifications arise.
Researchers identified an optimal combination of Exomiser parameters for candidate selection (Exomiser variant score ≥ 0.8 and increase in human phenotype score ≥ 0.2 between Exomiser runs), which, combined with the latest Exomiser’s automated ACMG/AMP classification pipeline, led to a recall rate (proportion of total diagnoses that were in Exomiser candidates) of 82.4% and precision rate of 87.5% (proportion of total Exomiser candidates that were diagnoses). This optimized strategy drastically reduced the number of disease-causing candidate variants to review per case from a median of 30 variants (range 11–214) to only one or two variants per case, enabling efficient case reinterpretation in minutes rather than hours.
Dr Vestito said: “We have demonstrated Exomiser’s ability to identify and re-classify novel candidates for diagnosis over time in light of novel emerging evidence of gene-disease associations, and demonstrated Exomiser’s efficacy in increasing the diagnostic yield of undiagnosed rare disease patients.”
Learn more in the Open Access publication
Vestito, L. et al. (2024). Efficient reinterpretation of rare disease cases using Exomiser. NPJ genomic medicine, 9(1), 65. https://doi.org/10.1038/s41525-024-00456-2
