Structural Variant Detection and Clinical Interpretation in 6224 Unsolved Rare Disease Exomes

Research Background

The diagnosis and study of rare diseases have been significant challenges in genetics and clinical medicine. Structural variants (SVs), including large deletions, duplications, inversions, translocations, and more complex events, can disrupt gene function and lead to rare diseases. However, current exome sequencing (ES) analysis pipelines for handling small intragenic variants such as single nucleotide variants (SNVs) and insertions-deletions (indels) shorter than 50 base pairs are not yet mature for SVs. This study aims to improve diagnostic rates, especially by detecting and interpreting SVs in ES data to resolve undiagnosed rare disease cases.

Research Source

This study was jointly completed by German Demidov, Steven Laurie, and others, involving experts and scholars from different research institutions. The article was published in the European Journal of Human Genetics in 2024, with DOI: 10.1038/s41431-024-01637-4.

Research Methods and Results

This study includes the following steps: 1. Detailed sample recruitment as described by the Solve-RD consortium. The study sample included 6224 patients and 3090 unaffected relatives, totaling 9351 exome samples. 2. Data analysis using the Manta SV detection software developed for exome data to detect SVs. 3. Estimation of allele frequencies (AF) of structural variants in the population and AF-based filtering to remove common events and artifacts. 4. Quality filtering, functional annotation, and clinical interpretation of results, submitted to corresponding clinical personnel for interpretation. 5. Visualization and quality assessment of suspicious variants using the IGV genome browser. 6. Short variant analysis and phenotype data collection.

The main results are as follows: - Detection, quality filtering, and annotation of SVs in 9351 ES datasets. - After automated filtering of raw SV callsets, 1404 SVs remained. - After evaluation by experts from corresponding European research networks, 32 patients (0.51%) were considered to be caused by 23 unique SVs. - 8 SVs (0.13% of patients) were not reported by read depth-based CNV detection methods, indicating the additional diagnostic value of SV detection.

Conclusions and Significance

Although the overall improvement in diagnostic yield was limited, successful diagnosis is crucial for families with rare diseases and may end their years-long diagnostic odyssey. This study emphasizes the value of incorporating SV detection in exome reanalysis, especially for cases that remain undiagnosed in routine testing.

Research Highlights

• The study provides the clinical application value of SV in ES reanalysis.
• This research can provide extremely valuable diagnostic information for unsolved cases.
• The study involves a large-scale sample of rare disease patients, providing a reference for future genetic rare disease patient diagnosis.

Other Important Content

The study also mentioned the need for further refinement and automation of relevant software tools to reduce manual assessment workload, such as the implementation of partially automated phenotype matching programs for disease-causing variants. All raw and processed data files are available in EGA (European Genome-phenome Archive).

This study significantly promoted the application and development of SV detection technology in clinical diagnosis and brought hope to those rare disease patients who have long been undiagnosed. Although some SV detection methods in this study have certain limitations, their potential value in rare disease diagnosis should not be overlooked.