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Clinical validation of RNA sequencing for Mendelian disorder diagnostics:

                                


1. Introduction

Despite significant progress in clinical sequencing, a considerable proportion of diagnostic cases remain unresolved due to the limitations of DNA-based testing alone. RNA sequencing (RNA-seq) has emerged as a promising tool to enhance genetic diagnostics by providing crucial functional insights into gene expression and splicing. While RNA-seq has been widely used in research, its clinical implementation remains a challenge. This study focuses on the development and validation of a clinical diagnostic RNA-seq test aimed at improving diagnostic outcomes for individuals with suspected genetic disorders, particularly in cases where comprehensive DNA testing has failed to provide conclusive results.

2. The Role of RNA Sequencing in Clinical Diagnostics

RNA sequencing offers a complementary approach to DNA-based testing by analyzing gene expression and alternative splicing, which are critical for understanding disease mechanisms. Unlike DNA sequencing, which detects genomic variants, RNA-seq provides dynamic insights into gene activity, allowing the identification of aberrant expression and splicing defects. This capability is particularly useful for diagnosing conditions caused by regulatory mutations, splice-site alterations, or deep intronic variants. The integration of RNA-seq into clinical workflows has the potential to significantly enhance diagnostic yield by uncovering disease-associated molecular signatures that would otherwise remain undetected.

3. Development of a Clinical Diagnostic RNA-Seq Test

To bridge the gap between research and clinical application, a robust diagnostic RNA-seq test was developed and validated for individuals undergoing genetic evaluation. The test processes RNA samples derived from fibroblasts or blood, utilizing advanced bioinformatics pipelines to detect expression outliers and splicing abnormalities. By establishing gene expression and splicing reference ranges, the test can systematically identify deviations associated with pathogenic variants. This development represents a significant step toward translating RNA-seq from an experimental tool into a standardized clinical diagnostic assay.

4. Validation Process and Benchmarking Strategies

The clinical validation of the RNA-seq test involved analyzing 130 samples, including 90 negative and 40 positive cases, with known diagnostic findings. To ensure reliability, benchmarking was performed using data from the GM24385 lymphoblastoid sample produced by the Genome in a Bottle Consortium. Short-read and long-read sequencing techniques were employed to define provisional expression and splicing standards. Reference ranges for each gene and splicing junction were established using a control dataset, allowing the identification of disease-associated outliers with high specificity and sensitivity. This rigorous validation process underscores the potential of RNA-seq as a reliable diagnostic tool.

5. Performance Evaluation and Clinical Implications

To assess the effectiveness of the RNA-seq test, its diagnostic pipeline was applied to 40 positive samples from the Undiagnosed Diseases Network (UDN), all of which had prior confirmed genetic diagnoses. The test successfully detected known pathogenic expression and splicing outliers, demonstrating its clinical utility. By integrating RNA-seq into routine diagnostic workflows, clinicians can improve the detection of previously elusive genetic disorders, refine variant interpretation, and enhance patient care. Additionally, RNA-seq provides valuable insights for functional validation of variants of uncertain significance (VUS), facilitating more accurate genetic counseling and disease management.

6. Future Perspectives and Broader Adoption of Clinical RNA-Seq

The successful validation of this diagnostic RNA-seq test paves the way for broader clinical adoption, but challenges remain in standardizing protocols and ensuring widespread accessibility. Future research should focus on expanding reference datasets to cover diverse populations, integrating RNA-seq with emerging multi-omics approaches, and improving computational methods for automated interpretation. The incorporation of long-read sequencing and machine learning-driven analysis will further enhance the precision and scalability of RNA-based diagnostics.

 

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