Case Study: How RDDC Aided Pathogenicity Analysis of a Novel NIPBL Splicing Variant
In rare disease diagnostics, intronic variants in non-coding regions remain a significant hurdle for elucidating pathogenic mechanisms. For complex disorders like Cornelia de Lange syndrome (CdLS), which is caused by anomalies in cohesin pathway genes, researchers urgently need reliable tools to assess function when Whole Exome Sequencing (WES) reveals an intronic Variant of Uncertain Significance (VUS). This case study demonstrates how the RDDC bioinformatics tool provided critical pathogenic evidence for a novel intronic variant in the NIPBL gene through precise splicing prediction, ultimately leading to a definitive diagnosis.
The Clinical Challenge: A Novel Intronic Variant
The challenge involved a 4-month-old male infant presenting with typical CdLS facial features, severe growth retardation, and multi-organ abnormalities. A WES analysis was performed, identifying a de novo heterozygous variant in intron 16 of the NIPBL gene that was absent from gnomAD and other major databases: c.3855+2_3855+6del.
This 5bp deletion was located at the canonical splice donor site. While strongly suggestive of a splicing defect, direct functional evidence was lacking. At this juncture, an accurate in silico prediction was essential to guide the diagnosis.
RDDC's Precise Prediction: Unmasking Splice Site Disruption
To evaluate the variant's pathogenicity, the research team employed bioinformatics tools, including RDDC and SpliceAI. The analysis from RDDC clearly indicated that the c.3855+2_3855+6del variant would likely destroy the function of the splice donor site.
This prediction meant the variant would cause severe mRNA mis-splicing during transcription, such as the skipping of an entire exon or the erroneous retention of intronic sequence. Either outcome would result in an aberrant mRNA, which would then be translated into a non-functional NIPBL protein, thereby disrupting the function of the entire cohesin complex.
Providing Key Evidence for Clinical Diagnosis
This critical predictive evidence from RDDC, combined with the patient's high clinical score of 12 (meeting the diagnostic criteria for classic CdLS) and the variant's de novo status, formed a complete chain of evidence. The research team was thus able to confidently classify this novel intronic variant as pathogenic, confirming the patient's CdLS diagnosis.
This case once again proves the significant value of the RDDC tool in parsing the functional impact of novel, non-coding variants. It empowers researchers to rapidly assess the potential damage of intronic variants on splicing, providing key pathogenic insights for VUS hits from WES and serving as a powerful ally in genetic research and molecular diagnostics.
Content Source and Disclaimer
This article is a compilation and interpretation of the scientific study cited below, intended to highlight the application of RDDC bioinformatics tools. All research data and conclusions belong to the original authors and publication.
Original Article:
[Literature Interpretation] Genotype and phenotype analysis of a case of Cornelia de Lange syndrome. (Source: Preprint)
