P509: Resolution of variants of uncertain significance by RNA sequencing
Bibliographic record
Abstract
Introduction: The clinical implementation of next-generation sequencing has revolutionized genetic diagnostics of rare disease, yet many exome and genome analyses remain inconclusive.This is due in part to challenges with variant prioritization and interpretation, particularly for those variants within intronic regions.The accuracy of in silico tools to predict the effect of a variant on RNA expression and/or splicing outside the canonical splice site remains low and therefore these variants are almost always variants of uncertain significance (VUSs) on clinical reporting, if reported at all.Methods: Following inconclusive genetic testing, families affected with undiagnosed rare disease consented to enrollment in the Care4Rare Canada research program.RNA sequencing (RNA-Seq) analyses were performed using appropriate tissue types from affected individuals.Control samples were obtained internally as well as from The Genotype-Tissue Expression (GTEx) project.Results: RNA-Seq has been useful for resolution of VUSs in a number of projects we have analyzed.For example, in one affected individual, research re-analysis of clinical trio exome sequencing (ES) data revealed compound heterozygous intronic VUSs in TRAPPC12, which were not included in the clinical report and was in keeping with her presentation.RNA-Seq studies revealed both variants resulted in exon skipping, events which were either absent or seen rarely in the control dataset, and was able to reclassify both of these intronic variants as likely pathogenic (LP).In a second study, an affected individual with suspected short-rib thoracic dysplasia 3 with or without polydactyly (SRTD3) had clinical genetic testing which identified a likely pathogenic frameshift variant and an intronic VUS in DYNC2H1 in trans.Research RNA-Seq investigations revealed significantly decreased DYNC2H1 gene expression in the proband as well as a novel splice junction as a result of the intronic variant, which again allowed reclassification of the variant to LP.Finally, in two siblings with suspected Joubert syndrome, RNA-Seq was able to identify their second causative variant.This variant was intractable to detection by routine exome and genome sequencing analyses as it is a 57 bp deletion in a repetitive intronic region, but was detectable by RNA-Seq as it leads to novel intron inclusion in the resulting transcript.Conclusion: Our studies highlight the benefits of RNA-Seq in obtaining a diagnosis for patients with rare disease where additional data is required after inconclusive clinical testing, especially for variants with a potential mRNA splice impact.While we recognize that non-coding variants play a role in disease, we currently lack robust tools to accurately interpret and classify them and most are either reported as VUSs or not reported (depending on laboratory policy and testing scenario).RNA-Seq can assess the impact of these non-coding variants via their effect on gene expression, RNA stability and RNA processing and therefore could be considered as a follow up clinical test in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".