A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and <i>in silico</i> methods
Bibliographic record
Abstract
Abstract Background/Objectives Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to ACMG criterion PVS1). However, the exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes is poorly understood. Methods A total of 184 rare CSSVs in unselected blood-expressed genes were identified by genome sequencing in 121 individuals, and their impact on splicing was interrogated manually in RNA sequencing (RNA-seq) data. Blind to these RNA-seq data, we attempted to predict the precise impact of CSSVs by applying in silico tools and the ClinGen Sequence Variant Interpretation Working Group 2018 guidelines for applying PVS1 criterion. Results There was no evidence of a frameshift nor of reduced expression consistent with nonsense-mediated decay (NMD) for 24% of CSSVs: 17% had wildtype splicing only and normal junction depths, 3.25% resulted in cryptic splice site usage and in-frame indels, 3.25% resulted in full exon skipping (in-frame), and 0.5% resulted in full intron inclusion (in-frame). Misclassification rates for splicing outcome (frameshift/NMD vs. no frameshift/no NMD) using (i) SpliceAI, (ii) MaxEntScan, and (iii) AutoPVS1 ranged from 30-41%, with none outperforming a simple “zero rule” classifier. Conclusion Nearly 1 in 4 CSSVs may not cause LoF based on analysis of RNA-seq data. Predictions from in silico methods were often discordant with findings from RNA-seq. More caution may be warranted in applying PVS1-level evidence to CSSVs in the absence of functional data.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".