Leveraging splice‐affecting variant predictors and a minigene validation system to identify Mendelian disease‐causing variants among exon‐captured variants of uncertain significance
Bibliographic record
Abstract
The genetic heterogeneity of Mendelian disorders results in a significant proportion of patients that are unable to be assigned a confident molecular diagnosis after conventional exon sequencing and variant interpretation. Here, we evaluated how many patients with an inherited retinal disease (IRD) have variants of uncertain significance (VUS) that are disrupting splicing in a known IRD gene by means other than affecting the canonical dinucleotide splice site. Three in silico splice-affecting variant predictors were leveraged to annotate and prioritize variants for splicing functional validation. An in vitro minigene system was used to assay each variant's effect on splicing. Starting with 745 IRD patients lacking a confident molecular diagnosis, we validated 23 VUS as splicing variants that likely explain disease in 26 patients. Using our results, we optimized in silico score cutoffs to guide future variant interpretation. Variants that alter base pairs other than the canonical GT-AG dinucleotide are often not considered for their potential effect on RNA splicing but in silico tools and a minigene system can be utilized for the prioritization and validation of such splice-disrupting variants. These variants can be overlooked causes of human disease but can be identified using conventional exon sequencing with proper interpretation guidelines.
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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 | 0.005 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".