Splice Site Variants in the <i>KCNQ1</i> and <i>SCN5A</i> Genes: Transcript Analysis as a Tool in Supporting Pathogenicity
Bibliographic record
Abstract
Background: Approximately 75% of clinically definite long QT syndrome (LQTS) cases are caused by mutations in the KCNQ1 , KCNH2 and SCN5A genes. Of these mutations, a small proportion (3.2-9.2%) are predicted to affect splicing. These mutations present a particular challenge in ascribing pathogenicity. Methods: Here we report an analysis of the transcriptional consequences of two mutations, one in the KCNQ1 gene (c.781_782delinsTC) and one in the SCN5A gene (c.2437-5C>A), which are predicted to affect splicing. We isolated RNA from lymphocytes and used a directed PCR amplification strategy of cDNA to show mis-spliced transcripts in mutation-positive patients. Results: The loss of an exon in each mis-spliced transcript had no deduced effect on the translational reading frame. The clinical phenotype corresponded closely with genotypic status in family members carrying the KCNQ1 splice variant, but not in family members with the SCN5A splice variant. These results are put in the context of a literature review, where only 20% of all splice variants reported in the KCNQ1 , KCNH2 and SCN5A gene entries in the HGMDPro 2015.4 database have been evaluated using transcriptional assays. Conclusions: Prediction programmes play a strong role in most diagnostic laboratories in classifying variants located at splice sites; however, transcriptional analysis should be considered critical to confirm mis-splicing. Critically, this study shows that genuine mis- splicing may not always imply clinical significance, and genotype/phenotype cosegregation remains important even when mis-splicing is confirmed. J Clin Med Res. 2017;9(8):709-718 doi: https://doi.org/10.14740/jocmr2894w
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".