Analysis of <i>DICER1</i> in familial and sporadic cases of transposition of the great arteries
Bibliographic record
Abstract
OBJECTIVE: We previously identified a pathogenic germline DICER1 variant in a child with transposition of the great arteries who was a member of a family with DICER1 syndrome. In view of a report linking Dicer1 knockout in murine cardiomyocytes to cardiac outflow defects, we investigated the involvement of DICER1 in transposition of the great arteries. DESIGN: We used Fluidigm access array followed by next generation sequencing to screen for variants in the coding exons, their exon/intron boundaries and the 3' untranslated region of DICER1 in patient DNA. CASES: Germline DNA was collected from 129 patients with either sporadic or familial forms of transposition of the great arteries from two sites in Australia and Italy. RESULTS: Most cases (85%) did not have any germline DICER1 variants. In the remaining 15% of cases, we identified 16 previously reported variants (5 synonymous, 6 intronic, and 5 missense) and 2 novel variants (1 intronic and 1 missense). None of the identified variants were predicted to be pathogenic. CONCLUSIONS: Here, we report that neither likely pathogenic nor pathogenic variants in DICER1 appear to play a major role in transposition of the great arteries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".