SNP in pre-miR-1666 decreases mature miRNA expression and is associated with chicken performance
Bibliographic record
Abstract
Polymorphisms in miRNA genes could potentially alter various biological processes by influencing the processing and (or) target selection of miRNAs. The rs14120863 (C > G) mutation, which we characterized in a Gushi-Anka F2 resource population, resides in the precursor region of miR-1666. Association analysis with chicken carcass and growth traits showed that the SNP was significantly associated with carcass weight, evisceration weight, breast muscle weight, leg muscle weight, and body weight at 8 weeks of age, as well as some body size indexes including shank girth, chest breadth, breast bone length, and body slanting length, in the Gushi-Anka F2 resource population. Quantitative RT-PCR results showed that miR-1666 expression levels in muscle tissues differed within various genotypes. Experiment in DF1 cells further confirmed that the SNP in miR-1666 could significantly alter mature miRNA production. Subsequently, using dual-luciferase report assay, we verified that miR-1666 could perform its function through targeting of the CBFB gene. In conclusion, the SNP in the precursor of miR-1666 could significantly reduce mature miR-1666 production. It may further affect the function of miR-1666 through the target gene CBFB, hence it is associated with chicken growth traits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".