Identification of candidate genes for chicken early- and late-feathering
Bibliographic record
Abstract
Previous studies suggest that prolactin receptor (Prlr) is a potential causative gene for chicken early- (EF) and late-feathering (LF) phenotypes. In this study, we evaluated candidate genes for this trait and determined the expression of 3 genes, including Prlr, sperm flagellar protein 2 (Spef2), and their fusion gene, in the skins of one-day-old EF and LF chicks using RT-qPCR. Data indicated that Prlr expression in the skin did not show significant difference between EF and LF chicks, suggesting Prlr may not be a suitable candidate gene. In contrast, Spef2 expression in the skin displayed a significant difference between EF and LF chicks (P < 0.01), suggesting that Spef2 may be a good candidate gene for chicken feathering. Moreover, dPrlr/dSpef2, the fusion gene, was also a good candidate gene as it was expressed only in LF chicks. However, the expression of the fusion gene was much lower than that of Prlr Additionally, using strand-specific primers, we found that the fusion gene was transcribed in 2 directions (one from dPrlr promoter, another from dSpef2 promoter), which could result in the formation of a double strand RNA. In conclusion, both Spef2 and the fusion gene are good candidate genes for chicken feathering, but Prlr is not. The research on the function and regulation of the candidate genes will help elucidate the molecular basis of the chicken feathering trait.
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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 | 0.000 | 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".