The Candidate Gene, Clock, Localizes to a Strong Spawning Time Quantitative Trait Locus Region in Rainbow Trout
Bibliographic record
Abstract
We applied a candidate gene mapping approach to an existing quantitative trait loci (QTL) data set for spawning date in rainbow trout (Oncorynchus mykiss) to ascertain whether these genes could potentially account for any observed QTL effects. Several genes were chosen for their known or suspected roles in reproduction, circadian, or circannual timing, including salmon-type gonadotropin-releasing hormone 3A and 3B (GnRH3A and GnRH3B), Clock, Period1, and arylalkylamine N-acetlytransferase-1 and -2 (AANAT-1 and AANAT-2). Genes were sequenced, and polymorphisms were identified in parents of two rainbow trout mapping families, one of which was used previously to detect spawn timing QTL. Interval mapping was used to identify associations between genetic markers and spawning date effects. Using a genetic map that was updated with 574 genetic markers (775 total), we found evidence for 11 significant or suggestive QTL regions. Most QTL were only localized within one of the parents; however, a strong QTL region was identified in both female and male parents on linkage group RT-8 that explained 20% and 50% of trait variance, respectively. The Clock gene mapped to this region. Period1 mapped to a region in the female parent associated with a marginal effect (P = .056) on spawn timing. Other candidate genes were not associated with significant QTL effects.
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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".