A microsatellite linkage map of rainbow trout and its application in QTL analysis
Bibliographic record
Abstract
The majority of species and strains reared globally for aquaculture are relatively unimproved for commercially important traits. The potential for genetic improvement in fish species compared with domestic livestock, is very high. Therefore, we are integrating molecular genetic technologies into aquaculture to help solve some of the major genetic problems. Our long-term goal is to use genetic markers to increase the efficiency of artificial selection in fish stock improvement. To do this, marker-assisted selection (MAS) has been proposed. MAS can be carried out with an understanding of the linkage relationships between quantitative trait loci (QTL) and markers. To identify QTL controlling traits of economic importance, a genetic linkage map is required, with variable markers distributed throughout the genome. We have constructed a genetic linkage map for rainbow trout using 192 microsatellite, 3 RAPD, 5 ESMP, and 7 allozyme markers in three backcross families. As a first step towards MAS, some QTLs associated with economically important traits have been identified using this linkage map. The genetic linkage map based on microsatellites could be useful for QTL analysis in aquaculture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".