Integrating the markers Pan I and haemoglobin with the genetic linkage map of Atlantic cod (Gadus morhua)
Bibliographic record
Abstract
BACKGROUND: Haemoglobin (Hb) and pantophysin (Pan I) markers have been used intensively in population studies of Atlantic cod (Gadus morhua) and in the analysis of traits such as temperature tolerance, growth characteristics and sexual maturation. We used an Illumina GoldenGate panel and the KASPar SNP genotyping system to analyse SNPs in three Atlantic cod families, one of which was polymorphic at the Hb β1 locus, and to generate a genetic linkage map integrating Pan I and multiple Hb loci. FINDINGS: Data generated allowed the mapping of nine Hb loci, the Pan I locus, and other 122 SNPs onto an existing linkage genetic map for Atlantic cod. Four Hb genes (i.e. α1, α4, β1 and β5) have been mapped on linkage group (LG) 2 while the other five (i.e. α2, α3, β2, β3 and β4) were placed on LG18. Pan I was mapped on LG 1 using a newly developed KASPar assay for a SNP variable only in Pan IA allelic variants. The new linkage genetic map presented here comprises 1046 SNPs distributed between 23 linkage groups, with a length of 1145.6 cM. A map produced by forcing additional loci, resulting in a reduced goodness-of-fit for mapped markers, allowed the mapping of a total of 1300 SNPs. Finally, we compared our genetic linkage map data with the genetic linkage map data produced by a different group and identified 29 shared SNPs distributed on 10 different linkage groups. CONCLUSIONS: The genetic linkage map presented here incorporates the marker Pan I, together with multiple Hb loci, and integrates genetic linkage data produced by two different research groups. This represents a useful resource to further explore if Pan I and Hbs or other genes underlie quantitative trait loci (QTL) for temperature sensitivity/tolerance or other phenotypes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".