Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout
Bibliographic record
Abstract
Abstract Sea lice ( Caligus rogercresseyi ) are ectoparasites that cause major production losses in the salmon aquaculture industry worldwide. Atlantic salmon ( Salmo salar ) and rainbow trout ( Oncorhynchus mykiss ) are two of the most susceptible salmonid species to sea lice infestation. The goal of this study was to identify common candidate genes involved in resistance against sea lice. For this, 2,626 Atlantic salmon and 2,643 rainbow trout from breeding populations were challenged with sea lice and genotyped with a 50k and 57k SNP panel. We ran two independent genome-wide association studies for sea lice resistance on each species and identified 7 and 13 windows explaining 3% and 2.7% respectively the genetic variance. Heritabilities were observed with values of 0.19 for salmon and 0.08 for trout. We identified genes associated with immune responses, cytoskeletal factors and cell migration. We found 15 orthogroups which allowed us to identify dust8 and dust10 as candidate genes in orthogroup 13. This suggests that similar mechanisms can regulate resistance in different species; however, they most likely do not share the same standing variation within the genomic regions and genes that regulate resistance. Our results provide further knowledge and may help establish better control for sea lice in fish populations.
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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 | 0.001 | 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) | 0.001 | 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".