Genetic architecture of acute hyperthermia resistance in juvenile rainbow trout (Oncorhynchus mykiss) and genetic correlations with production traits
Bibliographic record
Abstract
Abstract Background Selective breeding is a promising solution to reduce fish farms vulnerability to heat peaks which intensity and frequency are predicted to increase due to climate change. However, limited information about the genetic architecture of acute hyperthermia resistance in fish is available. Two batches of sibs from a rainbow trout commercial line were produced. The first batch (N=1,382) was phenotyped for acute hyperthermia resistance at nine months, and the second batch (N=1,506) was phenotyped for main production traits (growth, body length, muscle fat content and carcass yield) at twenty months. Fish were genotyped on a 57K SNP array, and their genotypes were imputed at high-density thanks to their parents being genotyped on a 665K SNP array. Results The heritability estimate of resistance to acute hyperthermia in juveniles was 0.29 ± 0.05, confirming the potential of selective breeding for this trait. Genetic correlations between acute hyperthermia resistance and main production traits at near harvest age were all close to zero. Hence, selecting for acute hyperthermia resistance should not impact the main production traits, and reversely. The genome-wide association study revealed that resistance to acute hyperthermia is highly polygenic; altogether, the six detected QTL explained less than 5% of the genetic variance. Two of these QTL, including the most significant one, might explain acute hyperthermia resistance differences across INRAE isogenic lines of rainbow trout. The phenotypic mean differences between homozygotes at peak SNP were up to 69% of the phenotypic standard deviation, showing promising potential for marker-assisted selection. We identified 89 candidate genes within the six QTL regions, among which the most convincing functional candidate genes were dnajc7 , hsp70b , nkiras2 , cdk12 , phb , fkbp10 , ddx5 , cygb1 , enpp7 , pdhx and acly . Conclusions This study provides valuable insight on the genetic architecture of acute hyperthermia resistance in juvenile rainbow trout. The potential for the selective breeding of this trait was shown to be substantial and should not interfere with selection for main production traits. Identified functional candidate genes give a new insight on physiological mechanisms involved in acute hyperthermia resistance, such as protein chaperoning, oxidative stress response, homeostasis maintenance and cell survival.
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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 | 0.000 |
| 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".