Loci associated with variation in gene expression and growth in juvenile salmon are influenced by the presence of a growth hormone transgene
Bibliographic record
Abstract
BACKGROUND: Growth regulation is a complex process influenced by genetic and environmental factors. We examined differences between growth hormone (GH) transgenic (T) and non-transgenic (NT) coho salmon to elucidate whether the same loci were involved in controlling body size and gene expression phenotypes, and to assess whether physiological transformations occurring from GH transgenesis were under the influence of alternative pathways. The following genomic techniques were used to explore differences between size classes within and between transgenotypes (T vs. NT): RNA-Seq/Differentially Expressed Gene (DEG) analysis, quantitative PCR (qPCR) and OpenArray analysis, Genotyping-by-Sequencing, and Genome-Wide Association Study (GWAS). RESULTS: ) spanned a broad range of biological processes, indicating wide-spread influence of the transgene on gene expression. Overexpression of growth hormone led to differences in regulatory loci between transgenotypes and size classes. Expression levels were significantly greater in T fish at 16 of 31 loci and in NT fish for 10 loci. Eleven genes exhibited different mRNA levels when the interaction of size and transgenotype was considered (IGF1, IGFBP1, GH, C3-4, FAS, FAD6, GLUT1, G6PASE1, GOGAT, MID1IP1). In the GWAS, 649 unique SNPs were significantly associated with at least one study trait, with most SNPs associated with one of the following traits: C3_4, ELA1, GLK, IGF1, IGFBP1, IGFII, or LEPTIN. Only 1 phenotype-associated SNP was found in common between T and NT fish, and there were no SNPs in common between transgenotypes when size was considered. CONCLUSIONS: Multiple regulatory loci affecting gene expression were shared between fast-growing and slow-growing fish within T or NT groups, but no such regulatory loci were found to be shared between NT and T groups. These data reveal how GH overexpression affects the regulatory responses of the genome resulting in differences in growth, physiological pathways, and gene expression in T fish compared with the wild type. Understanding the complexity of regulatory gene interactions to generate phenotypes has importance in multiple fields ranging from applications in selective breeding to quantifying influences on evolutionary processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".