GENETIC ARCHITECTURE OF PHENOTYPIC AND TRANSCRIPTIONAL VARIATION IN SALMON
Bibliographic record
Abstract
Understanding the genetic determinants of phenotypic variation is crucial for a predictive evolutionary theory. Although Fisher's fundamental theorem provides a simple quantitative framework for evolutionary processes, the underlying assumptions regarding the heritability and variability of traits and population structure can diverge from real systems drastically. Therefore, the genetic architecture of traits associated with fitness should be explored to verify the theory's relevance to evolutionary changes and its universality, but this isn't practiced much in natural systems. Pacific salmon provide an excellent model system to examine genetic architecture and variance structure in and among populations. Here, I analyzed trait inheritance in salmon, and characterized the underlying adaptive significance under different ecological scenarios. Using transcriptional traits, I examined the relationship between plasticity and genetic differentiation shaping salmon populations. I employed common garden rearing and factorial mating designs to evaluate the genetic architecture of traits under physiological stress (i.e. saltwater, temperature and immune) to explore phenotypic variance under different environments. In Chapter 2, I showed osmoregulation gene transcription diverged after anadromous steelhead trout (Oncorhynchus mykiss) were introduced to a landlocked lake, and non-additive inheritance of traits was common among diverging populations. In Chapter 3, the variation in innate immune response gene transcription was shown to be mediated by non-additive effects in farmed Chinook salmon (O. tshawytscha), and the effect was elevated after the immune stimulation with Vibrio vaccine. In Chapter 4, significant maternal components in traits closely related to fitness confounded the differences observed among populations. Finally, in Chapter 5, I characterized the among-population variance structure associated with individual response to immune stimulation using a multigene microarray approach. Overall, my research suggests that transcription and phenotypic plasticity is different among salmon populations, can rapidly evolve, and that non-additive genetic effects in transcriptional and phenotypic variation is common in salmon. In general these results are important to question applicability of fundamental theorem for salmon populations, hence conservational strategies based on evolutionary concerns. Furthermore, it presents a framework of population differentiation in salmon based on modifications to physiological response. These two combined would help us to unravel how salmon populations are structured in space and time.
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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 | 0.000 | 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) | 0.001 | 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".