MHC class IIB alleles contribute to both additive and nonadditive genetic effects on survival in Chinook salmon
Bibliographic record
Abstract
The genes of the major histocompatibility complex (MHC) are found in all vertebrates and are an important component of individual fitness through their role in disease and pathogen resistance. These genes are among the most polymorphic in genomes and the mechanism that maintains the diversity has been actively debated with arguments for natural selection centering on either additive or nonadditive genetic effects. Here, we use a quantitative genetics breeding design to examine the genetic effects of MHC class IIB alleles on offspring survivorship in Chinook salmon (Oncorhynchus tshawytscha). We develop a novel genetic algorithm that can be used to assign values to specific alleles or genotypes. We use this genetic algorithm to show simultaneous additive and nonadditive effects of specific MHC class IIB alleles and genotypes on offspring survivorship. The additive effect supports the rare-allele hypothesis as a potential mechanism for maintaining genetic diversity at the MHC. However, contrary to the overdominance hypothesis, the nonadditive effect led to underdominance at one heterozygous genotype, which could instead reduce variability at the MHC. Our algorithm is an advancement over traditional animal models that only partition variance in fitness to additive and nonadditive genetic effects, but do not allocate these effects to specific alleles and genotypes. Additionally, we found evidence of nonrandom segregation during meiosis in females that promotes an MHC allele that is associated with higher survivorship. Such nonrandom segregation could further reduce variability at the MHC and may explain why Chinook salmon has one of the lowest levels of MHC diversity of all vertebrates.
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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 | 0.000 |
| 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".