Genome scan for nonadditive heterotic trait loci reveals mainly underdominant effects in <i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
The overdominant model of heterosis explains the superior phenotype of hybrids by synergistic allelic interaction within heterozygous loci. To map such genetic variation in yeast, we used a population doubling time dataset of Saccharomyces cerevisiae 16 × 16 diallel and searched for major contributing heterotic trait loci (HTL). Heterosis was observed for the majority of hybrids, as they surpassed their best parent growth rate. However, most of the local heterozygous loci identified by genome scan were surprisingly underdominant, i.e., reduced growth. We speculated that in these loci adverse effects on growth resulted from incompatible allelic interactions. To test this assumption, we eliminated these allelic interactions by creating hybrids with local hemizygosity for the underdominant HTLs, as well as for control random loci. Growth of hybrids was indeed elevated for most hemizygous to HTL genes but not for control genes, hence validating the results of our genome scan. Assessing the consequences of local heterozygosity by reciprocal hemizygosity and allele replacement assays revealed the influence of genetic background on the underdominant effects of HTLs. Overall, this genome-wide study on a multi-parental hybrid population provides a strong argument against single gene overdominance as a major contributor to heterosis, and favors the dominance complementation model.
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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".