Paternally‐biased gene expression follows kin‐selected predictions in female honey bee embryos
Bibliographic record
Abstract
The Kinship Theory of Genomic Imprinting (KTGI) posits that, in species where females mate with multiple males, there is selection for a male to enhance the reproductive success of his offspring at the expense of other males and his mating partner. Reciprocal crosses between honey bee subspecies show parent-of-origin effects for reproductive traits, suggesting that males modify the expression of genes related to female function in their female offspring. This effect is likely to be greater in the Cape honey bee (Apis mellifera capensis), because a male's daughters have the unique ability to produce female offspring that can develop into reproductive workers or the next queen without mating. We generated reciprocal crosses between Capensis and another subspecies and used RNA-seq to identify transcripts that are over- or underexpressed in the embryos, depending on the parental origin of the gene. As predicted, 21 genes showed expression bias towards the Capensis father's allele in colonies with a Capensis father, with no such bias in the reciprocal cross. A further six genes showed a consistent bias towards expression of the father's allele across all eight colonies examined, regardless of the direction of the cross. Consistent with predictions of the KTGI, six of the 21 genes are associated with female reproduction. No gene consistently showed overexpression of the maternal allele.
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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".