Peptide biomarkers used for the selective breeding of a complex polygenic trait in honey bees
Bibliographic record
Abstract
We present a novel way to select for highly polygenic traits. For millennia, humans have used observable phenotypes to selectively breed stronger or more productive livestock and crops. Selection on genotype, using single-nucleotide polymorphisms (SNPs) and genome profiling, is also now applied broadly in livestock breeding programs; however, selection on protein/peptide or mRNA expression markers has not yet been proven useful. Here we demonstrate the utility of protein markers to select for disease-resistant hygienic behavior in the European honey bee (Apis mellifera L.). Robust, mechanistically-linked protein expression markers, by integrating cis- and trans- effects from many genomic loci, may overcome limitations of genomic markers to allow for selection. After three generations of selection, the resulting marker-selected stock outperformed an unselected benchmark stock in terms of hygienic behavior, and had improved survival when challenged with a bacterial disease or a parasitic mite, similar to bees selected using a phenotype-based assessment for this trait. This is the first demonstration of the efficacy of protein markers for industrial selective breeding in any agricultural species, plant or animal.
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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 | 0.001 | 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 | 0.001 | 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".