Expression biomarkers used for the selective breeding of complex polygenic traits
Bibliographic record
Abstract
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 quantitative trait loci (QTLs), is also now applied broadly in livestock breeding programs; however, selection on protein or mRNA expression markers have not been proved useful yet. Here we demonstrate the utility of protein markers to select for disease-resistant behaviour in the European honey bees ( 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 stock performed as well or better than bees selected using phenotype–based assessment of this trait, when challenged with disease. This is the first demonstration of the efficacy of protein markers for selective breeding in any agricultural species, plant or animal. Significance statement The honey bee has been in the news a lot recently, largely because of world-wide die-offs due to the parasitic Varroa mite, which is becoming resistant to the chemical controls the bee industry uses. In this study, we show that robust expression biomarkers of a disease-resistance trait can be used, in an out-bred population, to select for that trait. After three generations of selection, the resulting stock performed as well or better than bees selected using the phenotypic best method for assessing this trait when challenged with disease. This is the first demonstration of an expression marker for selective breeding in any agricultural species, plant or animal. This also represents a completely novel way to select for highly polygenic traits.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".