Prediction of Genetic Gains from Selection in Tree Breeding
Bibliographic record
Abstract
The prediction of genetic gain from artificial selection in a trait is important in plant and animal breeding. Lush’s classical breeder’s equation (BE) is widely used for this purpose, although it is also applied to predicting evolution under natural selection. The current application of high throughput sequencing techniques potentially allows breeders at the individual gene level to capture both additive and non-additive genetic effects. Here, we provide a comprehensive evaluation of predicting genetic gains from the selection at multiple hierarchical levels of population structure (provenances, families within provenances, and individuals within families within provenances). We discuss the processes that could influence the power of prediction under the classical BE, including genetic drift, natural selection, and gene flow. We extend the classical BE to molecular breeding methods for improving the prediction of genetic gains; they include the conventional breeding approach, marker-assistant selection (MAS), genome-wide association study (GWAS), and genomic selection (GS). Lastly, we discuss the genetic gains from the selection using multi-omics traits, including gene expression and epigenetic traits. Our overall synthesis should contribute to a better understanding of predicting genetic gains from the artificial selection under classical and molecular breeding.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".