Theoretical efficiency of multiple‐trait quantitative trait loci‐assisted selection
Bibliographic record
Abstract
The effectiveness of five selection methods for genetic improvement of net merit comprising trait 1 of low heritability (h(2) = 0.1) and trait 2 of high heritability (h(2) = 0.4) was examined: (i) two-trait quantitative trait loci (QTL)-assisted selection; (ii) partial QTL-assisted selection based on trait 1; (iii) partial QTL-assisted selection based on trait 2; (iv) QTL-only selection; and (v) conventional selection index without QTL information. These selection methods were compared under 72 scenarios with different combinations of the relative economic weights, the genetic correlations between traits, the ratio of QTL variance to total genetic variance of the trait, and the ratio of genetic variances between traits. The results suggest that the detection of QTL for multiple-trait QTL-assisted selection is more important when the index traits are negatively correlated than when they are positively correlated. In contrast to literature reports that single-trait marker-assisted selection (MAS) is the most efficient for low heritability traits, this study found that the identified QTL of the low heritability trait contributed negligibly to total response in net merit. This is because multiple-trait QTL-assisted selection is designed to maximize total net merit rather than the genetic response of the individual index trait as in the case of single-trait MAS. Therefore, it is not economical to identify the QTL of the low heritability traits for the improvement of total net merit. The efficient, cost-effective selection strategy is to identify the QTL of the moderate or high heritability traits of the QTL-assisted selection index to facilitate total economic returns. Detection of the QTL of the low h(2) traits for the QTL-assisted index selection is justified when the low h(2) traits have high negative genetic correlation with the other index traits and/or when both economic weights and genetic variances of the low h(2) traits are larger as compared to the other index traits of higher h(2). This study deals with theoretical efficiency of QTL-assisted selection, but the same principle applies to SNP-based genomic selection when the proportion of the genetic variance 'explained by the identified QTLs' in this study is replaced by 'explained by SNPs'.
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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 | 0.006 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".