The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice
Bibliographic record
Abstract
Genomic selection (GS) has revolutionized breeding practices by integrating genotype and phenotype data to predict genomic estimated breeding values, offering the potential to accelerate breeding cycles and intensify and enhance early-stage selections. This approach utilizes the concept of linkage disequilibrium (LD) between genetic markers and quantitative trait loci within populations. LD, the nonrandom association between alleles at different loci, provides valuable insights into historical recombination patterns, although it can change over time under strong selection or genetic drift. This study aimed to investigate the influence of recombination on haplotype sizes and LD, assess the impact of additive (A) versus additive + epistasis (A+I) genetic models on GS predictive ability (PA), and demonstrate how haplotype resolution in the training set (TS) impacts the PA of GS. For this, we used biparental (MP2) and multiparent (MP6-8) populations, where the main difference between them was the recombination rate. As expected, a strong correlation between LD decay and the number of recombination opportunities within populations was observed, with smaller haplotype blocks in populations experiencing more recombination. The use of A+I models increased heritability but did not improve PA. Finally, populations with smaller haplotype sizes in the TS exhibited enhanced PA. This study demonstrates the effect of haplotype size on GS accuracy, and its uniqueness lies in its focus on populations where the primary differentiating factor is haplotype size. It offers an important tool for breeders in designing GS strategies, providing valuable guidance for future breeding efforts.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".