The Effect of Linkage on Genetic Variances within Biparental Simulated and <i>Zea mays</i> Populations
Bibliographic record
Abstract
ABSTRACT Hybrid yield improvement in maize (Zea mays L.) has been attributed in part to the development of elite inbred lines within distinct germplasm groups adapted to distinct environments. Breeding largely within groups has caused the accumulation of distinct favorable alleles for adaptation, yield, and yield component traits. Favorable alleles may be linked to other favorable alleles in coupling phase or linked to unfavorable alleles in repulsion phase. To investigate the effects of linkage on genetic variances, we used a simple, two‐locus model to simulate populations of intermated and standard recombinant inbred lines (RILs) derived from parental lines with coupling or repulsion phase loci. Genetic variances differ between simulated intermated RILs (IRIL) and RIL testcross populations as a function of population size, the effect sizes of linked loci, and the genetic distance between loci. We also generated RIL and IRIL testcross (TC) populations from two short‐season inbred lines from different heterotic groups and evaluated yield, three yield component traits, and five flowering time–associated traits. For all nine traits, the inbred lines have accumulated distinct favorable alleles. Genetic variances are high and transgressive phenotypes are frequent in both testcross populations. Intermating does not increase genetic variation. Genetic variances of six traits within the RIL‐TC population are nominally between 1.25 and 1.75‐fold greater than variances within the IRIL‐TC population. We conclude that the genomes of the parental inbred lines may harbor coupling rather than repulsion phase loci and heterosis is unlikely due to pseudo‐overdominance.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".