Contemporary evolution of maize landraces and their wild relatives influenced by gene flow with modern maize varieties
Bibliographic record
Abstract
Mexico is recognized as the center of origin and domestication of maize. Introduction of modern maize varieties (MVs) into Mexico raised concerns regarding the possible effects of gene flow from MVs into maize landraces (LRs) and their wild relatives (WRs), teosintes. However, after more than 60 y from the release of the first MVs, the impact of the sympatry with LRs and their WRs has not been explored with genetic data. In this work, we assessed changes in the genomes of 7 maize LRs and 2 WR subspecies from collections spanning over 70 y. We compared the genotypes obtained by genotyping by sequencing (GBS) for LRs and WRs before and after the adoption of MVs, and observed introgression from sympatric MVs into LRs and into the WR Zea mays ssp. mexicana sampled after the year 2000. We also found a decrease in the paired divergence index ( F ST ) between MV-LR and MV-WR over the same time frame. Moreover, we determined that LR genetic diversity increased after 2000, probably as a result of gene flow from MVs introduced in the 1990s. Our findings allowed us to identify ongoing changes in the domesticated and wild maize genetic pools, and concur with previous works that have evaluated short-term gene flow from MVs into LRs in other crops. Our approach represents a useful tool for tracking evolutionary change in wild and domesticated genetic resources, as well as for developing strategies for their conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".