Genomic Screening for Artificial Selection during Domestication and Improvement in Maize
Bibliographic record
Abstract
BACKGROUND: Artificial selection results in phenotypic evolution. Maize (Zea mays L. ssp. mays) was domesticated from its wild progenitor teosinte (Zea mays subspecies parviglumis) through a single domestication event in southern Mexico between 6000 and 9000 years ago. This domestication event resulted in the original maize landrace varieties. The landraces provided the genetic material for modern plant breeders to select improved varieties and inbred lines by enhancing traits controlling agricultural productivity and performance. Artificial selection during domestication and crop improvement involved selection of specific alleles at genes controlling key morphological and agronomic traits, resulting in reduced genetic diversity relative to unselected genes. SCOPE: This review is a summary of research on the identification and characterization by population genetics approaches of genes affected by artificial selection in maize. CONCLUSIONS: Analysis of DNA sequence diversity at a large number of genes in a sample of teosintes and maize inbred lines indicated that approx. 2 % of maize genes exhibit evidence of artificial selection. The remaining genes give evidence of a population bottleneck associated with domestication and crop improvement. In a second study to efficiently identify selected genes, the genes with zero sequence diversity in maize inbreds were chosen as potential targets of selection and sequenced in diverse maize landraces and teosintes, resulting in about half of candidate genes exhibiting evidence for artificial selection. Extended gene sequencing demonstrated a low false-positive rate in the approach. The selected genes have functions consistent with agronomic selection for plant growth, nutritional quality and maturity. Large-scale screening for artificial selection allows identification of genes of potential agronomic importance even when gene function and the phenotype of interest are unknown. These approaches should also be applicable to other domesticated species if specific demographic conditions during domestication exist.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".