Genomic Screening for Artificial Selection during Domestication and Improvement in Maize
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it