Evidence for Selection on Gene Expression in Cultivated Rice (Oryza sativa)
Bibliographic record
Abstract
Artificial selection has been used throughout plant domestication and breeding to develop crops that are adapted to diverse environments. Here, we investigate whether gene regulatory changes have been widespread targets of lineage-specific selection in cultivated lines Minghui 63 and Zhenshan 97 of rice, Oryza sativa. A line experiencing positive selection for either an increase or a decrease in genes' transcript abundances is expected to have an overabundance of expression quantitative trait locus (eQTL) alleles that increase or decrease those genes' expression, respectively. Results indicate that several genes that share Gene Ontology terms or are members of the same coexpression module have eQTL alleles from one parent that consistently increase gene expression relative to the second parent. A second line of evidence for lineage-specific selection is an overabundance of cis-trans pairs of eQTL alleles that affect gene expression in the same direction (are reinforcing). Across all cis-trans pairs of eQTL, including pairs that both weakly and strongly affect gene expression, there is no evidence for selection. However, the frequency of genes with reinforcing eQTL increases with eQTL strength. Therefore, there is evidence that eQTL with strong effects were positively selected during rice cultivation. Among 41 cis-trans pairs with strong trans eQTL, 31 have reinforcing eQTL. Several of the candidate genes under positive selection accurately predict phenotypic differences between Minghui 63 and Zhenshan 97. Overall, our results suggest that positive selection for regulatory alleles may be a key factor in plant improvement.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".