The Relationship between Selection, Network Connectivity, and Regulatory Variation within a Population of Capsella grandiflora
Bibliographic record
Abstract
Interactions between genes can have important consequences for how selection shapes sequence variation at these genes. Specifically, genes that have pleiotropic effects by affecting the expression level of many other genes may be under stronger selective constraint. We used coexpression networks to measure connectivity between genes and investigated the relationship between gene connectivity and selection in a natural population of the plant Capsella grandiflora. We observed that network connectivity was negatively correlated with genetic divergence due to stronger negative selection on highly-connected genes even when controlling for variation in gene expression level. However, the presence of local regulatory variation for a gene's expression level was also associated with reduced negative selection and lower gene connectivity. While it is difficult to disentangle the causal relationships between these factors, our results show that both connectivity and local regulatory variation are important factors for explaining variation in selection between genes.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".