Molecular Evolution of Regulatory Genes in Spruces from Different Species and Continents: Heterogeneous Patterns of Linkage Disequilibrium and Selection but Correlated Recent Demographic Changes
Bibliographic record
Abstract
Genes involved in transcription regulation may represent valuable targets in association genetics studies because of their key roles in plant development and potential selection at the molecular level. Selection and demographic signatures at the sequence level were investigated for five regulatory genes belonging to the knox-I family (KN1, KN2, KN3, KN4) and the HD-Zip III family (HB-3) in three Picea species affected by post-glacial recolonization in North America and Europe. To disentangle neutral and selective forces and estimate linkage disequilibrium (LD) on a gene basis, complete or nearly complete gene sequences were analysed. Nucleotide variation within species, haplotype structure, LD, and neutrality tests, in addition to coalescent simulations based on Tajima's D and Fay and Wu's H, were estimated. Nucleotide diversity was generally low in all species (average pi = 0.002-0.003) and much heterogeneity was seen in LD and selection signatures among genes and species. Most of the genes harboured an excess of both rare and frequent alleles in the three species. Simulations showed that this excess was significantly higher than that expected under neutrality and a bottleneck during the Last Glacial Maximum followed by population expansion at the Pleistocene/Holocene boundary or shortly after best explains the correlated sequence patterns. These results indicate that despite recent large demographic changes in the three boreal species from two continents, species-specific selection signatures could still be detected from the analysis of nearly complete regulatory gene sequences. Such different signatures indicate differential subfunctionalization of gene family members in the three congeneric species.
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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 | 0.001 | 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 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".