Gene copy number variations involved in balsam poplar (<i>Populus balsamifera</i> L.) adaptive variations
Bibliographic record
Abstract
Abstract Gene copy number variations (CNVs) involved in phenotypic variations have already been shown in plants, but genomewide testing of CNVs for adaptive variation was not doable until recent technological developments. Thus, reports of the genomic architecture of adaptation involving CNVs remain scarce to date. Here, we investigated F1 progenies of an intraprovenance cross (north–north cross, 58th parallel) and an interprovenances cross (north–south cross, 58th/49th parallels) for CNVs using comparative genomic hybridization on arrays of probes targeting gene sequences in balsam poplar (Populus balsamifera L.), a widespread North American forest tree. A total of 1,721 genes were found in varying copy numbers over the set of 19,823 tested genes. These gene CNVs presented an estimated average size of 8.3 kb and were distributed over poplar's 19 chromosomes including 22 hotspot regions. Gene CNVs number was higher for the interprovenance progeny in accordance with an expected higher genetic diversity related to the composite origin of this family. Regression analyses between gene CNVs and seven adaptive trait variations resulted in 23 significant links; among these adaptive gene CNVs, 30% were located in hotspots. One‐to‐five gene CNVs were found related to each of the measured adaptive traits and annotated for both biotic and abiotic stress responses. These annotations can be related to the occurrence of a higher pathogenic pressure in the southern parts of balsam poplar's distribution, and higher photosynthetic assimilation rates and water‐use efficiency at high latitudes. Overall, our findings suggest that gene CNVs typically having higher mutation rates than SNPs may in fact represent efficient adaptive variations against fast‐evolving pathogens.
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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) | 0.001 | 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".