Scale and direction of adaptive introgression between black cottonwood (<i>Populus trichocarpa</i>) and balsam poplar (<i>P. balsamifera</i>)
Bibliographic record
Abstract
Introgression can introduce novel genetic variation at a faster rate than mutation alone and result in adaptive introgression when adaptive alleles are maintained in the recipient genome over time by natural selection. A previous study from our group demonstrated adaptive introgression from Populus balsamifera into P. trichocarpa in a target genomic region. Here we expand our local ancestry analysis to the whole genome of both parents to provide a comprehensive view of introgression patterns and to identify additional candidate regions for adaptive introgression genomewide. Populus trichocarpa is a large, fast-growing tree of mild coastal regions of the Pacific Northwest, whereas P. balsamifera is a smaller stature tree of continental and boreal regions with intense winter cold. The species hybridize where they are parapatric. We detected asymmetric patterns of introgression across the whole genome of these two poplar species adapted to contrasting environments, with stronger introgression from P. balsamifera to P. trichocarpa than vice versa. Admixed P. trichocarpa individuals contained more genomic regions with unusually high levels of introgression (19 regions) and also the largest introgressed genome fragment (1.02 Mb) compared with admixed P. balsamifera (nine regions). Our analysis also revealed numerous candidate regions for adaptive introgression with strong signals of selection, notably related to disease resistance, and enriched for genes that may play crucial roles in survival and adaptation. Furthermore, we detected a potential overrepresentation of subtelomeric regions in P. balsamifera introgressed into P. trichocarpa and possible protection of sex-determining regions from interspecific gene flow.
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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".