Transcriptome and association mapping revealed functional genes respond to drought stress in Populus
Bibliographic record
Abstract
Drought frequency and severity are exacerbated by global climate change, which could compromise forest ecosystems. However, there have been minimal efforts to systematically investigate the genetic basis of the response to drought stress in perennial trees. Here, we implemented a systems genetics approach that combines co-expression analysis, association genetics, and expression quantitative trait nucleotide (eQTN) mapping to construct an allelic genetic regulatory network comprising four key regulators (PtoeIF-2B, PtoABF3, PtoPSB33, and PtoLHCA4) under drought stress conditions. Furthermore, Hap_01PtoeIF-2B, a superior haplotype associated with the net photosynthesis, was revealed through allelic frequency and haplotype analysis. In total, 75 candidate genes related to drought stress were identified through transcriptome analyses of five Populus cultivars (P. tremula × P. alba, P. nigra, P. simonii, P. trichocarpa, and P. tomentosa). Through association mapping, we detected 92 unique SNPs from 38 genes and 104 epistatic gene pairs that were associated with six drought-related traits by association mapping. eQTN mapping unravels drought stress-related gene loci that were significantly associated with the expression levels of candidate genes for drought stress. In summary, we have developed an integrated strategy for dissecting a complex genetic network, which facilitates an integrated population genomics approach that can assess the effects of environmental threats.
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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 | 0.000 |
| 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".