Linkage‐linkage disequilibrium dissection of the epigenetic quantitative trait loci (epiQTLs) underlying growth and wood properties in <i>Populus</i>
Bibliographic record
Abstract
Abstract Increasing evidence indicates that DNA methylation is heritable and serves as an essential marker contributing to phenotypic variation. Linkage‐linkage disequilibrium mapping was used to decipher the epigenetic architecture underlying nine growth and wood property traits in a linkage population (550 F1 progeny) and a natural population (435 unrelated individuals) of Populus using methylation‐sensitive amplification polymorphism (MSAP)‐based analysis. The interactions between genetic and epigenetic variants in the causative genes was further unveiled using expression quantitative trait methylation (eQTM) and nucleotide (eQTN) mapping strategies. A total of 163 epigenetic quantitative trait loci (epiQTLs; LOD ≥ 3.0), explaining 1.7–44.5% of phenotypic variations, were mapped to a high‐resolution epigenetic map with 19 linkage groups, which was supported by the significant MSAP associations (P < 0.001) in the two populations. There were 23 causal genes involved in growth regulation and wood formation, whose markers were located in epiQTLs and associated with the same traits in both populations. Further eQTN and eQTM mapping showed that causal genetic and epigenetic variants within the 23 candidate genes may interact more in trans in gene expression and phenotype. The present study provides strategies for investigating epigenetic architecture and the interaction between genetic and epigenetic variants modulating complex traits in forest trees.
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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".