Unraveling genetic variation among white spruce families generated through different breeding strategies: Heritability, growth, physiology, hormones and gene expression
Bibliographic record
Abstract
Tree improvement programs select genotypes for faster growth, at both early and late stages, to increase yields over unimproved material, and the improvement is frequently attributed to genetic control in growth parameters among genotypes. Underutilized genetic variability among genotypes also has the potential to ensure future gains are possible. However, the genetic variation in growth, physiology and hormone control among genotypes generated from different breeding strategies has not been well characterized in conifers. We assessed growth, biomass, gas exchange, gene expression and hormone levels in white spruce seedlings obtained from three different breeding strategies (controlled crosses, polymix pollination, open pollination) using parents grafted into a clonal seed orchard in Alberta, Canada. A pedigree-based best linear unbiased prediction (ABLUP) mixed model was implemented to quantify variability and narrow-sense heritability for target traits. The levels of several hormones and expression of gibberellin-related genes in apical internodes were also determined. Over the first two years of development, the estimated heritabilities for height, volume, total dry biomass, above ground dry biomass, root:shoot ratio and root length, varied between 0.10 and 0.21, with height having the highest value. The ABLUP values showed large genetic variability in growth and physiology traits both between families from different breeding strategies, and within families. The principal component analysis showed that developmental and hormonal traits explained 44.2% and 29.4% of the total phenotypic variation between the three different breeding strategies and two growth groups. In general, controlled crosses from the fast growth group showed the best apical growth, with more accumulation of indole-3-acetic acid, abscisic acid, phaseic acid, and a 4-fold greater gene expression of PgGA3ox1 in genotypes from controlled crosses versus those from open pollination. However, in some cases, open pollination from the fast and slow growth groups showed the best root development, higher water use efficiency (iWUE and δ13C) and more accumulation of zeatin and isopentenyladenosine. In conclusion, tree domestication can lead to trade-offs between growth, carbon allocation, photosynthesis, hormone levels and gene expression, and we encourage the use of this phenotypic variation identified in improved and unimproved trees to advance white spruce tree improvement programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".