The genomic architecture and association genetics of adaptive characters using a candidate SNP approach in boreal black spruce
Bibliographic record
Abstract
BACKGROUND: The genomic architecture of adaptive traits remains poorly understood in non-model plants. Various approaches can be used to bridge this gap, including the mapping of quantitative trait loci (QTL) in pedigrees, and genetic association studies in non-structured populations. Here we present results on the genomic architecture of adaptive traits in black spruce, which is a widely distributed conifer of the North American boreal forest. As an alternative to the usual candidate gene approach, a candidate SNP approach was developed for association testing. RESULTS: A genetic map containing 231 gene loci was used to identify QTL that were related to budset timing and to tree height assessed over multiple years and sites. Twenty-two unique genomic regions were identified, including 20 that were related to budset timing and 6 that were related to tree height. From results of outlier detection and bulk segregant analysis for adaptive traits using DNA pool sequencing of 434 genes, 52 candidate SNPs were identified and subsequently tested in genetic association studies for budset timing and tree height assessed over multiple years and sites. A total of 34 (65%) SNPs were significantly associated with budset timing, or tree height, or both. Although the percentages of explained variance (PVE) by individual SNPs were small, several significant SNPs were shared between sites and among years. CONCLUSIONS: The sharing of genomic regions and significant SNPs between budset timing and tree height indicates pleiotropic effects. Significant QTLs and SNPs differed quite greatly among years, suggesting that different sets of genes for the same characters are involved at different stages in the tree's life history. The functional diversity of genes carrying significant SNPs and low observed PVE further indicated that a large number of polymorphisms are involved in adaptive genetic variation. Accordingly, for undomesticated species such as black spruce with natural populations of large effective size and low linkage disequilibrium, efficient marker systems that are predictive of adaptation should require the survey of large numbers of SNPs. Candidate SNP approaches like the one developed in the present study could contribute to reducing these numbers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".