Data from: Disentangling the roles of history and local selection in shaping clinal variation of allele frequencies and gene expression in Norway spruce (Picea abies)
Bibliographic record
Abstract
Understanding the genetic basis of local adaptation is challenging due to the subtle balance among conflicting evolutionary forces that are involved in its establishment and maintenance. One system with which to tease apart these difficulties are clines in adaptive characters. Here we analyzed genetic and phenotypic variation in bud set, a highly heritable and adaptive trait, among 18 populations of Norway spruce (Picea abies), arrayed along a latitudinal gradient ranging from 47°N to 68°N. We confirmed that variation in bud set is strongly clinal using a subset of five populations. Genotypes for 137 single nucleotide polymorphisms (SNPs) chosen from 18 candidate genes putatively affecting bud set, and 308 control SNPs chosen from 264 random genes, were analyzed for patterns of genetic structure and correlation to environment. Population genetic structure was low (FST = 0.05), but latitudinal patterns were apparent among Scandinavian populations. Hence, part of the observed clinal variation should be attributable to population demography. Conditional on patterns of genetic structure, there was enrichment of SNPs within candidate genes for correlations with latitude. Twenty-nine SNPs were also outliers with respect to FST. The enrichment for clinal variation at SNPs within candidate genes (i.e. SNPs in PaGI, PaPhyP, PaPhyN, PaPRR7 and PaFTL2) indicated that local selection in the 18 populations, and/or selection in the ancestral populations from which they were recently derived, shaped the observed cline. Validation of these genes using expression studies also revealed that PaFTL2 expression is significantly associated with latitude, thereby confirming the central role played by this gene in the control of phenology in plants.
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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 | 0.001 |
| 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) | 0.001 | 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".