Estimation of population structure in coastal Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] using allozyme and microsatellite markers
Bibliographic record
Abstract
Characterizing population structure using neutral markers is an important first step in association genetic studies in order to avoid false associations between phenotypes and genotypes that may arise from non-selective demographic factors. Population structure was studied in a wide sample of ∼1,300 coastal Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] trees from Washington and Oregon. This sample is being used for association mapping between cold hardiness and phenology phenotypes and single-nucleotide polymorphisms in adaptive-trait candidate genes. All trees were genotyped for 25 allozyme and six simple sequence repeat (SSR) markers using individual megagametophytes. Population structure analysis was done separately for allozyme and SSR markers, as well as for both datasets combined. The parameter of genetic differentiation (θ or F ST) was standardized to take into account high within-population variation in the SSR loci and to allow comparison with allozyme loci. Genetic distance between populations was positively and significantly correlated with geographic distance, and weak but significant clinal variation was found for a few alleles. Although the STRUCTURE simulation analysis inferred the same number of populations as used in this study and as based on previous analysis of quantitative adaptive trait variation, clustering among populations was not significant. In general, results indicated weak differentiation among populations for both allozyme and SSR loci (θ s = 0.006–0.059). The lack of pronounced population subdivision in the studied area should facilitate association mapping in this experimental population, but we recommend taking the STRUCTURE analysis and population assignments for individual trees (Q-matrix) into account in association mapping.
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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 | 0.001 |
| 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) | 0.000 | 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".