Gene copy number variations in adaptive evolution: The genomic distribution of gene copy number variations revealed by genetic mapping and their adaptive role in an undomesticated species, white spruce (<i>Picea glauca</i>)
Bibliographic record
Abstract
Gene copy number variation (CNV) has been associated with phenotypic variability in animals and plants, but a genomewide understanding of their impacts on phenotypes is largely restricted to human and agricultural systems. As such, CNVs have rarely been considered in investigations of the genomic architecture of adaptation in wild species. Here, we report on the genetic mapping of gene CNVs in white spruce, which lacks a contiguous assembly of its large genome (~20 Gb), and their relationships with adaptive phenotypic variation. We detected 3,911 gene CNVs including de novo structural variations using comparative genome hybridization on arrays (aCGH) in a large progeny set. We inferred the heterozygosity at CNV loci within parents by comparing haploid and diploid tissues and genetically mapped 82 gene CNVs. Our analysis showed that CNVs were distributed over 10 linkage groups and identified four CNV hotspots that we predict to occur in other species of the Pinaceae. Significant relationships were found between 29 of the gene CNVs and adaptive traits based on regression analyses with timings of bud set and bud flush, and height growth, suggesting a role for CNVs in climate adaptation. The importance of CNVs in adaptive evolution of white spruce was also indicated by functional gene annotations and the clustering of 31% of the mapped adaptive gene CNVs in CNV hotspots. Taken together, these results illustrate the feasibility of studying CNVs in undomesticated species and represent a major step towards a better understanding of the roles of CNVs in adaptive evolution.
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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 | 0.001 | 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) | 0.001 | 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".