Genome-Wide Analyses of Copy Number Variants in 751 <i>Populus trichocarpa</i> Individuals From Natural Populations
Bibliographic record
Abstract
Copy number variations (CNVs), including duplications and deletions of the genome ranging up to 1 Mb, are an important contributor to genomic variation, and may influence phenotypic variation. They are relatively understudied compared with single nucleotide polymorphisms despite affecting a higher proportion of the genome. Using whole genome sequencing data and RNA-sequencing data, we identified and characterized the natural diversity of CNVs across the native range of Populus trichocarpa and the effects of CNVs on gene expression. We analyzed whole genome sequencing data of 751 P. trichocarpa individuals to identify CNVs, analyzed their size, distribution and population structure. We also examined gene expression with RNA-sequencing data of leaf and xylem tissues for 390 individuals. We found 11,501 duplications and 22,839 deletions covering a major percentage of the genome. Genes overlapping with CNVs were enriched in important biological processes such as reproduction, cellulose production, and defense. Analysis of CNV genotypes with expression data showed that a minority of genes overlapping CNVs have a strong correlation of expression level with copy number. Those genes were significantly enriched in stress-related responses. Our identified CNVs provide insights into the extent, characteristics, and diversity of CNVs in wild populations of P. trichocarpa and the effects of CNVs on gene expression.
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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".