Spontaneous mutations and transmission distortions of genic copy number variants shape the standing genetic variation in <i>Picea glauca</i>
Bibliographic record
Abstract
Abstract Copy number variations (CNVs) are large genetic variations detected among the individuals of every multicellular organism examined so far. These variations are believed to play an important role in the evolution and adaptation of species. In plants, little is known about the characteristics of CNVs, particularly regarding the rates at which they are generated and the mechanics of their transmission from a generation to the next. Here, we used SNP-array raw intensity data for 55 two-generations families (3663 individuals) to scan the gene space of the conifer tree Picea glauca (Moench) Voss for CNVs. We were particularly interested in the abundance, inheritance, spontaneous mutation rate spectrum and the evolutionary consequences they may have on the standing genetic variation of white spruce. Our findings show that CNVs affect a small proportion of the gene space and are predominantly copy number losses. CNVs were either inherited or generated through de novo events. De novo CNVs present high rates of spontaneous mutations that vary for different genes and alleles and are correlated with gene expression levels. Most of the inherited CNVs (70%) are transmitted from the parents in violation of Mendelian expectations. These transmission distortions can cause considerable frequency changes between generations and be dependent on whether the heterozygote parents contribute as male or female. Transmission distortions were also influenced by the partner genotype and the parents’ genetic background. This study provides new insights into the effects of different evolutionary forces on copy number variations based on the analysis of a perennial plant.
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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 | 0.000 |
| 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".