Low Genetic Quality Alters Key Dimensions of the Mutational Spectrum
Bibliographic record
Abstract
Mutations affect individual health, population persistence, adaptation, diversification, and genome evolution. There is evidence that the mutation rate varies among genotypes, but the causes of this variation are poorly understood. Here, we link differences in genetic quality with variation in spontaneous mutation in a Drosophila mutation accumulation experiment. We find that chromosomes maintained in low-quality genetic backgrounds experience a higher rate of indel mutation and a lower rate of gene conversion in a manner consistent with condition-based differences in the mechanisms used to repair DNA double strand breaks. These aspects of the mutational spectrum were also associated with body mass, suggesting that the effect of genetic quality on DNA repair was mediated by overall condition, and providing a mechanistic explanation for the differences in mutational fitness decline among these genotypes. The rate and spectrum of substitutions was unaffected by genetic quality, but we find variation in the probability of substitutions and indels with respect to several aspects of local sequence context, particularly GC content, with implications for models of molecular evolution and genome scans for signs of selection. Our finding that the chances of mutation depend on genetic context and overall condition has important implications for how sequences evolve, the risk of extinction, and human health.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".