Evidence for elevated mutation rates in low-quality genotypes
Bibliographic record
Abstract
The deleterious mutation rate plays a key role in a number of important topics in biology, from mating system evolution to human health. Despite this broad significance, the nature and causes of variation in mutation rate are poorly understood, especially in multicellular organisms. We test whether genetic quality, the presence or absence of deleterious alleles, affects the mutation rate in Drosophila melanogaster by using a modified mutation accumulation approach. We find evidence that genotypes constructed to carry deleterious "treatment" alleles on one chromosome during mutation accumulation experience an elevated mutation rate on a different chromosome. Further, this elevation is correlated with the effect of the treatment alleles on phenotypic condition, measured as body mass. Treatment alleles that reduce mass by 10% cause a doubling in the rate of mutational decline. Our results show that mutation rates are sensitive to genetic stress, such that individuals with low-quality genotypes will produce offspring of even lower genetic quality, in a mutational positive feedback loop. This type of variation in mutation rate is expected to alter a variety of predictions based on mutation load theory and accelerate adaptation to new environments. Positive mutational feedback could affect human health by increasing the rate of germline mutation, and possibly somatic mutation, in individuals of poor health because of genetic or environmental stress.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".