Spontaneous Mutation Accumulation in<i>Daphnia pulex</i>in Selection-Free vs. Competitive Environments
Bibliographic record
Abstract
Understanding the rates, spectra, and fitness effects of spontaneous mutations is fundamental to answering key questions in evolution, molecular biology, disease genetics, and conservation biology. To estimate mutation rates and evaluate the effect of selection on new mutations, we propagated mutation accumulation (MA) lines of Daphnia pulex for more than 82 generations and maintained a non-MA population under conditions where selection could act. Both experiments were started with the same obligate asexual progenitor clone. By sequencing 30 genomes and implementing a series of validation steps that informed the bioinformatic analyses, we identified a total of 477 single nucleotide mutations (SNMs) in the MA lines, corresponding to a mutation rate of 2.30 × 10−9 (95% CI 1.90–2.70 × 10−9) per nucleotide per generation. The high overall rate of loss of heterozygosity (LOH) of 4.82 × 10−5 per site per generation was due to a large ameiotic recombination event spanning an entire arm of a chromosome (∼6 Mb) and several hemizygous deletion events spanning ∼2 kb each. In the non-MA population, we found significantly fewer mutations than expected based on the rate derived from the MA experiment, indicating purifying selection was likely acting to remove new deleterious mutations. We observed a surprisingly high level of genetic variability in the non-MA population, which we propose to be driven by balancing selection. Our findings suggest that both positive and negative selection on new mutations is powerful and effective in a strictly clonal population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".