Parental Age Affects Somatic Mutation Rates in the Progeny of Flowering Plants
Bibliographic record
Abstract
In humans, it is well known that the parental reproductive age has a strong influence on mutations transmitted to their progeny. Meiotic nondisjunction is known to increase in older mothers, and base substitutions tend to go up with paternal reproductive age. Hence, it is clear that the germinal mutation rates are a function of both maternal and paternal ages in humans. In contrast, it is unknown whether the parental reproductive age has an effect on somatic mutation rates in the progeny, because these are rare and difficult to detect. To address this question, we took advantage of the plant model system Arabidopsis (Arabidopsis thaliana), where mutation detector lines allow for an easy quantitation of somatic mutations, to test the effect of parental age on somatic mutation rates in the progeny. Although we found no significant effect of parental age on base substitutions, we found that frameshift mutations and transposition events increased in the progeny of older parents, an effect that is stronger through the maternal line. In contrast, intrachromosomal recombination events in the progeny decrease with the age of the parents in a parent-of-origin-dependent manner. Our results clearly show that parental reproductive age affects somatic mutation rates in the progeny and, thus, that some form of age-dependent information, which affects the frequency of double-strand breaks and possibly other processes involved in maintaining genome integrity, is transmitted through the gametes.
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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 | 0.000 | 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) | 0.002 | 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".