The Accumulation of Deleterious Mutations as a Consequence of Domestication and Improvement in Sunflowers and Other Compositae Crops
Bibliographic record
Abstract
For populations to maintain optimal fitness, harmful mutations must be efficiently purged from the genome. Yet, under circumstances that diminish the effectiveness of natural selection, such as the process of plant and animal domestication, deleterious mutations are predicted to accumulate. Here, we compared the load of deleterious mutations in 21 accessions from natural populations and 19 domesticated accessions of the common sunflower using whole-transcriptome single nucleotide polymorphism data. Although we find that genetic diversity has been greatly reduced during domestication, the remaining mutations were disproportionally biased toward nonsynonymous substitutions. Bioinformatically predicted deleterious mutations affecting protein function were especially strongly over-represented. We also identify similar patterns in two other domesticated species of the sunflower family (globe artichoke and cardoon), indicating that this phenomenon is not due to idiosyncrasies of sunflower domestication or the sunflower genome. Finally, we provide unequivocal evidence that deleterious mutations accumulate in low recombining regions of the genome, due to the reduced efficacy of purifying selection. These results represent a conundrum for crop improvement efforts. Although the elimination of harmful mutations should be a long-term goal of plant and animal breeding programs, it will be difficult to weed them out because of limited recombination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".