Elevated Proportions of Deleterious Genetic Variation in Domestic Animals and Plants
Bibliographic record
Abstract
A fraction of genetic variants segregating in any population are deleterious, which negatively impacts individual fitness. The domestication of animals and plants is associated with population bottlenecks and artificial selection, which are predicted to increase the proportion of deleterious variants. However, the extent to which this is a general feature of domestic species is unclear. Here, we examine the effects of domestication on the prevalence of deleterious variation using pooled whole-genome resequencing data from five domestic animal species (dog, pig, rabbit, chicken, and silkworm) and two domestic plant species (rice and soybean) compared with their wild ancestors. We find significantly reduced genetic variation and increased proportion of nonsynonymous amino acid changes in all but one of the domestic species. These differences are observable across a range of allele frequencies, both common and rare. We find proportionally more single nucleotide polymorphisms in highly conserved elements in domestic species and a tendency for domestic species to harbor a higher proportion of changes classified as damaging. Our findings most likely reflect an increased incidence of deleterious variants in domestic species, which is most likely attributable to population bottlenecks that lead to a reduction in the efficacy of selection. An exception to this pattern is displayed by European domestic pigs, which do not show traces of a strong population bottleneck and probably continued to exchange genes with wild boar populations after domestication. The results presented here indicate that an elevated proportion of deleterious variants is a common, but not ubiquitous, feature of domestic species.
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) | 0.001 | 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".