Ancestral reconstruction of sunflower karyotypes reveals non-random chromosomal evolution
Bibliographic record
Abstract
Abstract Mapping the chromosomal rearrangements between species can inform our understanding of genome evolution, reproductive isolation, and speciation. Here we present a novel algorithm for identifying regions of synteny in pairs of genetic maps, which is implemented in the accompanying R package, syntR. The syntR algorithm performs as well as previous ad-hoc methods while being systematic, repeatable, and is applicable to mapping chromosomal rearrangements in any group of species. In addition, we present a systematic survey of chromosomal rearrangements in the annual sunflowers, which is a group known for extreme karyotypic diversity. We build high-density genetic maps for two subspecies of the prairie sunflower , Helianthus petiolaris ssp. petiolaris and H. petiolaris ssp. fallax . Using syntR, and we identify blocks of synteny between these two subspecies and previously published high-density genetic maps. We reconstruct ancestral karyotypes for annual sunflowers using those synteny blocks and conservatively estimate that there have been 7.9 chromosomal rearrangements per million years – a high rate of chromosomal evolution. Although the rate of inversion is even higher than the rate of translocation in this group, we further find that every extant karyotype is distinguished by between 1 and 3 translocations involving only 8 of the 17 chromosomes. This non-random exchange suggests that specific chromosomes are prone to translocation and may thus contribute disproportionately to widespread hybrid sterility in sunflowers. These data deepen our understanding of chromosome evolution and confirm that Helianthus has an exceptional rate of chromosomal rearrangement that may facilitate similarly rapid diversification.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".