Genome-wide patterns of divergence during speciation: the lake whitefish case study
Bibliographic record
Abstract
The nature, size and distribution of the genomic regions underlying divergence and promoting reproductive isolation remain largely unknown. Here, we summarize ongoing efforts using young (12 000 yr BP) species pairs of lake whitefish (Coregonus clupeaformis) to expand our understanding of the initial genomic patterns of divergence observed during speciation. Our results confirmed the predictions that: (i) on average, phenotypic quantitative trait loci (pQTL) show higher F(ST) values and are more likely to be outliers (and therefore candidates for being targets of divergent selection) than non-pQTL markers; (ii) large islands of divergence rather than small independent regions under selection characterize the early stages of adaptive divergence of lake whitefish; and (iii) there is a general trend towards an increase in terms of numbers and size of genomic regions of divergence from the least (East L.) to the most differentiated species pair (Cliff L.). This is consistent with previous estimates of reproductive isolation between these species pairs being driven by the same selective forces responsible for environment specialization. Altogether, dwarf and normal whitefish species pairs represent a continuum of both morphological and genomic differentiation contributing to ecological speciation. Admittedly, much progress is still required to more finely map and circumscribe genomic islands of speciation. This will be achieved through the use of next generation sequencing data but also through a better quantification of phenotypic traits moulded by selection as organisms adapt to new environmental conditions.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".