Targeted sequence capture and resequencing implies a predominant role of regulatory regions in the divergence of a sympatric lake whitefish species pair (<i><scp>C</scp>oregonus clupeaformis</i>)
Bibliographic record
Abstract
Latest technological developments in evolutionary biology bring new challenges in documenting the intricate genetic architecture of species in the process of divergence. Sympatric populations of lake whitefish represent one of the key systems to investigate this issue. Despite the value of random genotype-by-sequencing methods and decreasing cost of sequencing technologies, it remains challenging to investigate variation in coding regions, especially in the case of recently duplicated genomes as in salmonids, as this greatly complicates whole genome resequencing. We thus designed a sequence capture array targeting 2773 annotated genes to document the nature and the extent of genomic divergence between sympatric dwarf and normal whitefish. Among the 2728 genes successfully captured, a total of 2182 coding and 10,415 noncoding putative single-nucleotide polymorphisms (SNPs) were identified after applying a first set of basic filters. A genome scan with a quality-refined selection of 2203 SNPs identified 267 outlier SNPs in 210 candidate genes located in genomic regions potentially involved in whitefish divergence and reproductive isolation. We found highly heterogeneous FST estimates among SNP loci. There was an overall low level of coding polymorphism, with a predominance of noncoding mutations among outliers. The heterogeneous patterns of divergence among loci confirm the porous nature of genomes during speciation with gene flow. Considering that few protein-coding mutations were identified as highly divergent, our results, along with previous transcriptomic studies, imply that changes in regulatory regions most likely had a greater role in the process of whitefish population divergence than protein-coding mutations. This study is the first to demonstrate the efficiency of large-scale targeted resequencing for a nonmodel species with such a large and unsequenced genome.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".