SNP signatures of selection on standing genetic variation and their association with adaptive phenotypes along gradients of ecological speciation in lake whitefish species pairs (Coregonus spp.)
Bibliographic record
Abstract
As populations adapt to novel environments, divergent selection will promote heterogeneous genomic differentiation via reductions in gene flow for loci underlying adaptive traits. Using a data set of over 100 SNP markers, genome scans were performed to investigate the effect of natural selection maintaining differentiation in five lakes harbouring sympatric pairs of normal and dwarf lake whitefish (Coregonus clupeaformis). A variable proportion of SNPs (between 0% and 12%) was identified as outliers, which corroborated the predicted intensity of competitive interactions unique to each lake. Moreover, strong reduction in heterozygosity was typically observed for outlier loci in dwarf but not in normal whitefish, indicating that directional selection has been acting on standing genetic variation more intensively in dwarf whitefish. SNP associations in backcross hybrid progeny identified 16 genes exhibiting genotype-phenotype associations for four adaptive traits (growth, swimming activity, gill rakers and condition factor). However, neither simple relationship between elevated levels of genetic differentiation with adaptive phenotype nor conspicuous genetic signatures for parallelism at outlier loci were detected, which underscores the importance of independent evolution among lakes. The integration of phenotypic, transcriptomic and functional genomic information identified two candidate genes (sodium potassium ATPase and triosephosphate isomerase) involved in the recent ecological divergence of lake whitefish. Finally, the identification of several markers under divergent selection suggests that many genes, in an environment-specific manner, are recruited by selection and ultimately contributed to the repeated ecological speciation of a dwarf phenotype.
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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 | 0.001 |
| 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".