The role of genomic vs. epigenomic variation in shaping patterns of convergent transcriptomic variation across continents in a young species complex
Bibliographic record
Abstract
ABSTRACT Repeated adaptive divergence in replicates of phenotypic diversification offers a propitious context to identify the molecular bases associated to adaptive divergence. A currently hotly debated topic pertains to the relative role of genomic vs. epigenomic variation in shaping patterns of phenotypic variation at the gene expression level. Here, we combined genomic, epigenomic and transcriptomic information from 64 individuals in order to quantify the relative role of SNPs and DNA methylation variation in the repeated evolution of four limnetic-benthic whitefish species pairs from Europe and North America. We first found evidence for 149 convergent differentially methylated regions (DMRs) between species across continents, which significantly influenced levels of gene expression. Hyper-methylated DMRs in the limnetic species were globally associated to an expression repression relatively to benthic species, and inversely. Furthermore, we identified 108 convergent genetic variants (eQTLs) associated to gene expression differences between species. Gene expression differences were more pronounced in genes harbouring eQTL compared to those associated with DMRs, thus revealing a greater effect of eQTLs on gene expression. Multivariate analyses allowed partitioning the relative contribution of epi-/genomic changes and their association to gene expression variation. Most of the gene expression variation was significantly explained by genomic (4.1%) and putatively genomic-epigenomic interactive variation (46.7%), while “pure” epigenomic variation explained marginally 2.3% of the gene expression variation across continents. This study provides a rare qualitative and quantitative documentation of the relative role of genomic, DNA methylation and their interaction in shaping patterns of convergent gene expression during the process of ecological speciation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".