Admixture influences the genetic architecture of DNA methylation in a wild primate hybrid zone
Bibliographic record
Abstract
Abstract Background Hybrid zones play a central role in evolutionary biology because they serve as natural laboratories for studying how traits and taxa diverge. Changes in gene regulation make important contributions to this process. However, the degree to which admixture shapes gene regulatory variation in hybrid populations remains poorly understood. Here, we combine genome-wide resequencing and DNA methylation data from 295 hybrid baboons—members of a single, intensively studied natural population—to investigate how admixture affects the genetic architecture of this important epigenetic mark. Results We find that local genetic ancestry frequently predicts DNA methylation levels and recapitulates differences between the parent species. By performing methylation quantitative trait locus (meQTL) mapping, we show that these differences predominantly arise due to evolved differences in allele frequencies. Thus, admixture in the hybrid population increases variance in DNA methylation, including by introducing meQTL that would otherwise be invariant. Finally, we integrate massively parallel reporter assay data to show that admixture-derived variation in DNA methylation alters enhancer activity and gene expression. Conclusions Together, these results demonstrate how admixture can meaningfully alter the genetic architecture of gene regulatory traits in natural hybrid zones. They also suggest that the genetic architecture of DNA methylation is conserved across closely related primates, suggesting that genetic effects on gene regulation may remain stable over timescales that range into the millions of years.
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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) | 0.002 | 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".