Epigenetic then genetic variations underpin rapid adaptation of oyster populations ( <i>Crassostrea gigas</i> ) to Pacific Oyster Mortality Syndrome (POMS)
Bibliographic record
Abstract
Abstract Disease emergence is accelerating in response to human activity-induced global changes. Understanding the mechanisms by which host populations can rapidly adapt to this threat will be crucial for developing future management practices. Pacific Oyster Mortality Syndrome (POMS) imposes a substantial and recurrent selective pressure on oyster populations ( Crassostrea gigas) . Rapid adaptation to this disease may arise through both genetic and epigenetic mechanisms. In this study, we used a combination of whole exome capture of bisulfite-converted DNA, next-generation sequencing, and (epi)genome-wide association mapping, to show that natural oyster populations differentially exposed to POMS displayed signatures of selection both in their genome (single nucleotide polymorphisms) and epigenome (CG-context DNA methylation). Consistent with higher resistance to POMS, the genes targeted by genetic and epigenetic variations were mainly related to host immunity. By combining correlation analyses, DNA methylation quantitative trait loci, and variance partitioning, we revealed that a third of the observed phenotypic variation was explained by interactions between the genetic sequence and epigenetic information, ∼14% by the genetic sequence, and up to 25% by the epigenome alone. Thus, as well as genetic adaptation, epigenetic mechanisms governing immune responses contribute significantly to the rapid adaptation of hosts to emerging infectious diseases.
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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 | 0.000 | 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".