Phenotype‐environment mismatch due to epigenetic inheritance? Programming the offspring's epigenome and the consequences of migration
Bibliographic record
Abstract
OBJECTIVE: It has been suggested that epigenetic inheritance is an important factor influencing mortality. We use data about the historical population of Québec (years 1670-1740) to study whether parents modify their offspring's phenotype epigenetically prior to conception in response to predicted/perceived mortality. If so, children growing up in the predicted environment enjoy a phenotype-environment-match that should lower mortality, whereas children growing up in a nonpredicted environment should have a higher mortality. METHODS: We use the large urban-rural mortality differential to capture the predicted/perceived mortality environment. We categorize children into different groups by their migration status: conceived and living in the same environment (urban or rural); conceived in one but born in another environment (urban-to-rural or rural-to-urban); and born in one but migrating to another environment. We use Kaplan-Meier survival curves and fixed effect survival models to estimate to what extent child survival up to the age of 15 depends on migration status. RESULTS: Child mortality within families that moved from urban to rural areas does not depend on the child's migration status. Within families that moved to urban areas, children who were conceived and born in the rural areas exhibit the lowest mortality. This contradicts a phenotype-environment-mismatch scenario, which would result in higher rather than lower mortality. CONCLUSION: We do not find evidence for functional (adaptive) epigenetic inheritance. Migration into an environment with lower or higher extrinsic mortality affects child mortality within the families differently than predicted by the concept of epigenetic inheritance. The results suggest that epigenetic inheritance may not be important for child mortality among migrants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".