Using genomics for birth defects epidemiology: Can epigenetics cut the GxE gordian knot?
Bibliographic record
Abstract
Most birth defects are etiologically complex disorders caused by combinations of genetic and environmental factors, but most studies of birth defect etiology have examined only genetic factors or only environmental factors and have not considered interactions among them. Genome-wide epigenetic studies, which use the same genomic technologies that have revolutionized our ability to identify genetic causes of disease, provide an attractive way to study gene-environment interactions. However, finding an association between epigenetic variation and an etiologically complex birth defect without knowledge of the genetic variation and environmental exposures affecting the individuals who were studied usually provides little or no information regarding the cause of the disorder. In order for genome-wide studies of epigenetic variation to contribute to our understanding of the causes of birth defects, these studies must be combined with studies of environmental exposures and studies of genetic variation in the same subjects. Under such circumstances, epigenetic studies may help to establish the molecular basis for gene-environment interactions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".