The genetics of pubertal timing in the general population
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article overviews advances in the genetics of puberty based on studies in the general population, describes evidence for sex-specific genetic effects on pubertal timing, and briefly reviews possible mechanisms mediating sexually dimorphic genetic effects. RECENT FINDINGS: Pubertal timing is highly polygenic, and many loci are conserved among ethnicities. A number of identified loci underlie both pubertal timing and related traits such as height and BMI. It is increasingly apparent that understanding the factors modulating the onset of puberty is important because the timing of this developmental stage is associated with a wider range of adult health outcomes than previously appreciated. Although most of the genetic effects underlying the timing of puberty are common between boys and girls, some effects show sex-specificity and many are epigenetically modulated. Several potential mechanisms, including hormone-independent ones, may be responsible for observed sex differences. SUMMARY: Studies of pubertal timing in the general population have provided new knowledge about the genetic architecture of this complex trait. Increasing attention paid to sex-specific effects may provide key insights into the sexual dimorphism in pubertal timing and even into the associations between puberty and adult health risks by identifying common underlying biological pathways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".