Male Bias in Distributions of Additive Genetic, Residual, and Phenotypic Variances of Shared Traits
Bibliographic record
Abstract
Despite a shared genetic architecture between males and females, sexual differences are widespread. The extent of this shared genetic architecture, reflected in the intersexual genetic correlation, has previously been correlated with the extent of phenotypic sexual dimorphism in shared traits. However, the magnitude of the difference in sex-specific additive genetic variances may also fuel sexual dimorphism. To explore the correlation between additive genetic variance dimorphism and phenotypic dimorphism, we conducted a literature search. We targeted traits expressed in both sexes and excluded sex-limited traits. The mean difference between the sexes in additive genetic variance was not significantly different from 0. However, the distribution of the sexual difference in additive genetic variance had a significant male-biased skew. This pattern persists even after removing traits explicitly related to reproduction. Furthermore, male traits had more residual and phenotypic variance than homologous female traits (as measured by both the mean and the skew), and this difference was not necessarily due to the difference between sexual traits and nonsexual traits. We found no evidence that sex chromosome system could explain sex differences in additive genetic, nonadditive genetic, or phenotypic variances. Finally, we found a significant correlation between the extent of sexual dimorphism in additive genetic variances and the extent of phenotypic sexual dimorphism. Understanding why traits have sex-specific patterns of variation awaits further investigation.
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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) | 0.001 | 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 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".