The sex-limited effects of mutations in the EGFR and TGF-β signaling pathways on shape and size sexual dimorphism and allometry in the <i>Drosophila</i> wing
Bibliographic record
Abstract
Abstract Much of the morphological diversity in nature-including among sexes within a species-is a direct consequence of variation in size and shape. However, disentangling variation in sexual dimorphism for both shape (SShD), size (SSD) and their relationship with one another remains complex. Understanding how genetic variation influences both size and shape together, and how this in turn influences SSD and SShD is challenging. In this study we utilize Drosophila wing size and shape as a model system to investigate how mutations influence size and shape as modulated by sex. Previous work has demonstrated that mutations in Epidermal Growth Factor Receptor (EGFR) and Transforming Growth Factor - ß (TGF-ß) signaling components can influence both wing size and shape. In this study we re-analyze this data to specifically address how they impact the relationship between size and shape in a sex-specific manner, in turn altering the pattern of sexual dimorphism. While most mutations influence shape overall, only a subset have a genotypic specific effect that influences SShD. Furthermore, while we observe sex-specific patterns of allometric shape variation, the effects of most mutations on allometry tend to be small. We discuss this within the context of using mutational analysis to understand sexual size and shape dimorphism.
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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) | 0.002 | 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".