Development Shapes a Consistent Inbreeding Effect in Mouse Crania of Different Line Crosses
Bibliographic record
Abstract
Development translates genetic variation into a multivariate pattern of phenotypic variation, distributing it among traits in a nonuniform manner. As developmental processes are largely shared within species, this suggests that heritable phenotypic variation will be patterned similarly, in spite of the different segregating alleles. To investigate developmental effect on the variational pattern in the shape of the mouse skull across genetically differentiated lines, we employed the full set of reciprocal crosses (a.k.a. diallel) between eight inbred mouse strains of the Collaborative Cross Project. We used geometric morphometrics and multivariate analysis to capture cranial size and shape changes in 8 parentals and their 54 F1 crosses. The high heterozygosity generated in the F1 crosses allowed us to compare the multivariate deviations of the F1 phenotypes from the expected midparental phenotypes in different haplotype combinations. In contrast to body weight, we found a high degree of nonadditive deviation in craniofacial shape. Whereas the phenotypic and genetic divergence of parental strains manifested in high dimensionality of additive effects, the nonadditive deviations exhibited lesser dimensionality and in particular a strikingly coherent direction in shape space. We interpret this finding as evidence for a strong structuring effect of a relatively small set of developmental processes on the mapping of genetic to phenotypic variation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".