Integration and the genetics of variation in facial shape
Bibliographic record
Abstract
Human facial form is both highly variable and heritable. Despite considerable effort, however, the genetic basis for the strong and often very particular patterns of resemblance among relatives are largely unknown. The apparently highly polygenic basis for facial shape variation appears to be at odds with the strong and often highly specific patterns of heritability for facial features. Facial shape variation tends to be structured around developmentally determined patterns of correlated variation. This tendency for covariation or integration results in morphological variation that occupies only a fraction of it possible dimensions. Synthesizing results from experimental and quantitative genetics of the face and applying multivariate genotype‐phenotype mapping, we argue that the integrated nature of variation in facial form can simplify qualitative explanations of the genetics of face but greatly complicate the ability to quantitatively relate genetic to phenotypic variation. Acting on variation in development, many genetic variants push or pull variation along the same limited number of dimensions that correspond to patterns of covariation among aspects of craniofacial variation, producing complex and overlapping patterns of pleiotropic effects. Developmental nonlinearities and interactions among processes at multiple levels result in gene interaction effects that can result in specific genetic variants having genetic background specific effects whilst having small and difficult to detect effects on variation at the population level. These findings have important general implications for the genetics of complex traits. Support or Funding Information CIHR Foundation Grant, NSERC #238992‐17, NIH R01DE019638; U01DE024440 This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".