Inbreeding Changes the Shape of the Genetic Covariance Matrix in<i>Drosophila melanogaster</i>
Bibliographic record
Abstract
The pattern of genetic covariation among traits (the G matrix) plays a central role in determining the pattern of evolutionary change from both natural selection and random genetic drift. Here we measure the effect of genetic drift on the shape of the G matrix using a large data set on the inheritance of wing characteristics in Drosophila melanogaster. Fifty-two inbred lines with a total of 4680 parent-offspring families were generated by one generation of brother-sister mating and compared to an outbred control population of 1945 families. In keeping with the theoretical expectation for a correlated set of additively determined traits, the average G matrix of the inbred lines remained proportional to the outbred control G matrix with a proportionality constant approximately equal to (1 - F), where F is the inbreeding coefficient. Further, the pattern of covariance among the means of the inbred lines induced by inbreeding was also proportional to the within-line G matrix of the control population with a constant very close to the expectation of 2F. Although the average G of the inbred lines did not show change in overall structure relative to the outbred controls, separate analysis revealed a great deal of variation among inbred lines around this expectation, including changes in the sign of genetic correlations. Since any given line can be quite different from the outbred control, it is likely that in nature unreplicated drift will lead to changes in the G matrix. Thus, the shape of G is malleable under genetic drift, and the evolutionary response of any particular population is likely to depend on the specifics of its evolutionary history.
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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 | 0.000 | 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) | 0.000 | 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".