Quantitative variation in Drosophila melanogaster wing shape and size
Bibliographic record
Abstract
Several studies examining the genetics of adaptation have identified single alleles, of large phenotypic e ect, contributing to divergence between populations. This empirical finding is consistent with predictions made by the geometric model of adaptation, where a small number of alleles of large e ect and many alleles of small e ect are fixed as the population adapts. However, these examples of single genes of large e ect may represent a biased sample of the alleles of adaptation with polygenic allele shifts having a greater contribution than currently understood. Increasing power to detect smaller e ect variants, due to falling sequencing costs and improved statistical methods, has made the contribution of small allele frequency shifts at many loci, or polygenic adaptation, more apparent. In contrast to models predicting single genes of large e ect with large allele frequency changes, polygenic adaptation allows for small allele frequency changes across many alleles of small e ect to contribute to phenotypic change. Using artificial selection, I demonstrate the alignment of genetic e ects contributing to wing shape variation within a developmental pathway but a lack of replication of these same genetic e ects in other wild-caught populations. Secondly, using advanced intercross QTL mapping between altitudinally diverged populations, I demonstrate a polygenic basis for wing shape and size variation. Finally, using comparative developmental biology I investigate how change to cell size and number in the wing may contribute to divergence between high and low altitude populations. Together, this work provides evidence for many alleles of small e ect rather than alleles of large e ect contributing to adaptive divergence of wing shape and size and provides context for identified alleles through replication in other populations and comparative developmental biology.
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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 | 0.001 | 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".