Deep learning reveals the complex genetic architecture of a highly polymorphic sexual trait
Bibliographic record
Abstract
The extraordinary variation in male guppy coloration has proven a powerful model for studying the interplay of natural and sexual selection. However, this variation has hampered the high-resolution characterization and determination of the genetic architecture underlying male guppy color, as well as clouded our understanding of how this exceptional level of diversity is maintained. Here we identify the heritability and genetic basis of male color variation using convolutional neural networks for high-resolution phenotyping coupled with selection experiments, controlled pedigrees and whole-genome resequencing for a Genome Wide Association Study (GWAS) of color. Our phenotypic and genomic results converge to show that color patterning in guppies is a combination of many heritable features, each with a largely independent genetic architecture spanning the entire genome. Autosomally-inherited ornaments are polygenic, with significant contributions from loci involved in neural crest cell migration. Unusually, our GWAS results suggest that gene duplicates from the autosomes to the Y chromosome are responsible for much of the sex-linked variation in color in guppies, providing a potential mechanism for the maintenance of variation of this classic model trait.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".