Admixture mapping in a hybrid zone reveals loci associated with avian feather coloration
Bibliographic record
Abstract
Identifying the genetic bases for colour patterns has provided important insights into the control and expression of pigmentation and how these characteristics influence fitness. However, much more is known about the genetic bases for traits based on melanin pigments than for traits based on another major class of pigments, carotenoids. Here, we use natural admixture in a hybrid zone between Audubon's and myrtle warblers ( Setophaga coronata auduboni / S. c. coronata ) to identify genomic regions associated with both types of pigmentation. Warblers are known for rapid speciation and dramatic differences in plumage. For each of five plumage coloration traits, we found highly significant associations with multiple single-nucleotide polymorphisms (SNPs) across the genome and these were clustered in discrete regions. Regions near significantly associated SNPs were enriched for genes associated with keratin filaments, fibrils that make up feathers. A carotenoid-based trait that differs between the taxa—throat colour—had more than a dozen genomic regions of association. One cluster of SNPs for this trait overlaps the Scavenger Receptor Class F Member 2 ( SCARF2 ) gene. Other scavenger receptors are presumed to be expressed at target tissues and involved in the selective movement of carotenoids into the target cells, making SCARF2 a plausible new candidate for carotenoid processing. In addition, two melanin-based plumage traits—colours of the eye line and eye spot—show very strong associations with a single genomic region mapping to chromosome 20 in the zebra finch. These findings indicate that only a subset of the genomic regions differentiated between these two warblers are associated with the plumage differences between them and demonstrate the utility of reduced-representation genomic scans in hybrid zones.
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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 | 0.001 |
| 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) | 0.001 | 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".