Gene expression divergence and nucleotide differentiation between males of different color morphs and mating strategies in the ruff
Bibliographic record
Abstract
By next generation transcriptome sequencing, it is possible to obtain data on both nucleotide sequence variation and gene expression. We have used this approach (RNA-Seq) to investigate the genetic basis for differences in plumage coloration and mating strategies in a non-model bird species, the ruff (Philomachus pugnax). Ruff males show enormous variation in the coloration of ornamental feathers, used for individual recognition. This polymorphism is linked to reproductive strategies, with dark males (Independents) defending territories on leks against other Independents, whereas white morphs (Satellites) co-occupy Independent's courts without agonistic interactions. Previous work found a strong genetic component for mating strategy, but the genes involved were not identified. We present feather transcriptome data of more than 6,000 de-novo sequenced ruff genes (although with limited coverage for many of them). None of the identified genes showed significant expression divergence between males, but many genetic markers showed nucleotide differentiation between different color morphs and mating strategies. These include several feather keratin genes, splicing factors, and the Xg blood-group gene. Many of the genes with significant genetic structure between mating strategies have not yet been annotated and their functions remain to be elucidated. We also conducted in-depth investigations of 28 pre-identified coloration candidate genes. Two of these (EDNRB and TYR) were specifically expressed in black- and rust-colored males, respectively. We have demonstrated the utility of next generation transcriptome sequencing for identifying and genotyping large number of genetic markers in a non-model species without previous genomic resources, and highlight the potential of this approach for addressing the genetic basis of ecologically important variation.
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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 | 0.000 | 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) | 0.001 | 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".