Mapping the genomic architecture of ecological speciation in the wild: does linkage disequilibrium hold the key?
Bibliographic record
Abstract
The hunt for the genes underlying ecological speciation has now closed in on a number of candidates, but making the link from genotype to phenotype continues to pose a significant challenge. This is partly because genetic studies in many systems remain impeded by long generation times or an inability to perform controlled crosses. Now, in this issue of Molecular Ecology, Malek et al. (2012) demonstrate the utility of a novel admixture mapping approach that can be used to identify genomic regions contributing to adaptive trait divergence between natural populations. Remarkably, they validate their approach by mapping traits associated with mate choice in a wild limnetic and benthic threespine stickleback (Gasterosteus aculeatus) species pair, finding several loci associated with male nuptial coloration and shape. While this study benefited from tried-and-true microsatellites in a well-characterized species with a detailed genetic map (and genome sequence), the field is quickly moving towards the use of next-generation sequencing, especially for nonmodel systems. The ability to characterize molecular polymorphisms for any system suggests that molecular ecologists working on virtually any species may benefit from applying Malek et al.'s approach, if naturally admixed populations are available.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".