Detecting the signature of selection on immune genes in highly structured populations of wild sheep (<i>Ovis dalli</i>)
Bibliographic record
Abstract
The confounding effects of population structure complicate efforts to identify regions of the genome under the influence of selection in natural populations. Here we test for evidence of selection in three genes involved in vertebrate immune function - the major histocompatibility complex (MHC), interferon gamma (IFNG) and natural resistance associated macrophage polymorphism (NRAMP) - in highly structured populations of wild thinhorn sheep (Ovis dalli). We examined patterns of variation at microsatellite loci linked to these gene regions and at the DNA sequence level. Simple Watterson's tests indicated balancing selection at all three gene regions. However, evidence for selection was confounded by population structure, as the Watterson's test statistics from linked markers were not outside of the range of values from unlinked and presumably neutral microsatellites. The translated coding sequences of thinhorn IFNG and NRAMP are fixed and identical to those of domestic sheep (Ovis aries). In contrast, the thinhorn MHC DRB locus shows significant evidence of overdominance through both an excess of nonsynonymous substitution and trans-species polymorphism. The failure to detect balancing selection at microsatellite loci linked to the MHC is likely the result of recombination between the markers and expressed gene regions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".