Many loci make light work: High individual diversity despite low population diversity and random mating at class I MHC in a Critically Endangered island songbird
Bibliographic record
Abstract
Multi-cellular organisms are under constant attack from parasites, making immune defence a critical aspect of fitness. In vertebrate animals, genes of the major histocompatibility complex (MHC) determine the breadth of pathogens to which individuals can respond. Having many MHC alleles can confer better protection against infectious disease, and balancing selection at MHC is widespread. Indeed, MHC loci are famously variable, with some populations harbouring thousands of alleles (Biedrzycka et al., 2018; Robinson, Soormally, Hayhurst, & Marsh, 2016). MHC has also long fascinated behavioural ecologists because mate choice-for example, preferring MHC-dissimilar partners-may amplify the effects of natural selection (Penn & Potts, 1999). But despite keen interest in the evolutionary ecology of MHC, extensive duplication (Minias, Pikus, Whittingham, & Dunn, 2019) has made these genes challenging to study. In a From the Cover article in this issue of Molecular Ecology, Stervander, Dierickx, Thorley, Brooke, and Westerdahl (2020) characterizes class I MHC in a Critically Endangered songbird, relating genotype to mate choice and survivorship. By inferring copy number and patterns of allelic co-segregation, the authors pave the way to elucidating the genomic architecture of MHC in this bottlenecked population. These insights help reconcile apparently counterintuitive findings: no effect of MHC genotype on mate choice or survival, and high MHC diversity within individuals despite low diversity at the population level. The latter finding is cause for optimism regarding conservation prospects. Moreover, these results suggest that ancient duplication events can have longstanding effects on the adaptive landscapes of natural and sexual selection.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".