“Balancing” balancing selection? Assortative mating at the major histocompatibility complex despite molecular signatures of balancing selection
Bibliographic record
Abstract
Abstract In vertebrate animals, genes of the major histocompatibility complex (MHC) determine the set of pathogens to which an individual's adaptive immune system can respond. MHC genes are extraordinarily polymorphic, often showing elevated nonsynonymous relative to synonymous sequence variation and sharing presumably ancient polymorphisms between lineages. These patterns likely reflect pathogen‐mediated balancing selection, for example, rare‐allele or heterozygote advantage. Such selection is often reinforced by disassortative mating at MHC. We characterized exon 2 of MHC class II, corresponding to the hypervariable peptide‐binding region, in song sparrows (Melospiza melodia). We compared nonsynonymous to synonymous sequence variation in order to identify positively selected sites; assessed evidence for trans‐species polymorphisms indicating ancient balancing selection; and compared MHC similarity of socially mated pairs to expectations under random mating. Six codons showed elevated ratios of nonsynonymous to synonymous variation, consistent with balancing selection, and we characterized several alleles similar to those occurring in at least four other avian families. Despite this evidence for historical balancing selection, mated pairs were significantly more similar at MHC than were randomly generated pairings. Nonrandom mating at MHC thus appears to partially counteract, not reinforce, pathogen‐mediated balancing selection in this system. We suggest that in systems where individual fitness does not increase monotonically with MHC diversity, assortative mating may help to avoid excessive offspring heterozygosity that could otherwise arise from long‐standing balancing 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 | 0.000 |
| 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 | 0.001 |
| 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".