MHC studies in nonmodel vertebrates: what have we learned about natural selection in 15 years?
Bibliographic record
Abstract
Elucidating how natural selection promotes local adaptation in interaction with migration, genetic drift and mutation is a central aim of evolutionary biology. While several conceptual and practical limitations are still restraining our ability to study these processes at the DNA level, genes of the major histocompatibility complex (MHC) offer several assets that make them unique candidates for this purpose. Yet, it is unclear what general conclusions can be drawn after 15 years of empirical research that documented MHC diversity in the wild. The general objective of this review is to complement earlier literature syntheses on this topic by focusing on MHC studies other than humans and mice. This review first revealed a strong taxonomic bias, whereby many more studies of MHC diversity in natural populations have dealt with mammals than all other vertebrate classes combined. Secondly, it confirmed that positive selection has a determinant role in shaping patterns of nucleotide diversity in MHC genes in all vertebrates studied. Yet, future tests of positive selection would greatly benefit from making better use of the increasing number of models potentially offering more statistical rigour and higher resolution in detecting the effect and form of selection. Thirdly, studies that compared patterns of MHC diversity within and among natural populations with neutral expectations have reported higher population differentiation at MHC than expected either under neutrality or simple models of balancing selection. Fourthly, several studies showed that MHC-dependent mate preference and kin recognition may provide selective factors maintaining polymorphism in wild outbred populations. However, they also showed that such reproductive mechanisms are complex and context-based. Fifthly, several studies provided evidence that MHC may significantly influence fitness, either by affecting reproductive success or progeny survival to pathogens infections. Overall, the evidence is compelling that the MHC currently represents the best system available in vertebrates to investigate how natural selection can promote local adaptation at the gene level despite the counteracting actions of migration and genetic drift. We conclude this review by proposing several directions where future research is needed.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".