<i>Cis</i>‐regulatory evolution in a wild primate: Infection‐associated genetic variation drives differential expression of <i><scp>MHC</scp>‐<scp>DQA</scp>1</i> in vitro
Bibliographic record
Abstract
Few studies have combined genetic association analyses with functional characterization of infection-associated SNPs in natural populations of nonhuman primates. Here, we investigate the relationship between host genetic variation, parasitism and natural selection in a population of red colobus (Procolobus rufomitratus tephrosceles) in Kibale National Park, Uganda. We collected parasitological, cellular and genomic data to test the following hypotheses: (i) MHC-DQA1 regulatory genetic variation is associated with control of whipworm (Trichuris) infection in a natural population of red colobus; (ii) infection-associated SNPs are functional in driving differential gene expression in vitro; and (iii) balancing selection has shaped patterns of variation in the MHC-DQA1 promoter. We identified two SNPs in the MHC-DQA1 promoter, both in transcription factor binding sites, and both of which are associated with decreased control of Trichuris infection. We characterized the function of both SNPs by testing differences in gene expression between the two alleles of each SNP in two mammalian cell lines. Alleles of one of the SNPs drove differential gene expression in both cell lines, while the other SNP drove differences in expression in one of the cell lines. Additionally, we found evidence of balancing selection acting on the MHC-DQA1 promoter, including extensive trans-species polymorphisms between red colobus and other primates, and an excess of intermediate-frequency alleles relative to genome-wide, coding and noncoding RADseq data. Our data suggest that balancing selection provides adaptive regulatory flexibility that outweighs the consequences of increased parasite infection intensity in heterozygotes.
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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 | 0.000 |
| 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".