Copy number variation and population-specific immune genes in the model vertebrate zebrafish
Bibliographic record
Abstract
Many species have hundreds of immune genes from the NLR family (Nucleotide-binding domain Leucine-rich Repeat containing). In plants they have a considerable amount of within-species variation, but not much is known about their variability in fishes. Here we captured and analysed the diversity of NLRs in zebrafish ( Danio rerio ) by sequencing 93 individuals from four wild and two laboratory strains. We found 1,560 unique NLR genes, and theoretical modelling revealed each wild population to have around 2,000. Only 100-550 were detected in each individual fish, and the observed variance of copy numbers differed among populations. Laboratory strains were found to have three times less NLRs than wild populations, and their genetic diversity was lower in general. Many NLRs showed no single nucleotide variation, but those that did showed evidence of purifying selection. Our study lays the groundwork for unraveling mechanisms driving the evolution of this large gene family in vertebrates. Significance statement We show here that the gene repertoires of vertebrates can be extremely variable, with different individuals having different genes. By sequencing one large family of immune receptors from 93 wild and laboratory zebrafish we found hundreds of novel gene copies, each only present in specific strains or specific individuals. Our observations can be explained by a combination of complex patterns of inheritance and a high rate of gene birth and death.
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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 | 0.000 | 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".