Patterns of presence-absence variation of NLRs across populations of <i>Solanum chilense</i> are clade-dependent and mainly shaped by past demographic history
Bibliographic record
Abstract
Abstract Understanding the evolution of pathogen resistance genes (nucleotide-binding site-leucine-rich repeats, also known as NLRs) within a species requires a comprehensive examination of factors that affect gene loss and gain. We present a new reference genome of Solanum chilense , that leads to an increased number and more accurate annotation of NLRs. Next, using a target-capture approach, we quantify the presence-absence variation (PAV) of NLR loci across 20 populations from different habitats. We build a rigorous pipeline to validate the identification of PAV of NLRs, then show that PAV is larger within populations than between populations, suggesting that maintenance of NLR diversity is linked to population dynamics. Furthermore, the amount of PAV is not correlated with the NLR presence in gene clusters in the genome, but rather with the past demographic history of the species, with loss of NLRs in diverging populations at the distribution edges and smaller population sizes. Finally, using a redundancy analysis, we find limited evidence of PAV being linked to environmental gradients. Our results contradict the classic assumptions of the important selective role of PAV for NLRs, and suggest that NLRs PAV is driven by random processes (and weak selection) in an outcrossing plant with high nucleotide diversity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".