Functional Bias and Demographic History Obscure Patterns of Selection among Single-Copy Genes in a Fungal Species Complex
Bibliographic record
Abstract
Abstract Many different evolutionary processes may be responsible for explaining natural variation within genomes, some of which include natural selection at the molecular level and changes in population size. Fungi are highly adaptable organisms, and their relatively small genomes and short generation times make them pliable for evolutionary genomic studies. However, adaptation in wild populations has been relatively less documented compared to experimental or clinical studies. Here, we analyzed DNA sequences from 502 putative single-copy orthologous genes in 63 samples that represent seven recently diverged North American Amanita ( jacksonii -complex) lineages. For each gene and each species, we measured the genealogical sorting index ( gsi ) and infinite-site-based summary statistics, such as , and D Taj in coding and intron regions. MKT-based approaches and likelihood-ratio-test K n /K s models were used to measure natural selection in all coding sequences. Multi-locus (Extended) Bayesian Skyline Plots (eBSP) were used to model intraspecific demographic changes through time based on unlinked, putative neutral regions (introns). Most genes show evidence of long-term purifying selection, likely reflecting a functional bias implicit in single-copy genes. We find that two species have strongly negatively skewed Tajima’s D , while three other have a positive skew, corresponding well with patterns of demographic expansion and contraction. Standard MKT analyses resulted in a high incidence of near-zero α with a tendency towards negative values. In contrast, α estimates based on the distribution of fitness effects (DFE), which accounts for demographic effects and slightly deleterious mutations, suggest a higher proportion of sites fixed by positive selection. The difference was more notorious in species with expansion signatures or with historically low population sizes, evidencing the concealing effects of specific demographic histories. Finally, we attempt to mitigate Gene Ontology term overrepresentation, highlighting the potential adaptive or ecological roles of some genes under positive 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".