Small but significant genetic differentiation among populations of Phyllachora maydis in the midwestern United States revealed by microsatellite (SSR) markers
Bibliographic record
Abstract
Phyllachora maydis Maubl, the causal pathogen of tar spot of corn (Zea mays L.), has emerged recently in the United States and Canada. Studies related to its genetic diversity and population structure are limited and are necessary to improve our understanding of this pathogen’s biology, ecology, epidemiology, and evolutionary potential within this region. This study developed and used 13 microsatellites (SSR markers) to assess the genetic population structure, diversity, gene flow and reproductive mode of 181 P. maydis samples across five states in the Midwest U.S. The polymorphic information content (PIC) of loci ranged from 0.32 to 0.72 per locus, indicating their high utility for assessing the dynamics of P. maydis populations. Analysis of molecular variance (AMOVA) detected a significantly low, but statistically significant genetic differentiation (FST = 0.15) among populations, where 85% of the variance resided within populations. P. maydis populations were highly diverse (He = 0.55), with moderate gene flow (Nm = 2.80), and showed evidence of sexual recombination (r̄d; p = > 0.001). Structure analysis showed the samples were not geographically structured but rather grouped into two genetic clusters (k =2) of severe genetic admixture suggesting possible long-distance dispersal of aerial spores or infected corn materials among the five Midwest states. Both principal coordinate analysis (PCoA) and discriminate analysis of principal component (DAPC) supported the STRUCTURE analysis of the two clusters. These 13 highly polymorphic molecular markers could be used for future investigations of this pathogen’s population dynamics within the U.S., and possibly populations outside.
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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 | 0.001 | 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) | 0.000 | 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".