Genetic diversity and phylogeny of South African Meloidogyne populations using genotyping by sequencing
Bibliographic record
Abstract
Meloidogyne species cause great crop losses worldwide. Although genetic host plant resistance is an effective control strategy to minimize damage caused by Meloidogyne, some resistant genes are ineffective against virulent species such as Meloidogyne enterolobii. Detailed knowledge about the genetic composition of Meloidogyne species is thus essential. This study focused on genotyping-by-sequencing (GBS) and Pool-Seq to elucidate the genetic relation between South African M. enterolobii, M. incognita and M. javanica populations. Hence, 653 common single nucleotide polymorphisms (SNPs) were identified and used to compare these species at genetic level. Allele frequencies of 34 SNPs consistently differed between the three Meloidogyne species studied. Principal component and phylogenetic analyses grouped the M. enterolobii populations in one clade, showing a distant relation to the M. javanica populations. These two species also shared genetic links with the M. incognita populations studied. GBS has been used successfully in this study to identify SNPs that discriminated among the three Meloidogyne species investigated. Alleles, only occurring in the genome of M. enterolobii and located in genes involved in virulence in other animal species (e.g. a serine/threonine phosphatase and zinc finger) have also been identified, accentuating the value of GBS in future studies of this nature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".