Improving detection of potato virus X resistance genes Rx1 and Rx2 in tetraploid potato using optimized PCR and KASP markers
Bibliographic record
Abstract
Potato virus X (PVX) seriously threatens global potato cultivation, necessitating a deeper understanding of the genetic mechanisms responsible for resistance. The Rx1 and Rx2 genes play a pivotal role in breeding potatoes with broad-spectrum PVX resistance. However, current DNA markers for these genes present uncertainties, as they are found in both resistant and susceptible materials, complicating the identification of the responsible resistance gene. This study aimed to develop robust high-throughput molecular markers, specifically Kompetitive Allele-Specific PCR (KASP) markers, to reliably detect the Rx1 and Rx2 genes. Genetic polymorphism analysis of seven resistant and four susceptible potato genotypes revealed seven single nucleotide polymorphisms (SNPs) and one insertion-deletion (Indel), along with corresponding amino acid changes in the Rx1 and Rx2 protein sequences. Based on these variations, high-throughput molecular markers were developed. Notably, the primer pairs Rx1-1R-F + Rx1-5R-R and Rx2-1R2-F + Rx2-2R2-R showed high accuracy, detecting Rx1 and Rx2 genes without false positives or false negatives. Optimized multiplex PCR protocols also demonstrated strong potential in simultaneously detecting both genes. KASP markers targeting SNPs at positions 2 515 (G to C) in the Rx1 CDS and 2 445 (C to G) in the Rx2 CDS efficiently differentiated between heterozygous resistant and homozygous susceptible genotypes. Furthermore, these markers effectively identified Rx1 and Rx2 genotypes in 243 and 133 progenies across multiple crosses. Progenies that tested positive for Rx1 and Rx2 through PCR or KASP assays exhibited extreme resistance (ER) to PVX, while negative progeny was susceptible. These findings highlight the potential of the developed markers to accelerate the breeding cycle and improve the production of PVX-resistant potato varieties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".