Saturated genic<scp>SNP</scp>mapping identified functional candidates and selection tools for the<i>Pinus monticola Cr2</i>locus controlling resistance to white pine blister rust
Bibliographic record
Abstract
Molecular breeding incorporates efficient tools to increase rust resistance in five-needle pines. Susceptibility of native five-needle pines to white pine blister rust (WPBR), caused by the non-native invasive fungus Cronartium ribicola (J.C. Fisch.), has significantly reduced wild populations of these conifers in North America. Major resistance (R) genes against specific avirulent pathotypes have been found in several five-needle pine species. In this study, we screened genic SNP markers by comparative transcriptome and genetic association analyses and constructed saturated linkage maps for the western white pine (Pinus monticola) R locus (Cr2). Phenotypic segregation was measured by a hypersensitive reaction (HR)-like response on the needles and disease symptoms of cankered stems post inoculation by the C. ribicola avcr2 race. SNP genotypes were determined by HRM- and TaqMan-based SNP genotyping. Saturated maps of the Cr2-linkage group (LG) were constructed in three seed families using a total of 34 SNP markers within 21 unique genes. Cr2 was consistently flanked by contig_2142 (encoding a ruvb-like protein) and contig_3772 (encoding a delta-fatty acid desaturase) across the three seed families. Cr2 was anchored to the Pinus consensus LG-1, which differs from LGs where other R loci of Pinus species were mapped. GO annotation identified a set of NBS-LRR and other resistance-related genes as R candidates in the Cr2 region. Association of one nonsynonymous SNP locus of an NBS-LRR gene with Cr2-mediated phenotypes provides a valuable tool for marker-assisted selection (MAS), which will shorten the breeding cycle of resistance screening and aid in the restoration of WPBR-disturbed forest ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".