Limber pine (<i>Pinus flexilis</i> James) genetic map constructed by exome‐seq provides insight into the evolution of disease resistance and a genomic resource for genomics‐based breeding
Bibliographic record
Abstract
Limber pine (Pinus flexilis) is a keystone species of high-elevation forest ecosystems of western North America, but some parts of the geographic range have high infection and mortality from the non-native white pine blister rust caused byCronartium ribicola. Genetic maps can provide essential knowledge for understanding genetic disease resistance as well as local adaptation to changing climates. Exome-seq was performed to construct high-density genetic maps in two seed families. Composite maps positioned 9612 unigenes across 12 linkage groups (LGs). Syntenic analysis of genome structure revealed that the majority of orthologs were positional orthologous genes (POGs) with localization on homologousLGs among conifer species. Gene ontology (GO) enrichment analysis showed relatively fewer constraints forPOGs with putative roles in adaptation to environments and relatively more conservation forPOGs with roles in basic cell function and maintenance. The mapped genes included 639 nucleotide-binding site leucine-rich repeat genes (NBS-LRRs), 290 receptor-like protein kinase genes (RLKs), and 1014 genes with potential roles in the defense response and induced systemic resistance to attack by pathogens. Orthologous loci for resistance to rust pathogens were identified and were co-positioned with multiple members of theR gene family, revealing the evolutionary pressure acting upon them. This high-density genetic map provides a genomic resource and practical tool for breeding and genetic conservation programs, with applications in genome-wide association studies (GWASs), the characterization of functional genes underlying complex traits, and the sequencing and assembly of the full-length genomes of limber pine and relatedPinus species.
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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 | 0.000 |
| 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) | 0.002 | 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".