A Picea abies Linkage Map Based on SNP Markers Identifies QTLs for Four Aspects of Resistance to Heterobasidion parviporum Infection
Bibliographic record
Abstract
A consensus linkage map of Picea abies, an economically important conifer, was constructed based on the segregation of 686 SNP markers in a F1 progeny population consisting of 247 individuals. The total length of 1889.2 cM covered 96.5% of the estimated genome length and comprised 12 large linkage groups, corresponding to the number of haploid P. abies chromosomes. The sizes of the groups (from 5.9 to 9.9% of the total map length) correlated well with previous estimates of chromosome sizes (from 5.8 to 10.8% of total genome size). Any locus in the genome has a 97% probability to be within 10 cM from a mapped marker, which makes the map suited for QTL mapping. Infecting the progeny trees with the root rot pathogen Heterobasidion parviporum allowed for mapping of four different resistance traits: lesion length at the inoculation site, fungal spread within the sapwood, exclusion of the pathogen from the host after initial infection, and ability to prevent the infection from establishing at all. These four traits were associated with two, four, four and three QTL regions respectively of which none overlapped between the traits. Each QTL explained between 4.6 and 10.1% of the respective traits phenotypic variation. Although the QTL regions contain many more genes than the ones represented by the SNP markers, at least four markers within the confidence intervals originated from genes with known function in conifer defence; a leucoanthocyanidine reductase, which has previously been shown to upregulate during H. parviporum infection, and three intermediates of the lignification process; a hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyltransferase, a 4-coumarate CoA ligase, and a R2R3-MYB transcription factor.
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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 | 0.000 | 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) | 0.001 | 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".