Western white pine SNP discovery and high-throughput genotyping for breeding and conservation applications
Bibliographic record
Abstract
BACKGROUND: Western white pine (WWP, Pinus monticola Douglas ex D. Don) is of high interest in forest breeding and conservation because of its high susceptibility to the invasive disease white pine blister rust (WPBR, caused by the fungus Cronartium ribicola J. C. Fisch). However, WWP lacks genomic resource development and is evolutionarily far away from plants with available draft genome sequences. Here we report a single nucleotide polymorphism (SNP) study by bulked segregation-based RNA-Seq analysis. RESULTS: A collection of resistance germplasm was used for construction of cDNA libraries and SNP genotyping. Approximately 36-89 million 2 × 100-bp reads were obtained per library and de-novo assembly generated the first shoot-tip reference transcriptome containing a total of 54,661 unique transcripts. Bioinformatic SNP detection identified >100,000 high quality SNPs in three expressed candidate gene groups: Pinus highly conserved genes (HCGs), differential expressed genes (DEGs) in plant defense response, and resistance gene analogs (RGAs). To estimate efficiency of in-silico SNP discovery, genotyping assay was developed by using Sequenom iPlex and it unveiled SNP success rates from 40.1% to 61.1%. SNP clustering analyses consistently revealed distinct populations, each composed of multiple full-sib seed families by parentage assignment in the WWP germplasm collection. Linkage disequilibrium (LD) analysis identified six genes in significant association with major gene (Cr2) resistance, including three RGAs (two NBS-LRR genes and one receptor-like protein kinase -RLK gene), two HCGs, and one DEG. At least one SNP locus provided an excellent marker for Cr2 selection across P. monticola populations. CONCLUSIONS: The WWP shoot tip transcriptome and those validated SNP markers provide novel genomic resources for genetic, evolutionary and ecological studies. SNP loci of those candidate genes associated with resistant phenotypes can be used as positional and functional variation sites for further characterization of WWP major gene resistance against C. ribicola. Our results demonstrate that integration of RNA-seq-based transcriptome analysis and high-throughput genotyping is an effective approach for discovery of a large number of nucleotide variations and for identification of functional gene variants associated with adaptive traits in a non-model 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".