Identification of QTLs associated with seed protein concentration in two diverse recombinant inbred line populations of pea
Bibliographic record
Abstract
Improving the seed protein concentration (SPC) of pea (Pisum sativum L.) has turned into an important breeding objective because of the consumer demand for plant-based protein and demand from protein fractionation industries. To support the marker-assisted selection (MAS) of SPC towards accelerated breeding of improved cultivars, we have explored two diverse recombinant inbred line (RIL) populations to identify the quantitative trait loci (QTLs) associated with SPC. The two RIL populations, MP 1918 × P0540-91 (PR-30) and Ballet × Cameor (PR-31), were derived from crosses between moderate SPC × high SPC accessions. A total of 166 and 159 RILs of PR-30 and PR-31, respectively, were genotyped using an Axiom® 90K SNP array and 13.2K SNP arrays, respectively. The RILs were phenotyped in replicated trials in two and three locations of Saskatchewan, Canada in 2020 and 2021, respectively, for agronomic assessment and SPC. Using composite interval mapping, we identified three QTLs associated with SPC in PR-30 and five QTLs in PR-31, with the LOD value ranging from 3.0 to 11.0. A majority of these QTLs were unique to these populations compared to the previously known QTLs for SPC. The QTL SPC-Ps-5.1 overlapped with the earlier reported SPC associated QTL PC-QTL-3. Three QTLs, SPC-Ps-4.2, SPC-Ps-5.1, and SPC-Ps-7.2 with LOD scores of 7.2, 7.9, and 11.3, and which explained 14.5%, 11.6%, and 11.3% of the phenotypic variance, respectively, can be used for marker-assisted breeding to increase SPC in peas. Eight QTLs associated with the grain yield were identified with LOD scores ranging from 3.1 to 8.2. Two sets of QTLs, SPC-Ps-2.1 and GY-Ps-2.1, and SPC-Ps-5.1 and GY-Ps-5.3, shared the QTL/peak regions. Each set of QTLs contributed to either SPC or grain yield depending on which parent the QTL region is derived from, thus confirming that breeding for SPC should take into consideration the effects on grain yield.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".