Integration of BSA-seq and high-resolution mapping reveals genomic regions and candidate genes controlling seed oil accumulation in peanut (Arachis hypogaea L.)
Bibliographic record
Abstract
Abstract Increasing seed oil content (SOC) is a primary objective in peanut (Arachis hypogaea L.) breeding, meeting the rising global demand for edible oil. Quantitative trait loci (QTL) mapping can help to identify genes underlying SOC variation and develop markers to enhance selection efficiency by marker-assisted breeding. In the present study, three major and stable QTL for SOC were identified on peanut chromosome Arahy.08, using a bulked segregant analysis (BSA) approach based on whole-genome sequencing of F8 recombinant inbred lines (RILs). The QTL qSOCA08-1, explaining 11.41-20.97% of phenotypic variation, was mapped on a 0.65 Mb genomic region. The QTL qSOCA08-2, accounting 25.57-39.40% of phenotypic variation, was located on a 1.04 Mb physical interval. Finally, qSOCA08-3 explaining up to 17.31% of the phenotypic variation, was mapped in a genomic region of 1.02 Mb. The genetic effects of these three QTL were assessed using near‑isogenic lines (NILs), derived from residual heterozygous individuals, and a tetraploid peanut germplasm panel. Potential candidate genes within the physical intervals of corresponding major QTL were predicted to participate in the oil biosynthesis in peanut. In summary, our study provides valuable genetic resources and tightly linked molecular markers for peanut molecular breeding aimed at improving SOC.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".