Identification of two major QTLs for pod shell thickness in peanut (Arachis hypogaea L.) using BSA-seq analysis
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Bibliographic record
Abstract
Abstract Background Pod shell thickness (PST) is an important agronomic trait of peanut because it affects the ability of shells to resist pest infestations and pathogen attacks, while also influencing the peanut shelling process. However, very few studies have explored the genetic basis of PST. Results An F 2 segregating population derived from a cross between the thick-shelled cultivar Yueyou 18 (YY18) and the thin-shelled cultivar Weihua 8 (WH8) was used to identify the quantitative trait loci (QTLs) for PST. On the basis of a bulked segregant analysis sequencing (BSA-seq), four QTLs were preliminarily mapped to chromosomes 3, 8, 13, and 18. Using the genome resequencing data of YY18 and WH8, 22 kompetitive allele-specific PCR (KASP) markers were designed for the genotyping of the F 2 population. Two major QTLs ( qPSTA08 and qPSTA18 ) were identified and finely mapped, with qPSTA08 detected on chromosome 8 (0.69-Mb physical genomic region) and qPSTA18 detected on chromosome 18 (0.15-Mb physical genomic region). Moreover, qPSTA08 and qPSTA18 explained 31.1–32.3% and 16.7–16.8% of the phenotypic variation, respectively. Fifteen genes were detected in the two candidate regions, including three genes with nonsynonymous mutations in the exon region. Two molecular markers (Tif2_A08_31713024 and Tif2_A18_7198124) that were developed for the two major QTL regions effectively distinguished between thick-shelled and thin-shelled materials. Subsequently, the two markers were validated in four F 2:3 lines selected. Conclusions The QTLs identified and molecular markers developed in this study may lay the foundation for breeding cultivars with a shell thickness suitable for mechanized peanut shelling.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it