Candidate gene identification and marker development for seed coat peeling rate in peanut (Arachis Hypogaea L.)
Bibliographic record
Abstract
BACKGROUND: Cultivated peanut (Arachis hypogaea L.) is an important economic and oilseed crop in China. The seed coat plays a crucial role in resisting pests and diseases, and seed coat peeling rate (SCPR) is a key factor influencing the efficiency and quality of mechanical shelling. Given the high kernel breakage rate and susceptibility to Aspergillus flavus infection during mechanical shelling, gene mining for SCPR holds significant theoretical and practical value. However, the genetic basis of SCPR has rarely been reported. RESULTS: This study represented the first identification of genetic loci associated with SCPR in peanut. A genome-wide association study (GWAS) was conducted on a natural population comprising 353 peanut accessions, while quantitative trait locus (QTL) mapping was performed using a recombinant inbred line (RIL) population of 521 lines derived from YZ9102 and WT09-0023. GWAS analysis revealed a significantly associated genomic region at the distal end of chromosome 5, encompassing 111 significant single nucleotide polymorphisms (SNPs), among which six SNPs were consistently detected across two environments and exhibited strong linkage with SCPR. QTL mapping identified five QTLs associated with SCPR, located on chromosomes A04, A05, A09, A10, and A18, with LOD scores ranging from 3.06 to 5.54. Notably, the co-localization of GWAS signals and QTL mapping at the distal end of chromosome 5 suggests that qSCPRA05 represents a stable and major QTL governing SCPR in peanut, spanning a 385.66 kb physical interval (Arahy.05:114,895,772 - 115,281,432). Within this region, three linkage disequilibrium (LD) blocks were detected, harboring 33 candidate genes. Among them, Arahy.0C6ZNN, which encodes laccase, was identified as the most likely candidate gene through integration of sequence variation analysis between the RIL parental lines and functional gene annotation. Furthermore, a functional marker A05.114993389 was developed and validated in both the natural and RIL populations, providing a valuable genomic resource for marker-assisted selection (MAS) in peanut breeding programs. CONCLUSIONS: This study represented the gene mining of SCPR in peanut, providing novel insights into its genetic basis and laying a foundation for elucidating the underlying regulatory mechanisms. The identification of a major QTL qSCPRA05 and the candidate gene Arahy.0C6ZNN may offer valuable targets for further functional research. Moreover, the development of molecular markers linked to SCPR presents a promising tool for marker-assisted selection (MAS), facilitating genetic improvement and accelerating breeding efforts for enhanced seed coat integrity in peanut.
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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 | 0.001 |
| 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".