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Record W4409155554 · doi:10.3389/fpls.2025.1551804

GWAS and WGCNA analysis uncover candidate genes associated with drought in Brassica juncea L.

2025· article· en· W4409155554 on OpenAlexaff
Yusong Zhang, Xiaoyan Yuan, Yunyun Zhang, Yanqing Luo, Kaiqin Zhao, Feng Zu, Zhengshu Tian, Jinfeng Li, Lifan Zhang, Xiaoying He, Jingyang Gao, Fu MingLian, Genze Li, Feihu Liu

Bibliographic record

VenueFrontiers in Plant Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsMinistry of Agriculture
FundersModern Agricultural Technology Industry System of Shandong province
KeywordsDrought toleranceSeedlingBiologyGerminationGermplasmBrassicaCandidate geneAgronomyHorticultureGeneGenetics

Abstract

fetched live from OpenAlex

Drought poses a major challenge to crop growth and yield, and exploring the drought tolerance of crops is an effective and economical approach to mitigating the effects of drought. To screen drought-tolerant germplasm resources and key functional genes related to drought tolerance in Brassica juncea L.(193 accessions), three treatments were applied at the germination and seedling stages:control(CK), moderate drought stress (M), and severe drought stress (S). Drought tolerance identification, GWAS, and RNA-Seq analysis of these materials under different treatments showed that drought stress significantly reduced the germination rate, aboveground and underground fresh weight at the seedling stage, harvest index at maturity, and expanded the root/shoot ratio. From the 193 materials, 24 drought-tolerant, 139 drought-tolerant medium, and 30 drought-sensitive materials were identified. The 77 SNPs identified by GWAS were associated with the relative germination rate at the germination stage, and the fresh weight of the aboveground and underground parts at the seedling stage, which could be integrated into 27 QTLs. WGCNA identified 15, 0, and 5 modules significantly related to drought tolerance in the aboveground and underground parts at the germination and seedling stages, respectively. By correlating the significant GWAS SNPs with the significant WGCNA modules, a total of 11 genes related to drought tolerance under moderate and severe drought stress were identified. These genes were involved in the regulation of auxin-responsive protein ( SAUR ), LEA protein, glucosidase, AP2/ERF , WRKY and GATA transcription factors, FLZ zinc finger domain, PRP , and b561 proteins. Among them, the BjuB035910 gene was detected in the underground parts of the seedling and germination stages under moderate drought stress. GWAS and selective sweep analysis jointly identified the 23.955-24.089 Mb region of chromosome B06, where four genes ( BjuB022264, BjuB022292, BjuB022282 , and BjuB022235 ) were located, as confirmed by WGCNA analysis. A total of 125 SNPs with high linkage disequilibrium were found in this region, and 12 haplotypes were detected, with Hap1 being present exclusively in drought-tolerant materials and Hap3-Hap12 distributed in drought-sensitive materials. These findings provide new insights into the drought tolerance mechanisms of B. juncea and will contribute to the breeding of drought-tolerant rapeseed varieties.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.198
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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