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Record W4405596668 · doi:10.1186/s12870-024-05930-8

Identification of two QTLs for web blotch resistance in peanut (Arachis hypogaea L.) based on BSA-seq

2024· article· en· W4405596668 on OpenAlexaff
Mingbo Zhao, Ziqi Sun, Feiyan Qi, Liu Hua, Stefano Pavan, Liuyang Fu, Juan Wang, Guoquan Chen, Fanke Zeng, Xiaohui Wu, Pengyu Qu, Wenzhao Dong, Zheng Zheng, Xinyou Zhang

Bibliographic record

VenueBMC Plant Biology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPeanut Plant Research Studies
Canadian institutionsMinistry of Agriculture
FundersHenan Academy of Agricultural SciencesNational Natural Science Foundation of China
KeywordsBiologyQuantitative trait locusArachis hypogaeaBulked segregant analysisPopulationAgronomyGeneticsGene mappingGeneChromosome

Abstract

fetched live from OpenAlex

BACKGROUND: Peanut (Arachis hypogaea L.) is a globally important oilseed and cash crop. Web blotch is one of the most important peanut foliar diseases, causing severe yield losses worldwide. RESULTS: population was used to identify quantitative trait loci (QTLs) for peanut web blotch resistance, based on bulked segregant analysis (BSA). Kompetitive Allele-Specific PCR (KASP) markers were developed and used to further narrow QTL intervals and detect candidate genes. Two major QTLs, qWBRA05 and qWBRA08 were identified, spanning physical intervals of 465.75 Kb and 434.83 Kb, and explaining percentages of phenotypic variation (PVE) of 8.79% and 15.09%, respectively. Moreover, two KASP markers were developed within the QTL interval effectively distinguished between web blotch resistance and web blotch susceptible materials. CONCLUSIONS: The QTLs identified and two molecular markers closely linked to web blotch resistance were developed within the QTL interval, which are potentially valuable in peanut breeding.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

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

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 head0.048
GPT teacher head0.300
Teacher spread0.252 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2024
Admission routes1
Has abstractyes

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