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Record W4224316218 · doi:10.3390/plants11091146

Novel QTL for Low Seed Cadmium Accumulation in Soybean

2022· article· en· W4224316218 on OpenAlexafffund
Nour Nissan, Julia Hooker, Arezo Pattang, Martin Charette, Malcolm J. Morrison, Kangfu Yu, Anfu Hou, Ashkan Golshani, Stephen J. Molnar, Elroy R. Cober, Bahram Samanfar

Bibliographic record

VenuePlants · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsCarleton UniversityAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaCanadian Field Crop Research Alliance
KeywordsQuantitative trait locusBiologyLocus (genetics)AllelePopulationGenetic linkageGeneticsGeneInbred strainGene mappingMicrosatelliteGenetic markerEuropean unionChromosome

Abstract

fetched live from OpenAlex

Soybean is a valuable crop, used in animal feed and for human consumption. Selecting soybean cultivars with low seed cadmium (Cd) concentration is important for the purpose of minimizing the transfer of Cd into the human body. To ensure international trade, farmers need to produce soybean that meets the European Union (EU) Cd limit of 0.2 mg kg−1. In this study, we evaluated two populations of recombinant inbred lines (RILs), X5154 and X4050, for seed Cd accumulation. Linkage maps were constructed with 325 and 280 polymorphic simple sequence repeat (SSR) markers, respectively, and used to identify a novel minor quantitative trait locus (QTL) on chromosome 13 in the X4050 population between SSR markers Satt522 and Satt218. Based on a gene ontology search within the QTL region, seven genes were identified as candidates responsible for low seed Cd accumulation, including Glyma.13G308700 and Glyma.13G309100. In addition, we confirmed the known major gene, Cda1, in the X5154 population and developed KASP and CAPS/dCAPS allele-specific markers for efficient marker-assisted breeding for Cda1.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.059
GPT teacher head0.260
Teacher spread0.201 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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