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Record W2565912248 · doi:10.1139/gen-2016-0104

Rapid identification of a new gene influencing low amylose content in rice landraces (<i>Oryza sativa</i> L.) using genome-wide association study with specific-locus amplified fragment sequencing

2016· article· en· W2565912248 on OpenAlexvenueno aff
Xinghai Yang, Baoxuan Nong, Xiuzhong Xia, Zongqiong Zhang, Yu Zeng, Kaiqiang Liu, Guofu Deng, Danting Li

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
FundersGuangxi Academy of Agricultural SciencesNatural Science Foundation of Guangxi ProvinceNational Natural Science Foundation of China
KeywordsBiologyAmyloseOryza sativaLocus (genetics)GeneticsGeneGenomePanicleGenome-wide association studyCandidate geneOryzaStarchBotanySingle-nucleotide polymorphismGenotypeFood science

Abstract

fetched live from OpenAlex

Starch is the major component of milled rice, and amylose content (AC) affects eating quality. In this study, a genome-wide association study (GWAS) with specific-locus amplified fragment sequencing (SLAF-seq) data was performed for AC on a core collection of 419 rice landraces. Using the compressed mixed linear method based on the Q+K model, we identified a new gene, LAC6 (Chr6: 5.65-6.04 Mb), associated with AC in the low amylose content panel. The LAC6 candidate gene was detected by qRT-PCR in rice panicles. Results indicate that LOC_Os06g11130 was up-regulated, and LOC_Os06g11340 was significantly down-regulated, making it most likely a key candidate gene of LAC6. In conclusion, the findings provide a certain theoretiacal basis of molecular biology for genetic improvement of AC in rice and rice quality variety 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 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.225
Teacher spread0.194 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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