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Record W2972146365 · doi:10.3389/fgene.2019.00946

Single Nucleotide Polymorphisms in Starch Biosynthetic Genes Associated With Increased Resistant Starch Concentration in Rice Mutant

2019· article· en· W2972146365 on OpenAlexaff
Selvakumar Gurunathan, Bharathi Raja Ramadoss, Venkataramana Mudili, Chandranayaka Siddaiah, Naveen Kumar Kalagatur, J.R. Kannan Bapu, Chakrabhavi Dhananjaya Mohan, Abdulaziz A. Alqarawi

Bibliographic record

VenueFrontiers in Genetics · 2019
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Saskatchewan
FundersKing Saud UniversityDefence Research and Development Organisation
KeywordsMutantAmyloseStarchPopulationBiologySingle-nucleotide polymorphismResistant starchGeneOryza sativaGeneticsMutationFood scienceGenotypeMedicine

Abstract

fetched live from OpenAlex

Resistant Starch (RS), plays a crucial role in human health and nutrition by controlling glucose metabolism. RS or dietary fibre content in rice is low because it goes through a variety of process before it is ready for cooking and consumption. Hence, this study was carried out to develop a rice mutant with increased RS. The rice mutant (γ278) with increased RS was developed by utilizing gamma (γ) rays as a mutagen. Mutant γ278 was characterized for mutations in the starch biosynthetic genes viz., GBSSI, SSI, SSIIa, SSIIIa, SBEIa and SBEIIb to reveal the functional mutations/variations led to high RS content in rice. A total of 31 sequence variants/mutations in six genes were identified. We report the discovery of three deleterious mutation/variants each in GBSSI, SSIIa, and SSIIIa with the potential to increase RS content in rice. Further, wild x mutant crosses were made to develop an F2 population to study the effect of combination of deleterious mutations. The SNP (GBSSI:ssIIa:ssIIIa) combination responsible for high RS content in F2 population was identified and recorded highest amylose content (AC) (26.18%) and RS (8.68%) content. In conclusion, this marker combination will be highly useful to develop a rice variety with increased RS.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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

Citations27
Published2019
Admission routes1
Has abstractyes

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