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Record W2749692403 · doi:10.1139/gen-2017-0097

Identification of quantitative trait loci for seedling root traits from Tibetan semi-wild wheat (<i>Triticum aestivum</i> subsp. <i>tibetanum</i>)

2017· article· en· W2749692403 on OpenAlexvenueno aff
Jian Ma, Wei Luo, Han Zhang, Zhou Xiao-hong, Nana Qin, Yuming Wei, Yaxi Liu, Qiantao Jiang, Guoyue Chen, Youliang Zheng, Xiujin Lan

Bibliographic record

VenueGenome · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
FundersDepartment of Science and Technology of Sichuan ProvinceNational Natural Science Foundation of China
KeywordsBiologyQuantitative trait locusLocus (genetics)SeedlingInbred strainPopulationAgronomyAlleleCultivarGeneGenetics

Abstract

fetched live from OpenAlex

As a primitive hexaploid wheat resource distributed only in Tibet, Tibetan semi-wild wheat (Triticum aestivum subsp. tibetanum Shao) possesses unique characteristics that could be exploited in wheat breeding programs. Its good root system could offer a stable platform for above-ground components. To detect possible excellent locus for root traits from Tibetan semi-wild wheat, we identified QTLs for root traits using a recombinant inbred line population derived from a cross between Tibetan semi-wild wheat Q1028 and Zhengmai 9023. A total of 15 QTLs on eight chromosomes were detected, including four major QTLs, QMrl.sau-7B, QTrl.sau-4B, QAd.sau-7A, and QSa.sau-4B. The phenotypic variation explained by each of these QTLs ranges from 5.67% to 16.68%. Positive alleles of six QTLs were derived from Q1028. Several novel QTLs for root traits were identified. In addition, significant correlations were detected amongst root traits and agronomic traits. Taken together, these results suggest that Tibetan semi-wild wheat and the newly identified novel QTLs could be useful in future breeding programs.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.384

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 head0.033
GPT teacher head0.259
Teacher spread0.226 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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