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Record W4389542139 · doi:10.1139/cjas-2023-0102

Single-nucleotide polymorphisms in calpastatin gene and the association with growth traits in Tibetan sheep (<i>Ovis aries</i>)

2023· article· en· W4389542139 on OpenAlexvenueno aff
Zhanhong Gao, Sayed Haidar Abbas Raza, Boyan Ma, Fengshuo Zhang, Shengzhen Hou, Hessah Alshammari, Essam Eldin Abdelhady Salama, Waleed Al Abdulmonem, Abdullah S.M. Aljohanih, Ahmed A. El‐Mansi, Ayman H. Abd El‐Aziz, Bandar Hamad Aloufi, Linsheng Gui

Bibliographic record

VenueCanadian Journal of Animal Science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalpain Protease Function and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsCalpastatinSingle-nucleotide polymorphismBiologyGeneGeneticsGenetic associationOvisGene expressionPolymorphism (computer science)Molecular biologyGenotypeCalpainEnzyme

Abstract

fetched live from OpenAlex

The calpastatin gene has been extensively studied as a candidate gene because of its regulatory function within muscle development of animals. However, little is known about the association between variation of the calpastatin gene and growth traits in Tibetan sheep. Using DNA sequencing, four single-nucleotide polymorphisms g.3844A&gt;G, g.88874T&gt;C, g.89126C&gt;T, and g.89157A&gt;C were identified in this study. An association analysis indicates that the g.3844A&gt;G and g.89126C&gt;T polymorphisms affected body weight ( P &lt; 0.05). The g.89157A&gt;C polymorphism was significantly correlated with body weight and chest circumference ( P &lt; 0.05). The quantitative real-time polymerase chain reaction analysis revealed that the expression of calpastatin gene presented an increasing trend with an increase in age. Remarkably lower mRNA expression was detected at the fetal stage compared with adult ewes ( P &lt; 0.05). These findings indicated that the calpastatin gene polymorphisms were involved in growth-related traits in Tibetan sheep, which can be considered as genetic markers for improving the growth traits of Chinese Tibetan sheep.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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