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Association analyses of a SNP in the promoter of <i>IGF1</i> with fat deposition and carcass merit traits in hybrid, Angus and Charolais beef cattle

2009· article· en· W2066213232 on OpenAlexafffund
Khandker Khaldun Islam, Michael Vinsky, R. E. Crews, E. K. Okine, S. S. Moore, D. H. Crews, Chengdao Li

Bibliographic record

VenueAnimal Genetics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of LethbridgeUniversity of Alberta
FundersAlberta Beef Producers
KeywordsBiologyBeef cattleBreedSNPSingle-nucleotide polymorphismGenotypePopulationAnimal scienceAlleleGeneticsGene

Abstract

fetched live from OpenAlex

A SNP in the promoter region of insulin like growth factor-1 (IGF1) (c.-512C>T) was analysed for associations with 10 fat deposition and carcass merit traits in hybrid (n = 455), Angus (n = 204) and Charolais (n = 186) beef cattle populations. Significant associations of the SNP were found for ultrasound backfat thickness (P = 0.030), carcass average backfat (P = 0.015) and carcass lean meat yield (LMY) (P = 0.023) in the Angus beef population, with the 'CC' genotype showing higher fat depth and lower LMY than the 'TT' genotype. Analyses of transcription factor binding sites based on transcription element search system prediction revealed that the 'C' allele introduces a binding site for nuclear factor I, which has an adipose tissue-specific regulatory role and thus may contribute to the SNP effect on fat deposition in the population of pure Angus cattle, a breed with greater fat depth than the hybrid and Charolais breeds.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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

Citations37
Published2009
Admission routes2
Has abstractyes

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