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Record W1797768122 · doi:10.4141/cjas2011-111

Short Communication: An association analysis between one missense polymorphism at the <i>SREBF1</i> gene and milk yield and composition traits in goats

2012· article· en· W1797768122 on OpenAlexvenueno aff
Arianna Manunza, A. Zidi, Víctor M. Fernández‐Cabanás, J. Jordana, J. Carrizosa, E. Belaifa, B. Urrutia, O. Polvillo, Pedro González Redondo, Marcel Amills, J.M. Serradilla

Bibliographic record

VenueCanadian Journal of Animal Science · 2012
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGeneMissense mutationGeneticsPolymorphism (computer science)ExonComposition (language)GenotypePhenotype

Abstract

fetched live from OpenAlex

Manunza, A., Zidi, A., Fernández-Cabanás, V. M., Jordana, J., Carrizosa, J., Belaifa, E., Urrutia, B., Polvillo, O., González-Redondo, P., Amills, M. and Serradilla, J. M. 2012. Short Communication: An association analysis between one missense polymorphism at the SREBF1 gene and milk yield and composition traits in goats. Can. J. Anim. Sci. 92: 167–173. Sterol regulatory element binding transcription factor 1 (SREBF1) regulates the expression of genes involved in the biosynthesis of fatty acids and cholesterol. Herewith, we have sequenced the near-complete coding region and part of the 3′UTR of the goat SREBF1 gene. In doing so, we have detected a missense c.353C>T polymorphism causing a proline to leucine substitution at position 118 (P118L). An association analysis with milk composition traits recorded in Murciano-Granadina goats only revealed a statistical tendency linking SREBF1 genotype and milk omega-3 fatty acid content. The lack of significant associations suggests that the P118L substitution does not involve a functional change.

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)0.002
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.039
GPT teacher head0.273
Teacher spread0.234 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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