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Record W337119818 · doi:10.2527/2003.8181919x

Fine mapping of quantitative trait loci and assessment of positional candidate genes for backfat on bovine chromosome 14 in a commercial line of Bos taurus1

2003· article· en· W337119818 on OpenAlexaff
S. S. Moore, C. Li, J. A. Basarab, W. M. Snelling, J. Kneeland, Brenda M. Murdoch, C. Hansen, B. Benkel

Bibliographic record

VenueJournal of Animal Science · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAgriculture and Agri-Food CanadaAlberta Crop Industry Development FundUniversity of Alberta
Fundersnot available
KeywordsQuantitative trait locusBiologyCandidate geneGeneticsSingle-nucleotide polymorphismGeneHaplotypeSNPChromosomeAlleleGenotype

Abstract

fetched live from OpenAlex

Backfat thickness is one of the major quantitative traits that affect carcass quality in beef cattle. In this study, we have fine mapped a QTL for backfat EBV on bovine chromosome 14, using an identical-by-descent haplotype-sharing analysis, in a commercial line of Bos taurus. We also examined the association between gene-specific single nucleotide polymorphism (SNP) markers of the genes diacylgcerol acyltransferase 1 (DGAT1) and thyroglobulin (TG) and the backfat EBV. The results indicate that the QTL region for backfat identified on chromosome 14 is in agreement with previous studies. However, neither of the two polymorphisms of candidate genes tested, DGAT1 nor TG, showed a significant (P > 0.10) association with the backfat EBV in the cattle populations examined. However, a strong association (P = 0.0058) was detected between a microsatellite marker (CSSM66) lying approximately mid-way between the two candidate genes and the backfat EBV. These results suggest that other SNP of DGAT1, TG, or other gene(s) in the chromosomal region should be examined to test whether they have a significant effect on lipid metabolism.

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 score0.597
Threshold uncertainty score0.241

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 head0.030
GPT teacher head0.325
Teacher spread0.295 · 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

Citations86
Published2003
Admission routes1
Has abstractyes

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