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Record W4409577115 · doi:10.1016/j.psj.2025.105194

Genome-wide association study to identify biological and metabolic pathways associated with carcass portion weights in turkeys

2025· article· en· W4409577115 on OpenAlexafffund
Emily M. Leishman, Ryley J Vanderhout, Benjamin J. Wood, Christine F. Baes, Shai Barbut

Bibliographic record

VenuePoultry Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaOntario GenomicsOntario Genomics InstituteGenome Canada
KeywordsBiologyGenome-wide association studyAssociation (psychology)Genetic associationComputational biologyGeneticsGeneSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

The application of genetic and genomic improvement strategies in the poultry industry has been widely successful at improving meat yield and efficiency, however some challenges persist. As demand for larger and leaner birds increases, we have not fully assessed how selection for growth affects various carcass portions. The objective of this study was to conduct a genome wide association study (GWAS) and functional analysis on turkey carcass portion weights. Phenotypic data consisted of carcass portion weights (fillets, tenders, drums, thighs) obtained at processing (N = 646 - 1,478). Genotypic records were available from a proprietary 65 K single nucleotide polymorphism (SNP) chip. A linear mixed model was used to estimate SNP effects and a 30-SNP sliding window approach was used. Across all traits, 14 functional candidate genes (FCGs) were identified, and these were predominately associated with protein metabolism and immune function. Interestingly, carcass portions did not share FCGs, except for the thighs and drums, which shared one functional candidate gene (PDGFB). These results add to the understanding of the genetic architecture of carcass portion weights, and this could be applied in a turkey breeding program.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.037
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.269
Teacher spread0.256 · 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
Published2025
Admission routes2
Has abstractyes

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