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Record W4388539474 · doi:10.1093/jas/skad281.158

212 Genome-Wide Association Study Investigating the Genomic Components of Efficiency in Beef Cows

2023· article· en· W4388539474 on OpenAlexaff
Laine T Radwell, Justin J Delver, H.A. Lardner, Greg B Penner, Mika Asai-Coakwell

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsResidual feed intakeGenome-wide association studyBeef cattleBiologyGenomic selectionIce calvingRestricted maximum likelihoodPercentileBest linear unbiased predictionAnimal scienceStatisticsSingle-nucleotide polymorphismGeneticsMathematicsFeed conversion ratioBody weightComputer scienceMaximum likelihoodGenotypeLactationPregnancySelection (genetic algorithm)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract Efficiency of mature beef cows consuming high-forage diets is not comparable with standard definitions of beef cattle efficiency such as feed conversion or residual feed intake in the feedlot setting. This study aimed to conduct a genome-wide association study (GWAS) to identify regions of the bovine genome associated with efficiency in mature beef cows. Ninety-eight black Angus cows were managed under extensive feeding programs over a two-year period. Using rump fat at calving, calving date, and calf weaning weight as a percentage of the body weight of the dam, a weighted percentile scoring system was used to rank efficiency. Eighty-three of the cows were retained for the GWAS. The 20 most and 20 least efficient cows (HD dataset, n=40) were genotyped with the Illumina BovineHD BeadChip (777,000 SNPs) while the remaining cows were genotyped with the Neogen GGP Bovine 100K chip (LD dataset, n = 43). The LD dataset was imputed to the HD SNP array density using Beagle5.4 (FULL dataset, n = 83). Three separate GWAS were conducted using GAPIT (version3). The first GWAS was performed on the HD dataset using a quantitative phenotype determined by the ranking system. The second GWAS was performed with the HD dataset using a qualitative phenotype (efficient vs non-efficient cows). For the final GWAS, the FULL dataset with ranked phenotypes was used. Five models were evaluated for each GWAS, including the general linear model (GLM), mixed linear model (MLM), multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). The model that best represented each dataset as determined by quantile-quantile plots was used for downstream analysis. Although results of the GWAS were not significant (Bonferroni threshold), associations were identified on BTA10, 16, 17, 25, 27 and 29. Positional candidate genes in the associated regions include: FOXN3, UPB1, SNX29, DDX54, LOC112444612, RITA1, and TACC1. Further functional analyses are required to confirm these findings; however, this work provides a foundation for identifying genes and gene mutations influencing this newly described phenotype.

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)0.002
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.272
Teacher spread0.249 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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