0891 Genome-wide efficient mixed-model study for meat quality in Nellore cattle
Bibliographic record
Abstract
The quality of meat, which includes several traits such as tenderness, juiciness, and fat thickness, is essential for the beef industry. Previous genome-wide association studies (GWAS) using Bayesian methods have shown that Brazilian Nellore cattle have enough genetic variation for improvement of these traits. Thus, the aim of this study was to further identify quantitative trait loci (QTL) associated with meat-quality-related traits in Nellore beef cattle by using the univariate linear mixed model (LMM) approach implemented in the GEMMA software and compare it with our previous GWA studies performed using Bayesian approaches. A total of 387 Nelore steers comprising 34 half-sib families were genotyped using the IlluminaBovineHDBeadChip. We analyzed the association between markers and Warner-Bratzler shear force, backfat thickness, ribeye muscle area, scanning parameters lightness (L*), redness (a*), and yellowness (b*) to ascertain color characteristics of the meat, water-holding capacity, cooking loss, muscle pH, myofibrillar fragmentation index, saturated fat sum, omega-6 fatty acids sum, omega-3 fatty acids sum, and ethereal extract. These phenotypes were measured in the Longissimus dorsi muscle between the 11th and 13th ribs collected at slaughter. We identified fifty-three genomic regions that each contained at least one single nucleotide polymorphism (SNP) that showed a significant association with meat quality traits (1-Mb SNP windows). Highlighted, we found regions associated with three genes—neuronal growth regulator 1 (NEGR1, chr03: 70884613–71949611), dynamin 3, and phosphatidylinositol glycan anchor biosynthesis class C (DNM3/PIGC, chr16: 37340706–38007593)—related to lipid metabolism and obesity. Our results provide a better understanding of QTL regions associated with meat quality unexplored in our previous Bayesian approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".