Interaction of vitamin A supplementation level with ADH1C genotype on intramuscular fat in beef steers
Bibliographic record
Abstract
Previously, the single nucleotide polymorphism in alcohol dehydrogenase (ADH1C c.-64T>C) was shown to have an association with intramuscular fat (IMF) in the longissimus thoracis (LT) muscle when vitamin A was limited in finishing rations of beef steers. The purpose of this study was to determine the optimum vitamin A supplementation level, in combination with ADH1C genotype, to increase IMF of the LT muscle. In total, 45 TT genotype, 45 CT and 27 CC Black Angus crossbred steers were backgrounded on a commercial ration containing 3360 IU vitamin A/kg dry matter (DM). During finishing, the steers were randomly assigned to one of three vitamin A treatments at 25%, 50% and 75% of the National Research Council recommendation of 2200 IU/kg DM. Treatments were administered via an oral bolus. Carcass quality was evaluated and a sample from the LT muscle was collected for analysis of IMF. A treatment×genotype interaction (P=0.04) was observed for IMF; TT steers on the 75% treatment had higher IMF relative to CT and CC steers on the same treatment. Western blot analysis showed that TT steers had higher (P=0.02) ADH1C protein expression in hepatic tissue. Previously, TT steers exhibited increased IMF when fed limited vitamin A. In the current study, the lack of variation in IMF between treatments and genotypes at the lower vitamin A treatment levels was likely due to the majority of the steers grading Canada AAA (USDA Choice). However, the western blot data supports that TT steers are expected to have higher IMF deposition, due to an increased production of ADH1C. The interaction between ADH1C genotype and vitamin A supplementation level has the potential for use in marker-assisted management programs to target niche markets based on increased marbling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".