<i>Toll-like receptor</i> 4 gene polymorphisms influence milk production traits in Chinese Holstein cows
Bibliographic record
Abstract
The research reported in this Research Communication aimed to describe the influence of Toll-like receptor 4 gene polymorphisms on milk production traits in Chinese Holstein cows. Toll-like receptor 4 (TLR4) is an important member of the toll-like receptor gene family that is widely found in various organisms. Since TLR4 can identify molecular patterns from various pathogenic microorganisms and induce natural and acquired immunity, it plays an important role in disease resistance in dairy cows. Two single nucleotide polymorphisms (SNPs) of TLR4 (c.-226 G > C and c.2021 C > T) that were previously found to be associated with health traits were genotyped using Sequenom MassARRAY (Sequenom Inc., San Diego, CA) for Chinese Holstein cows (n = 866). The associations between SNPs or their haplotypes and milk production traits and somatic cell count were analyzed by the generalized linear model procedure of Statistics Analysis System software (SAS). The c.-226 G > C and c.2021 C > T showed low linkage disequilibrium (r2 = 0·192). There was no association between these two SNPs and SCC, but significant effects were found for SNP c.-226 G > C on test-day milk yield, fat content, protein content, and total solid and milk urea nitrogen (P T and the SNP haplotypes on test-day milk yield, fat content, protein content, lactose content and total solids (P C was located within several potential transcription factor binding sites, including transcription factor AP-2. The polymorphisms c.-226 G > C and c.2021 C > T had significant effects on the milk production for Chinese Holstein, and these SNP could be used for molecular marker-assisted selection of milk production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".