Genome-wide identification of Diacylglycerol Acyltransferases (DGAT) family genes influencing Milk production in Buffalo
Bibliographic record
Abstract
BACKGROUND: The diacylglycerol acyltransferases (DGAT) are a vital group of enzymes in catalyzing triacylglycerol biosynthesis. DGAT genes like DGAT1 and DGAT2, have been identified as two functional candidate genes affecting milk production traits, especially for fat content in milk. Buffalo milk is famous for its excellent quality, which is rich in fat and protein content. Therefore, this study aimed to characterize DGAT family genes in buffalo and to find candidate markers or DGAT genes influencing lactation performance. RESULTS: We performed a genome-wide study and identified eight DGAT genes in buffalo. All the DGAT genes classified into two distinct clades (DGAT1 and DGAT2 subfamily) based on their phylogenetic relationships and structural features. Chromosome localization displayed eight buffalo DGAT genes distributed on five chromosomes. Collinearity analysis revealed that the DGAT family genes were extensive homologous between buffalo and cattle. Afterward, we discovered genetic variants loci within the genomic regions that DGAT genes located in buffalo. Seven haplotype blocks were constructed and were associated with buffalo milk production traits. Single marker association analyses revealed four most significant single nucleotide polymorphisms (SNPs) mainly affecting milk protein percentage or milk fat yield in buffalo. Genes functional analysis indicated that these DGAT family genes could influence lactation performance in the mammal through regulating lipid metabolism. CONCLUSION: In the present study, we performed a comprehensive analysis for the DGAT family genes in buffalo, which including identification, structural characterization, phylogenetic classification, chromosomal distribution, collinearity analysis, association analysis, and functional analysis. These findings provide useful information for an in-depth study to determine the role of DGAT family gens play in the regulation of milk production and milk quality improvement in buffalo.
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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 | 0.001 | 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".