Identification of candidate gene networks affecting the number of somatic cells count and milk production in Iranian Holstein cows using Genome-wide association study
Bibliographic record
Abstract
One of the most powerful tools for identifying genomic regions associated with various phenotypes is GWAS. Identifying genes influencing milk production traits in Iranian Holstein dairy cows is crucial to understanding the genetic mechanisms underlying these traits and improving future milk production. Therefore, using a single-step GWAS, this study aimed to identify genomic regions, genes, and pathways associated with milk yield (MY), milk fat percentage (FP), milk protein percentage (PP), and somatic cell count (SCC) traits in the Iranian Holstein cattle population. In this study, 210 animals were genotyped using 30K (150 animals from Herd 1) and 50K (60 animals from Herd 2) SNP arrays. Genotypes were then imputed to whole-genome sequence level using the 1000 Bull Genomes Project reference panel, resulting in 6,583,595 high-confidence imputed SNPs forGWAS analysis. Genomic regions associated with milk production traits included 184 significant SNP markers (milk yield, milk fat, milk protein, and somatic cell count, with 86, 18, 22, and 58 significant SNP markers, respectively) based on a significance threshold of P value < 1 × 10⁻⁸ across 10 chromosomes (2, 5, 7, 17, 19, 21, 24, 26, and 28). For the traits FP, PP, MY, and SCS, 5, 6, 9, and 7 candidate genes were identified near the significant SNPs, respectively. Key genes with important biological roles included ATE1, FGFR2, ALDH1A3, CHSY1, GABRG3, FBXO36, PID1, TRIP12, CD52, WDTC1, MATN1, CIDEA, LYZ, CPM, FBXO42, MAML3, SGMS2, HADH, CYP2U1, SCLT1 and THRSP. Therefore, the ATE1, FGFR2, and LYZ genes is not only a key marker for udder health and milk quality but also a promising candidate for genomic selection and therapeutic applications aimed at improving disease resistance in dairy herds. Our research led to the discovery of novel SNPs linked to milk production traits, which could be valuable for future livestock breeding programs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".