Identification of potential candidate genes associated with milk protein differences in Holstein cows: a meta-analysis integrating GWAS and RNA-Seq transcriptome
Bibliographic record
Abstract
Despite the identification of candidate genes influencing milk protein, the connections between genes and regulatory pathways remains elusive. This study aimed integrate findings from genome-wide association studies (GWAS) and RNA sequencing (RNA-Seq) through meta-analysis to pinpoint single nucleotide polymorphisms (SNPs) and genes responsible for high and low protein yield in cows. Previous GWAS and RNA-Seq analyses had identified 663 SNPs and 1106 genes ( P < 0.05). Twenty SNPs from GWAS, 10 genes from RNA-Seq, and 49 SNP/gene associations from both datasets, were identified using meta-analysis. Meta-analysis validated several SNPs previously identified through GWAS, such as rs135549651 ( P = 2.6 × 10−256), rs109146371 ( P = 3.1 × 10−208), rs109350371 ( P = 4.0 × 10−207), and rs109774038 ( P = 8.6 × 10−587). Genes identified in RNA-Seq experiments, including NR4A1 ( P = 3.2 × 10−7), ATF3 ( P = 9.6 × 10−7), CDH16 ( P = 9.9 × 10−7), VEGFA ( P = 1.0 × 10−6), and SAA3 ( P = 7.3 × 10−11), were confirmed. The combined GWAS and RNA-Seq datasets highlighted CCND2 ( P = 8.9 × 10−111), MAPK15 ( P = 1.3 × 10−151), and CPSF1 ( P = 1.2 × 10−306) as the most significant genes. Additionally, significant gene ontology (GO) terms, including ionizing radiation ( P = 1.5 × 10−4), nuclear pore cytoplasmic filaments ( P = 9.4 × 10−5), and phenylalanine 4-monooxygenase activity ( P = 1.4 × 10−5), were identified. In conclusion, the integration of GWAS and RNA-Seq, coupled with GO enrichment, allowed identification of candidate SNPs and genes with higher accuracy. These findings improve our knowledge about genomic architecture of milk protein and enhance evaluation of Holstein cows.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".