A large number of genome-wide association studies (GWAS) in livestock, especially in dairy cows, provide favorable conditions to integrate multiple independent studies. Methods such as meta-analysis provide the identification of effective QTLs with higher precision and power. A meta-analysis for milk production traits between different countries was conducted using the GWAS summary statistics (i.e., P-value, sample size, allele effects, and etc.) in Holstein cows. In the present study, METAL software was used for the weighted Z-score model. Gene network analysis was used as a complementary method to improve our knowledge of the genome structure of milk production traits and was implemented through the STRING plug-in in Cytoscape software. The Cytoscape ClueGO plug-in was also used for GO enrichment in order to identify biological process, cellular component, and molecular function associated with genomic regions. The aim of this study was to improve the power of QTLs detection and identify the biological mechanisms associated with milk production traits. Data were obtained from 26 published studies from 2010 to 2019. A total of 2,072 SNPs were identified for milk production traits, of which 1,583 SNPs were significant (). Meta-analysis identified 9 QTLs for milk yield, 36 QTLs for fat percentage, and 10 QTLs for protein percentage. Some QTLs were confirmed on BTA14, e.g., BTA14:1801116 close to the DGAT1 gene (milk yield, ; fat percentage, ; protein percentage, ) and BTA14:1651311 close to the PPP1R16A gene (milk yield, ; fat percentage, ). We identified pleiotropic effects of lead SNPs for milk production traits, e.g., one SNP (rs109421300) at BTA14 had pleiotropic effects on milk yield, fat percentage, and protein percentage traits. The most important SNPs for studied traits across countries implicated to network scoring and visualization were including: rs109421300 (DGAT1 gene) for milk yield, fat percentage, and protein percentage; rs109146371 (PPP1R16A gene) for milk yield and fat percentage; rs109968515 (CYHR1 gene) for milk yield and fat percentage; rs134432442 (CPSF1 gene) for fat percentage; rs111018678 (TRAPPC9 gene) for protein percentage. Significant pathways involved in milk production traits through GO term enrichment analysis for biological process, cellular component, and molecular function included: regulation of cation channel activity (), ion channel complex (), and phosphoric diester hydrolase activity () for milk yield; negative regulation of organ growth (), transmembrane transporter complex (), and potassium ion transmembrane transporter activity () for fat percentage; mRNA polyadenylation (), mRNA cleavage factor complex (), and phosphoric diester hydrolase activity () for protein percentage, respectively. Thus, the combination of GWAS summary statistics through a powerful methodology such as meta-analysis will assist us to accurately identify QTLs, potential candidate genes, and biological mechanisms. This kind of studies will help us to have better understanding of QTL regions and genome structure for milk production traits and improve genomic evaluations in Holstein cows. To the best of our knowledge, this is the first meta-analysis of GWAS and GO enrichment across countries for milk production traits in Holstein cows.

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