Genome-wide association study on dairy goat milk production traits using three models.

Identifying genetic markers associated with economically important traits in dairy goats helps enhance breeding efficiency, thereby increasing industry value. However, the potential genetic structure of key economic traits in dairy goats is still largely unknown. This study used three genome-wide association study (GWAS) models (GLM, MLM, FarmCPU) to analyze dairy goat milk production traits (milk yield, fat percentage, protein percentage, lactose percentage, ash percentage, total dry matter, and somatic cell count). The goal was to identify SNPs and positional and functional candidate genes significantly associated with these traits. The GWAS analysis results identified a total of 242 significant SNPs. Among these, 45 SNPs exhibited genome-wide significance, while 197 SNPs demonstrated suggestive associations, corresponding to 99 positional candidate genes within a 50 kb upstream and downstream range. 15 significant SNP loci were consistently identified across all three models, corresponding to 18 candidate genes.The integrated analysis of three models detected 2, 19, 17, 4, 115, 23, and 62 significant SNPs associated with milk yield, ash percentage, protein percentage, lactose percentage, somatic cell count, fat percentage, and total dry matter percentage, respectively. Correspondingly, 6, 24, 9, 12, 37, 14, and 30 candidate genes were identified for these traits. Additionally, several new candidate genes related to milk production traits were proposed (LCORL, TNFRSF1A, VWF, SPATA6, MAN1C1, MASP1, BRCA2). In summary, the results of this study provide an important reference for further exploration of the genetic mechanisms underlying dairy goat milk production traits and the development of molecular breeding markers.