eng Rovedar Small Animal Advances 2821-2363 2025-09-01 4 3 11 19 10.58803/saa.v4i3.36 38 Umar Aziz 0 https://orcid.org/0009-0009-6266-4340 Abdul Rehman 1 https://orcid.org/0009-0009-3221-2984 Muhammad Hanzalah Yousaf 2 https://orcid.org/0009-0005-7241-5701 Fasih Ur Rehman 3 https://orcid.org/0000-0002-7750-3453 Muhammad Mushahid 4 https://orcid.org/0000-0001-8670-3011 Nauman Khan 2 https://orcid.org/0009-0008-1289-6859 Jiayuan Li 2 Xugan Wang 2 Hanbing Yan 2 Xiaopeng An 2 NorthWest A&F UniversityDepartment of Animal Breeding, Genetics and Reproduction, College of Animal Science and Technology, Northwest A&F University, Yangling, China Faculty of Animal Production and Technology, Cholistan University of Veterinary & Animal Sciences, Punjab, Pakistan Department of Animal Breeding, Genetics and Reproduction, College of Animal Science and Technology, Northwest A&F University, Yangling, China Department of Parasitology, University of Veterinary and Animal Sciences, Lahore, Pakistan Institute of Animal and Dairy Sciences, University of Agriculture Faisalabad, Constituent College Toba Tek Singh, Toba Tek Singh, Punjab, Pakistan Introduction: Goat milk production is a vital economic trait, driven by rising global demand due to its digestibility, nutritional benefits, and hypoallergenic properties. To explore the genetic basis of milk yield, a genome-wide association study (GWAS) was conducted using whole-genome sequencing (WGS) data from two dairy goat breeds, Guanzhong (China) and Beetal (Pakistan). The present study aimed to identify genomic variants linked to milk yield in Guanzhong (China) and Beetal (Pakistan) goat breeds by performing an in silico GWAS using available WGS data. Materials and methods: Raw sequencing reads from both breeds were retrieved from public repositories and processed through an established bioinformatics pipeline. A GWAS was performed using a linear mixed model with GCTA and GEMMA, accounting for population structure and polygenic background. After quality control and alignment to the ARS1 goat reference genome using BWA, single-nucleotide polymorphisms (SNPs) were identified, and variants were filtered using SAMtools/BCFtools by applying thresholds of minor allele frequency (> 5%) and genotype call rate (> 90%). Population structure was assessed through principal component analysis and a genomic kinship matrix, both conducted using GCTA software, to control for stratification. The Manhattan plot revealed several genome-wide significant peaks, including loci near LALBA, PRLR, and SPP1, which are associated with lactation traits in dairy goats. Results: The GWAS revealed significant SNPs near LALBA on chromosome 19 (p = 1 × 10-¹⁰) and PRLR on chromosome X (p = 3.2 × 10-⁹) strongly associated with milk yield in Guanzhong and Beetal goats. In Guanzhong goats, SNPs near ANPEP, ADRA1A, and PRKG1 exhibited significant allele frequency differences, while in Beetal goats, SNPs near IGFBP3 and LEPR were linked to lactation traits. These loci provided robust genomic markers for enhancing the dairy goat breeding program. Conclusion: The present study demonstrated the feasibility of WGS-based GWAS in goats and identified candidate loci such as SPP1, ERBB4, and LALBA, previously linked to lactation, that may serve as genomic markers in future selection programs. https://saa.rovedar.com/index.php/SAA/article/view/36 Beetal Genome-wide association study Goat milk Guanzhong dairy goat Kinship matrix