Integrating landscape genomics and machine learning to analyze the genetic structure and specific molecular markers...

During long-term artificial selection, China has developed indigenous dairy goat populations with breed-specific traits and environmental adaptability. However, the genetic relationships and genomic characteristics of these populations remain insufficiently studied.

We analyzed 270 whole-genome sequences from 16 goat breeds, including eight Chinese dairy breeds, four introduced dairy breeds, and four other breeds. Population structure analysis revealed significant genetic differentiation among Chinese, European, and African dairy goats. Different dairy goat breeds in China formed geographically distinct subclusters, and multiple gene flow events with introduced breeds were detected. Compared with European and African populations, Chinese dairy goats exhibited higher genetic diversity, lower genomic inbreeding levels, and faster linkage disequilibrium decay. Among five machine learning algorithms evaluated across 10 repeated validations, k-nearest neighbors (KNN) showed the best performance (accuracy = 0.981, AUC = 0.991), and a minimal 134-SNP panel was selected for accurate classification of Chinese dairy goat breeds. Genome-environment association analyses identified candidate genes associated with energy and lipid metabolism, stress response, reproduction, and immune function, with a missense variant in HDAC10 (chr5:118557145 A > G) showing significant differentiation among Chinese dairy goat populations from different regions. Selection signature and genome-wide association analyses identified candidate genes associated with horn type and coat color, including two missense mutations in ERG (chr1:149921788 C > G) and MITF (chr22:31686241 T > C).

These findings systematically reveal the genetic structure and genomic characteristics of Chinese dairy goats, providing an important scientific basis for genetic improvement, breed identification, and the conservation of dairy goat genetic resources.