Unravelling Population Structures and Selection Signatures for milk production and adaptive traits in Genetically Admixed Population of Sub-Saharan African Cattle

Abstract Sub-Saharan African cattle breeds such as Kuri, N’Dama, and Bororo represent unique genetic resources with distinct adaptive traits and production potentials. Understanding their genomic diversity and population structure is critical for conservation and genetic improvement. In this study, we analysed high-density SNP genotyping data to investigate population structure, breed classification and signatures of selection for milk production and adaptation of these cattle. Random forest classification identified twenty highly informative SNPs for breed differentiation and revealed traceability and assignment of the breeds. Principal component analysis (PCA), admixture, and neighbour-joining (NJ) trees revealed distinct clustering patterns, consistent with the breeds’ evolutionary histories. Manhattan plot illustration, F ST and gene enrichment analyses confirmed overlaps with known milk production and adaptation QTLs. Manhattan plots and subsequent annotation of candidate regions identified genes associated with both milk production ( APP, CHODL, ROBO1, EPHA3, GAP43, KALRN, EPHB1, BRWD1, DSCAM and COL18A1 ) and adaptive and other traits ( PDCD10, TBC1D5, CHN1, RAPGEF4, ERBB4, DOCK10, DDR2, ROR1, NGEF and AGAP1 ). In conclusion, random forest, a machine learning approach and breed informative markers could be used for breed traceability, assignment and assessment of genetic diversity meant for cattle breed conservation and improvement. Therefore, this finding provide new insights into the genomic architecture of African cattle and support strategies for their sustainable management and utilization.