Genome-Wide Identification of Natural Selection Footprints in Bos Indicus Using Principal Component Analysis

Background: To describe natural selection, numerous analytical methods for ascertaining candidate genomic region have been developed. There is a substantial drive in population genomics to identify loci intricate in local adaptation. A potent method to find genomic regions subject to local adaptation is to genotype numerous molecular markers and look for outlier loci. Methods: In this study, population structure and genome wide footprints scan of natural selection in cattle was performed using principal component analysis based on alternative individual method assumed in the PCAdapt R-package. This method was used on the hypothesis that extremely related population markers are also local population adaptation candidates. To test PCAdapt method in cattle, the data of sixty three animals were collected from four different origins or agro-ecological zones (Achai = 18, Cholistani = 13, Lohani = 19, and Tharparkar = 13) and genotyped using the high density SNPs BeadChip. Results: As expected from the sampling from different zones the principal component result indicated the clear division in these animals into three clusters. K= 3 was the optimal number suggested by eigenvalues. Conclusion: The result of this study revealed that the genomic regions harboring signals of the candidate genes were associated with immunity system and muscle formation. Signature of selection detecting in this study targeted the historical adaptation in these breeds that will be useful in future to understand cattle origin under different environment.