Assessing footprints of natural selection through PCA analysis in cattle

Nina Moravčíková, Veronika Kukučková, Gábor Mészáros, Johann Sölkner, Ondrej Kadlečík, Radovan Kasarda


Received: 2016-05-24 | Accepted: 2016-07-28 | Available online: 2017-06-20

The aim of this study was to determine the population structure and to perform genome-wide scan of footprints of natural selection in cattle using principal component analysis. The applied statistics to identify the SNPs associated with selection pressure focused mainly on the extreme values of FST index. In our study the alternative individual-based approach adopted in the PCAdapt R package has been used. This approach is based on the assumption that markers extremely related to the population structure are also candidates for local adaptation of the population. The genotype data of 350 animals originating from four historically or geographically connected populations (Austrian Pinzgau, Slovak Pinzgau, Brown Swiss, Tyrol Grey) have been used to test this approach in cattle. As expected based on breed's origin the principal component analysis showed the division of animals in to the 3 separate clusters and the eigenvalues suggested to use of K=3 as optimal number. The analysis of genomic regions harbouring signals revealed the candidate genes previously associated with muscle formation and immunity system. Detecting signals of adaptation that were also the targets of historical selection will allow in the future a better understanding of cattle origin.

Keywords: local adaptation, selection, cattle, SNP50 BeadChip, PCAdapt, population subdivision


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