Detection of Selection Signals in Cattle Populations by Pca

Abstract

The presented study provides a genome-wide scan of selection signals in cattle by principal component analysis (PCA). The aim was to identify SNP affected by intensive selection based on package PCAdapt implemented under software R. This analysis provided insight into the association between the SNP frequencies related to population differentiation. The four cattle populations were involved in the analysis (Slovak Spotted cattle, Ayrshire, Swiss Simmental and Holstein) with overall 272 of genotyped individuals. After applying quality control, the final dataset consisted of 35 675 SNPs, with an overall length of 2496.14 Mb and average space between adjacent SNP 70.03 ± 76.1 kb. After performing PCA analysis, the uniqueness of the breeds was revealed. On the other hand, a close genetic relationship and eleven SNPs affected by selection were found, with a position close to 162 genes involved in the various biological processes. The majority of genes were involved in the positive regulation of adenylate cyclase activity, embryo development and somatic diversification of immune receptors via somatic mutation. Several candidate genes for genetic control of the immune system (DNAJB9), muscle development (SEPT7, TRIM32, ROCK1, NRAP, PZDZ8, HSPA12A and FGFR2), milk production (SOCS5, CD46), reproduction (LHCGR, EEPD1, FSHR) and coat colour (KIT) were identified. Our results provide insights into the regions of the genome affected by the intensive selection of analysed cattle populations.

Genome-wide assessment of runs of homozygosity in Sahiwal cattle of Pakistan

“…ROH can produce accurate evaluations of genomic autozygosity and allows one to estimate the genomic inbreeding coefficient (F ROH ) [13,14]. Estimates of F ROH can be made for any animal with genotypic data and provide a more accurate prediction of the genome and the actual degree of autozygosity [15,16].…”