Analysis of SNPs in the KIT Gene of Cattle with Different Coat Colour Patterns and Perspectives to Use These Markers for Breed Traceability and Authentication of Beef and Dairy Products

摘要

Abstract The identification of the breed of origin of farm animals has recently assumed particular relevance, since increasing interests in marketing mono-breed labelled lines of beef and dairy products have in fact created the need to protect them from frauds. In order to develop DNA based breed traceability and authentication protocols, the first step is the identification of breed specific markers with high discriminatory power among breeds. We analysed two single nucleotide polymorphisms (SNP) identified in exon 2 (g.72779776C>T) and exon 3 (g.72783182A>G) of the KIT gene (a candidate gene for the spotting locus ) in seven cattle breeds with different coat colour patterns (Italian Holstein-Friesian, no. = 61; Italian Brown, no. = 60; Italian Simmental, no. = 78; Jersey, no. = 60; Rendena, no. = 51; Reggiana, no. = 128; and Modenese, no. = 52). The two alleles of both SNPs were detected in all analysed breeds making their use unsuitable in breed traceabilty with a deterministic approach. Italian Simmental was almost fixed for the most common alleles (g.72779776C and g.72783182A). Haplotype analysis showed that spotted breeds (Italian Holstein-Friesian and Italian Simmental) had only two haplotypes, of which one ([C:A]) with high frequency (～90% and ～99%, respectively). Analysis of molecular variance (AMOVA) averaged over the two markers indicated that genetic variation between spotted and non-spotted groups of breeds amounted to 25.3% (P KIT gene in influencing the spotted phenotype, but probably not determining it, as we previously suggested. Pairwise F_st values indicated significant differences among almost all pair of investigated breeds. The high discriminatory power of the analysed SNPs is an important characteristic for the inclusion of these markers in SNP panels useful for breed allocation and traceability based on probabilistic approaches.