Identification of divergently selected regions between Japanese Black and Holstein cattle using bovine 50k SNP array

Abstract

Differences between average allelic frequencies of genes that relate to traits suggest that it would be evidence of artificial selections. Sliding window approach is a useful method to identify genomic regions that have been differently selected between two breeds. The objective of this study was to identify the divergently selected regions between Japanese Black (JB) and Japanese Holstein (JH) cattle based on genotypic information obtained through a high-density single nucleotide polymorphism (SNP) panel. After genotyping of 54 001 SNP markers on 100 animals (50 JB and 50 JH), 40 635 SNPs were suitable for the analysis. For each of these SNPs, the absolute difference between allelic frequencies of JB and JH was calculated. In the current study, 10 consecutive SNPs were defined as components of a window. For each window, the average difference in allelic frequency was calculated. This was termed sliding window average difference (SWAD). Among 40 055 windows, we focused on 39 windows with the largest SWAD. This was equivalent to 0.1% of all windows and the SWAD was more than 0.435. Some of these windows overlapped and were distributed in 11 regions. These regions were in good agreement with reported quantitative trait locus, therefore would be selection signatures and good candidates that harbor the causative mutations.

Identification of differential selection traces in two Polish cattle breeds

“…To this end, we used the sliding window approach proposed by Hosokawa et al . () and Hayes et al . ().…”

“…; Hosokawa et al . ). This thesis is supported by findings stating that the frequency of polymorphisms in genes affecting milk production ( DGAT1 and ABCG2 ) is distinct in different cattle breeds (Kaupe et al .…”

Identification of selection signatures in livestock species

“…Within each group, several breeds have been developed, and there is considerable intra- and inter-group variability in productive (milk yield and quality, meat production), morphological (coat color, presence/absence of horns) and adaptive (disease resistance, heat tolerance) traits (The Bovine HapMap Consortium, 2009). Several genome-wide studies focusing on different approaches and using different sets of breeds have sought for selection signatures in bovines (Prasad et al , 2008; Barendse et al , 2009; Flori et al , 2009; Gautier et al , 2009; Hayes et al , 2009; MacEachern et al , 2009; The Bovine HapMap Consortium, 2009; Li et al , 2010a; Qanbari et al , 2010, 2011; Stella et al , 2010; Wiener et al , 2011; Hosokawa et al , 2012). …”

“…The increase in allele frequency differences between meat and dairy cattle and the high linkage disequilibrium in dairy breeds (using EHH and iHS methods) suggest that the region surrounding DGAT1 is under selection (Hayes et al , 2009; Qanbari et al , 2010; Hosokawa et al , 2012; Schwarzenbacher et al , 2012). This gene is suggested to be responsible for a QTL with a major effect on milk fat percentage (Grisart et al , 2002; Khatkar et al , 2004; Cole et al , 2009; Hayes et al , 2010; Jiang et al , 2010).…”

“…This gene has been implicated in mammary gland development and is associated with milk, fat and protein yield in Holstein cattle (Cobanoglu et al , 2006). Two studies comparing allele frequency differences between beef and dairy cattle suggested a selection signal in the region surrounding this gene (Hayes et al , 2009; Hosokawa et al , 2012). …”