Selection of SNP from 50K and 777K arrays to predict breed of origin in cattle1

Abstract

Reliable breed assignment can be performed with SNP. Currently, high density SNP chips are available with large numbers of SNP from which the most informative SNP can be selected for breed assignment. Several methods have been published to select the most informative SNP to distinguish among breeds. In this study, we evaluated Delta, Wright's FST, and Weir and Cockerham's FST, and extended these methods by adding a rule to avoid selection of sets of SNP in high linkage disequilibrium (LD) providing the same information. The SNP that had a r2 value>0.3 with any of the SNP already selected were discarded. The different selection methods were evaluated for both the 50K SNP and 777K Bovine BeadChip. Animals from 4 cattle breeds (989 Holstein Friesian, 97 Groningen White headed, 137 Meuse-Rhine-Yssel, and 64 Dutch Friesian) were genotyped. After editing 30,447 and 452,525 SNP were available for the 50K and 777K SNP chip, respectively. All selection methods showed that only a small set of SNP is needed to differentiate among the 4 Dutch cattle breeds, whereas comparison of the selection methods showed only small differences. In general, the 777K performed marginally better than the 50K BeadChip, especially at higher confidence thresholds. The rule to avoid selection of SNP in high LD reduced the required number of SNP to achieve correct breed assignment. The Global Weir and Cockerham's FST performed marginally better than other selection methods. There was little overlap in the SNP selected from the 2 BeadChips, whereas the number of SNP selected was about the same.

Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds

“…In fact, where there is sufficient genetic heterogeneity among populations, a few genetic markers can be easily used to identify and verify the origin of individuals, whereas it becomes more complicated for population with low genetic differentiation, such as the sheep breeds involved in this study (Mastrangelo et al, 2012;Tolone et al, 2012). It is well known that a high number of genotyped animals can capture the whole within population variability reducing the possibility that some individuals would not be assigned correctly due to atypical genotypes (Hulsegge et al, 2013). Considering the high level of admixture among these sheep breeds , and the relative low number of analysed individuals, our study reported relevant results.…”

Combined use of principal component analysis and random forests identify population‐informative single nucleotide polymorphisms: application in cattle breeds

“…These SNPs with high LD were derived by the PCA selection procedure that selected quite a high number of SNPs in high LD (data not shown). This is a problem that has been already observed in PCA selected SNPs (Paschou et al 2007;Hulsegge et al 2013). However, RFs procedures reduced a lot the number of these SNPs (only 6 of 96 SNPs were in LD with another one of this panel), suggesting that a preselection step of the SNPs based on the value of r 2 could be superfluous.…”

Conservation priorities for the different lines of Dutch Red and White Friesian cattle change when relationships with other breeds are taken into account

“…Previously performed editing steps to remove uninformative SNP are described by Hulsegge et al . (). In short, Holstein Friesian animals were genotyped with a BovineSNP50 BeadChip and imputed to the BovineHD BeadChip using Beagle (Browning & Browning ).…”

“…These data included 1287 purebred cows; 989 were Holstein Friesian (HF), 97 Groningen White headed (GWH), 137 Meuse-Rhine-Yssel (MRY) and 64 Dutch Friesian (DF). Previously performed editing steps to remove uninformative SNP are described by Hulsegge et al (2013). In short, Holstein Friesian animals were genotyped with a BovineSNP50 BeadChip and imputed to the BovineHD BeadChip using Beagle (Browning & Browning 2009).…”