[Impacts of SNP genotyping call rate and SNP genotyping error rate on imputation accuracy inHolsteincattle].

Single nucleotide polymorphism (SNP) chips have been widely used in genetic studies and breeding applications in animal and plant species. The quality of SNP genotypes is of paramount importance. More often than not, there are situations in which a number of genotypes may fail, requiring them to be imputed. There are also situations in which ungenotyped loci need to be imputed between different chips, or high-density genotypes need to be imputed based on low-density genotypes. Under these circumstances, the validity and reliability of subsequent data analyses is subject to the accuracy of these imputed genotypes. For justifying a better understanding of factors affecting imputation accuracy, in the present study, the impacts of SNP genotyping call rate and SNP genotyping error rate on the accuracy of genotype imputation were investigated under two scenarios in 20 116 U.S. Holstein cattle, each genotyped with a GGP 50K SNP chip. When the two factors were not correlated in scenario 1, simulated genotyping call rate varied from 50% to 100% and simulated genotyping error rate changed from 0% to 50%, with both factors being independent of each other. In scenario 2, genotyping error rates were correlated with genotyping call rate, and the relationship was set up by fitting a linear regression model between the two variables on a real dataset. That is, the simulated SNP call rate varied from 100% to 50% whereas the SNP genotyping rate changed from 0% to 13.55%. Finally, a 5-fold cross-validation was used to assess the subsequent imputation accuracy. The results showed that when original SNP genotyping call rate were independent of SNP genotyping error rate, the imputation accuracy did not change significantly with the original genotyping call rate (P>0.05), but it decreased significantly as the genotyping error rate increased (P<0.01). However, when original genotyping call rate was negatively correlated with genotyping error rate, the imputation error increased with elevated original genotyping error rate. In both scenarios, genotyping call rate needs to be no less than 0.90 in order to obtain 98% or higher genotype imputation accuracy. The present results can provide guidance for establishing quality assurance criteria for SNP genotyping in practice.

[Estimating genomic breed composition of individual animals using selected SNPs].

Natural and artificial selection, geographical segregation and genetic drift can result in differentiation of allelic frequencies of single nucleotide polymorphism (SNP) at many loci in the animal genome. For individuals whose ancestors originated from different populations, their genetic compositions exhibit multiple components correlated with the genotypes or allele frequencies of these breeds or populations. Therefore, by using an appropriate statistical method, one can estimate the genomic contribution of each breed (ancestor) to the genome of each individual animal, which is referred to as the genomic breed composition (GBC). This paper reviews the principles, statistical methods and steps for estimating GBC of individual animals using SNP genotype data. Based on a linear regression model and an admixture model respectively, the protocols were demonstrated by the breed characterization of 198 purported Akaushi cattle, which included selection of reference SNPs and reference individual animals, and computing GBC for animals to be evaluated. The reference populations consist of 36 574 cattle from five cattle breeds (Akaushi, Angus, Hereford, Holstein and Jersey), each genotyped on either a 40K or 50K SNP chip. Four common SNP panels scanned from commercial chips for estimating GBC of individual animals are optimally selected, thereby expanding the functionalities of the currently available commercial SNP chips. It remains to be explored in future studies as to how estimated GBC can be incorporated to improve the accuracies on genomic prediction in purebred animals and crossbreds as well.