An important problem in genomic selection in livestock is the cost of genotyping. Genotype imputation is a process of predicting unknown genotypes or un-typed Single nucleotide polymorphism (SNP), which uses reference population to predict missing genotypes for animal genetic variations. Support vector machines are algorithms based machine learning methods. We compared the Support Vector Machines (SVMs) and Beagle software for Genotype imputation in parent-offspring trios in term of imputation accuracy and computation of time. The methods employed uses simulated data (1000 trios with 10k SNPs) to impute the missing SNPs in parent-offspring trios. The genome consists of 5 chromosomes and each chromosome was set as 100 CM length. For simulated dataset five versions: NA10, NA30, NA50, NA70 and NA 90, were created (10, 30, 50, 70 and 90 percent of offspring genotypes are missing). Our results show that in all versions of simulated dataset Beagle outperformed SVM in term of imputation accuracy and computation of time. The Beagle requires almost no tuning and can easily handle missing predictor genotypes. We conclude to use of SVM in larger Sample size (i.e 10000) for imputation of parent-offspring trios.