Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard SNP arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges and the accuracy of genomic prediction using GBS is currently under investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (Experiments 1 and 2). Given that GBS data come with a large percentage of un-called genotypes, we evaluated methods using non-imputed, imputed, and GBS-inferred haplotypes of different length (short or long). GBS and pedigree data were incorporated into statistical models using either the Genomic Best Linear Unbiased Predictors (GBLUP) or Reproducing Kernel Hilbert Spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. Results show: (i) relative to pedigree or marker-only models, consistent gains in prediction accuracy by combining pedigree and GBS data, (ii) increased predictive ability when using imputed or non-imputed GBS data over inferred haplotype in Experiment 1, or non-imputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in Experiment 2, (iii) the level of prediction accuracy achieved using GBS data in Experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays, and (iv) GBLUP and RKHS models with pedigree with non-imputed and imputed GBS data gave the best prediction correlations for the three traits in Experiment 1, whereas for Experiment 2, RKHS gave slightly better prediction than GBLUP for drought stressed environments, and both models gave similar predictions in well-watered environments.