The rapid increase in high-throughput single-nucleotide polymorphism data has led to a great interest in applying genome-wide evaluation methods to identify an individual's genetic merit. Genome-wide evaluation combines statistical methods with genomic data to predict genetic values for complex traits. Considerable uncertainty currently exists in determining which genome-wide evaluation method is the most appropriate. We hypothesize that genome-wide methods deal differently with the genetic architecture of quantitative traits and genomes. A genomic linear method (GBLUP), and a genomic nonlinear Bayesian variable selection method (BayesB) are compared using stochastic simulation across three effective population sizes and a wide range of numbers of quantitative trait loci (N(QTL)). GBLUP had a constant accuracy, for a given heritability and sample size, regardless of N(QTL). BayesB had a higher accuracy than GBLUP when N(QTL) was low, but this advantage diminished as N(QTL) increased and when N(QTL) became large, GBLUP slightly outperformed BayesB. In addition, deterministic equations are extended to predict the accuracy of both methods and to estimate the number of independent chromosome segments (M(e)) and N(QTL). The predictions of accuracy and estimates of M(e) and N(QTL) were generally in good agreement with results from simulated data. We conclude that the relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on M(e,) which is a property of the target genome, as well as the architecture of the trait (N(QTL)).