1Herein we report the impacts of applying five selection methods across 40 cycles of recurrent 2 selection and identify interactions with other factors on genetic response using simulated families 3 of recombinant inbred lines derived from 21 homozygous soybean lines used for the Soybean 4 Nested Association Mapping study. The other factors we investigated included the number of 5 quantitative trait loci, broad sense heritability on an entry mean basis, selection intensity, and 6 training sets. Both the rates of genetic improvement in the early cycles and limits to genetic 7 improvement in the later cycles are affected by interactions among the factors. All genomic 8 selection methods provided greater rates of genetic improvement (per cycle) than phenotypic 9 selection, but phenotypic selection provided the greatest long term responses. Model updating 10 significantly improved prediction accuracy and genetic response for three parametric genomic 11 prediction models. Ridge Regression, if updated with training sets consisting of data from prior 12 cycles, achieved greater rates of response relative to BayesB and Bayes LASSO GP models. A 13 Support Vector Machine method, with a radial basis kernel, resulted in lowest prediction 14 accuracies and the least long term genetic response. Application of genomic selection in a closed 15 breeding population of a self-pollinated crop such as soybean will need to consider the impact of 16 these factors on trade-offs between short term gains and conserving useful genetic diversity in 17 the context of goals for the breeding program.18 19 4 Background 20 Plant breeding programs consist of 1) recurrent genetic improvement projects, 2) variety 21 development projects 3) trait introgression projects and 4) product placement projects (Fehr, 22 1991). Genetic improvement is assessed using realized genetic gain, which is an estimate of 23 change of the average genotypic value for traits of interest across cycles of selection and inter-24