Abstract
In this study, we conducted simulations of a common bean (Phaseolus vulgaris) breeding program to better understand the interplay between different choices a breeder must make when launching a genomic selection (GS) pipeline. GS uses dense marker data to estimate breeding values of selection candidates in a crop breeding program. We complement preceding studies focused on optimizing model parameters and training set makeup by exploring the practical implementation of GS in a common bean breeding program aimed at increasing seed yield. We simulated 24 GS implementation pathways, focusing on (1) what generation to train a new prediction model, (2) what generation to select parents for the next cycle, (3) which generation to collect training data, and (4) whether to use linear regression or a nonparametric model for estimating breeding values (BVs). We found that early-generation parent selections (also called rapid-cycle genomic selection) generally resulted in higher gain over three breeding cycles compared to late-generation parent selections. When it comes to implementing a new parametric genomic prediction model, we found that training data should be as diverse as possible, while also matching testing data in terms of genetic makeup and allele frequency. Parametric models showed more consistent GEBV prediction accuracy, while nonparametric models fluctuated, showing both the highest and the lowest prediction accuracy across all pathways. While there is typically a trade-off between high gains and genetic variance, nonparametric models showed greater balance of allelic diversity and gains. This indicates a potential for their use, but more investigation will be required to stabilize their performance. Employing more robust training sets accumulated over time, or developing more tailored and informative model architectures may help to stabilize the performance of nonparametric models. We observed that the key to sustained gains over time is the renewal of genetic variance, which can be accomplished by making crosses within the existing breeding program germplasm.