Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean

Increasing the rate of genetic gain for seed yield remains the primary breeding objective in both public and private soybean breeding programs. Genomic selection (GS) has the potential to accelerate the rate of genetic gain for soybean seed yield. To date, limited studies have empirically validated accuracy of GS and compared to phenotypic selection (PS), and none has been done for soybean breeding. This study conducted the first empirical validation of GS for increasing seed yield using over 1,500 lines and over 7 years (2010-2016) of replicated experiments in the University of Nebraska soybean breeding program. The study was designed to capture the varying genetic relatedness of the training population to three validation sets: two large bi-parental populations (TBP-1 and TBP-2), and a large validation set comprised of 457 pre-selected advanced lines derived from 45 bi-parental populations in the variety development program (TMP). We found that prediction accuracy (0.54) from our validation experiments was competitive with what we obtained from a series of cross-validation experiments (0.64). Both GS and PS were more effective for increasing population mean performance with similar realized gain but significantly greater than random selection (RS). We found a selection advantage of GS over PS where higher genetic gain and identification of top-performing lines was maximized at higher selection stringency from 10 to 20% selected proportion. GS led to at least 2% increase in the mean genetic similarity vs. PS and RS, potentially causing a minimal loss of genetic diversity. We showed that loss of genetic variance in the GS set was presumably due to a significant shift on allelic frequencies towards the extremes. Across all loci, an average increase of 0.04 in allelic frequency in the GS set was observed after selection, which is about 5% higher than the base population when no selection was made. Overall, we demonstrate that GS performed as effectively as PS, and the implementation of GS in a public soybean breeding program should be warranted mainly for reducing breeding cycle time and lowering cost per unit gain.