Impact of Genomic Prediction Model, Selection Intensity, and Breeding Strategy on the Long-Term Genetic Gain and Genetic Erosion in Soybean Breeding - Silva, Éder David Borges da; Xavier, Alencar; Faria, Marcos Ventura (Frontiers in Genetics, 2021)
Genomic assisted breeding has become an important tool in soybean breeding. However, the impact of different genomic selection approaches on short- and long-term gain is not well understood. Such gains are conditional on the breeding design and may vary with a combination of the prediction model, family size, selection strategies, and selection intensity. To address these open questions, we evaluated various scenarios through a simulated closed soybean breeding program over 200 breeding cycles. The genomic prediction was performed using GBLUP, Bayesian methods and Random Forest, benchmarked against selection on phenotypic values, true breeding values, and random selection. Breeding strategies included selections within-family (WF), across-family (AF), and within pre-selected families (WPSF), with selection intensities of 2.5, 5.0, 7.5, and 10.0%. Selections were performed at F4 generation, where individuals were phenotyped and genotyped with a 6K SNP array. Initial genetic parameters for the simulation were estimated from the SoyNAM population. WF selections provided the most significant long-term genetic gains. GBLUP and Bayesian methods outperformed random forest and provided most of the genetic gains within the first 100 generations, being outperformed by phenotypic selection after generation 100. All methods provided similar performance under WPSF selections. Faster decay in genetic variance was observed when individuals were selected AF and WPSF, as 80% of the genetic variance was depleted within 28 to 58 cycles, whereas WF selections preserved the variance up to cycle 184. Surprisingly, the selection intensity had less impact on long-term gains than breeding strategies. The study supports that genetic gains can be optimized in the long-term with specific combinations of prediction models, family size, selection strategies, and selection intensity. A combination of strategies may be necessary for balancing short-, medium- and long-term genetic gains in breeding programs while preserving genetic variance.