Genomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs.

Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F₁ or F₂ stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F₁ intercross (F_1i) families and keeping the total number of progeny of F_1i families constant, we observed a decrease in genetic gain and increase in genetic diversity, whereas increasing the number of progeny per F_1i family while keeping a constant number of F_1i families increased the rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F₂ family phenotypes to the training population increased the accuracy of genomic breeding values (GEBVs) and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.