Abstract
Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.
Highlights
Plant breeding has played a crucial role in the development of human societies
No difference was observed between the maximum GV (MGV) of proposed selection strategy with single cross (S1) and conventional recurrent selection strategy with single cross (P1) when quantitative trait loci (QTL) = 30 and h2 = 0.3 (Figure 2A), whereas S1 was significantly higher than P1 when QTL = 30 and h2 = 0.6 or 1.0 (Figures 2C,E; Supplementary Tables 3, 4)
MGVs for the single cross strategies (S1 and P1) were significantly lower than in the multiple cross strategies, i.e., S5, proposed selection strategy with multiple crosses (S10), and P5, conventional recurrent selection strategy with multiple crosses (P10) during selection cycles when QTL = 30 and h2 = 0.3 (Figure 2A; Supplementary Tables 3, 4), while no significant difference was observed between S1 and any of the multiple crosses strategies based on prediction values P5 or P10 when QTL = 30 and h2 = 0.6 or 1.0 (Figures 2C,E; Supplementary Tables 3, 4), except for the difference between S1 and P10 when QTL = 30 and h2 = 1.0 in later cycles (Figure 2E; Supplementary Tables 3, 4)
Summary
Plant breeding has played a crucial role in the development of human societies. The demands of the increasing world population could be met by improving yield and nutrient content in self-pollinating crops such as rice, wheat, and soybean, which account for a large part of the human food supply (FAO, 2015), through breeding better varieties (Tester and Langridge, 2010). Simulation-Based Selection in Soybean programs for self-pollinated crops typically use the bulk population method, where a segregating population is generated and repeatedly selfed over several generations without selection, followed by the selection of genetically fixed plants with favorable traits (Brown and Caligari, 2008). Important agronomic traits such as yield and nutrient content are known to be controlled by multiple genes, which are defined as quantitative trait loci (QTLs). These approaches aim to increase the frequency of multiple favored QTLs in a population (Bernardo, 2008)
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