Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing

Daisuke Sekine; Mai Tsuda; Shiori Yabe; Takehiko Shimizu; Kayo Machita; Masayasu Saruta; Tetsuya Yamada; Masao Ishimoto; Hiroyoshi Iwata; Akito Kaga

doi:10.3389/fpls.2021.729645

Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing

Front Plant Sci. 2021 Sep 1:12:729645. doi: 10.3389/fpls.2021.729645. eCollection 2021.

Authors

Daisuke Sekine^{1

2}, Mai Tsuda^{2

3}, Shiori Yabe², Takehiko Shimizu², Kayo Machita², Masayasu Saruta², Tetsuya Yamada², Masao Ishimoto², Hiroyoshi Iwata⁴, Akito Kaga²

Affiliations

¹ Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization, Tsu, Japan.
² Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan.
³ Tsukuba Plant Innovation Research Center, University of Tsukuba, Tsukuba, Japan.
⁴ Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Japan.

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.

Keywords: genomic prediction; genomic selection; marker-assisted recurrent selection; quantitative trait; seed protein content; segregation simulation; soybean.