Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Abstract

Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59-0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.

Keywords: common bean germplasm; genomic prediction; population structure.

Figure 1

A workflow for genomic prediction of common bean germplasm. The above three ways are represented by the flow of the three numbers (1, 2, 3) in the figure. (1) The landraces from each of two subgene pools were used as training sets to predict breeding lines, respectively. (2) All landraces were used as training sets to predict breeding lines. (3) A part of the breeding lines was selected and included in the training sets.

Figure 2

Different number of SNPs and different population sizes were used in ten-fold cross-validation using 100 replications for 15 agronomic traits in 484 common bean landraces (The figure shows four traits: DF, days to flowering; PH, plant height; SP, seeds per pod; and SH, seed height. Other traits are shown in Supplementary Figure S2).

Figure 3

The three principal components of a PCA performed on 9781 SNPs markers among 628 common bean accessions. The four colors (shapes) represent landraces and breeding lines in the two gene pools.