INTRODUCTION: Due to the difference in breeding objectives, northeast japonica rice (Oryza sativa subsp. geng or japonica) is more advantageous than Japanese japonica rice in terms of yield level, while Japanese japonica rice is significantly better than Chinese japonica rice in terms of eating quality. Clarifying the genetic basis of the differences in eating quality between Chinese and Japanese japonica rice is of great value for cultivating high-yield and high-quality japonica rice.

RATIONALE: 274 Chinese and Japanese japonica rice were used as research materials to quantify the eating quality of the rice and to analyze the genetic basis of the taste differences between Chinese and Japanese japonica rice by combining genome-wide association analysis with the downscaling of many parameters.

RESULTS: The results showed that the significant differences in the taste values of Chinese and Japanese japonica rice were reflected in three textural parameters: Adhesion Force (ADF), First Recoverable Deformation Cycle (FRDC) and Elasticity Index (EI). Meanwhile, the correlation analysis between the taste values and 30 textural characters showed that 24 characters had significant or highly significant correlations with rice taste value. The 30 metrics of textural characterization were downscaled to four principal components that explained 80% of the phenotypic variation in the population, and the genome-wide associations of their eigenvalues were mined to two primary effector loci affecting the textural characterization of Chinese-Japanese japonica rice, qFPC4.3 and qFPC9.2.

CONCLUSION: In this study, we quantified the parameters of eating quality from a qualitative perspective, and thus analyzed the genetic basis of the differences in eating quality between Chinese and Japanese rice, which provided valuable genetic information and theoretical basis for the genetic improvement of eating quality of japonica rice in China.

PCA analysis and Genome wide association analysis based on principal component eigenvalues of texture characteristics indicators. PCA analysis was performed using 2021 data.