Lee CM, Park J, Kim B, Seo J, Lee G, Jang S, Koh HJ.

Rice (N Y). 2015 Dec;8(1):33. doi: 10.1186/s12284-015-0066-1. Epub 2015 Oct 30.

Abstract

BACKGROUND:

Grain size is one of the key factors determining yield and quality in rice. A large number of genes are involved in the regulation of grain size parameters such as grain length and grain width. Different alleles of these genes have different impacts on the grain size traits under their control. However, the combined influence of multiple alleles of different genes on grain size remains to be investigated. Six key genes known to influence grain size were investigated in this study: GS3, GS5, GS6, GW2, qSW5/GW5, and GW8/OsSPL16. Allele and grain measurement data were used to develop a regression equation model that can be used for molecular breeding of rice with desired grain characteristics.

RESULTS:

A total of 215 diverse rice germplasms, which originated from or were developed in 28 rice-consuming countries, were used in this study. Genotyping analysis demonstrated that a relatively small number of allele combinations were preserved in the diverse population and that these allele combinations were significantly associated with differences in grain size. Furthermore, in several cases, variation at a single gene was sufficient to influence grain size, even when the alleles of other genes remained constant. The data were used to develop a regression equation model for prediction of rice grain size, and this was tested using data from a further 34 germplasms. The model was significantly correlated with three of the four grain size-related traits examined in this study.

CONCLUSION:

Rice grain size is strongly influenced by specific combinations of alleles from six different genes. A regression equation model developed from allele and grain measurement data can be used in rice breeding programs for the development of new rice varieties with desired grain size and shape.

See: http://www.ncbi.nlm.nih.gov/pubmed/26519289

Figure 1: Validation of the regression equation model. The regression equation model was evaluated by comparing estimated values with actual measured values for four grain size-related traits (GL, GW, LWR, and KGW). The x-axis indicates measured values and the y-axis indicates estimated values. The dotted line indicates the 95 % prediction limits, the blue-shaded region indicates the 95 % confidence limits, and the solid line indicates the best prediction from the model.