Abstract
Rice yield potential is partially affected by grain size and weight, which associates with a great number of genes and QTLs. However, it is still unclear that how multiple alleles in different genes take a combined effect on grain shape/size. Here, we investigated seven core grain size-related functional genes (GL7, GS3, GW8, GS5, TGW6, WTG1, and An-1) and observed a wide phenotypic variation for five agronomic traits (grain length, grain width, grain length–width ratio, grain thickness and thousand-grain weight) in 521 rice germplasm. The correlation analysis showed a strong association among these grain traits which have distinct impacts on determining the final rice grain size. Genotyping analysis demonstrated that a relatively small number of allele combinations were preserved in the diverse population and these allele combinations were significantly associated with differences in grain size. Furthermore, alleles were regarded as individual variables to develop the multiple regression equation. We found that B and C allelic types of GS3 and conventional type of WTG1 played relevant roles in grain size and thousand-grain weight, separately. The models would conduce to devise instructive approaches by selecting appropriate candidate alleles, which could fuel further research for breeding preferred grain shape and high-yielding crop.
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Funding
This study was financially supported by the National Key Research and Development Program of China (Grant no. 2016YFD0100400), the National Special Key Project for Transgenic Breeding (Grant no. 2016ZX08001001), the 863 program (Grant no. 2014AA10A604-9), and Key Grant Project of Chinese Ministry of Education (Grant no. 313039).
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YL and HZ conceptualized this study research. CL performed statistical analyses and validation. HZ wrote the manuscript. WK, YZ, TS, and GZ helped in data consolidation and manuscript revision.
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Communicated by Stefan Hohmann.
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Zhong, H., Liu, C., Kong, W. et al. Effect of multi-allele combination on rice grain size based on prediction of regression equation model. Mol Genet Genomics 295, 465–474 (2020). https://doi.org/10.1007/s00438-019-01627-y
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DOI: https://doi.org/10.1007/s00438-019-01627-y