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Logistic model-based genetic analysis for kernel filling in a maize RIL population

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Abstract

Kernel filling is an important factor that directly affects kernel yield in maize. Based on a Logistic model, the process of kernel filling in maize can be effectively fitted, and the characteristic parameters with biological significance can be estimated. To clarify the genetic mechanism of characteristic parameters of kernel filling in maize, a recombinant inbred line (RIL) population including 208 lines derived from the maize inbred lines DH1M and T877 were evaluated in Nantong in 2015 and in Yangzhou in 2016, respectively. The kernel dry weights of recombinant inbred lines were measured 10, 15, 20, 25, 30, 35, 40, 43, 46, 49, 52, 55, 58 and 61 days after pollination (DAP). A total of 12 characteristic parameters related to kernel filling were estimated in different environments using the Logistic model. These parameters showed abundant phenotypic variation across two environments in the recombinant inbred line population. Some more ideal genotypes were selected through clustering based on BLUP values of characteristic parameters. Genetic analysis indicated that the 12 characteristic parameters conformed to the “major gene plus polygenes” model. The results of two environments were reproduced well. Most of the characteristic parameters related to kernel filling were controlled by two major genes, and a few characteristic parameters were controlled by three or four major genes. In addition, the genetic models of some characteristic parameters differed in the two environments due to interactions between the genes and environments. This study not only laid a foundation for further clarifying the genetic mechanism of maize kernel filling and mapping the related genes but also suggests a new paradigm for dynamic developing traits.

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Acknowledgements

This work was supported by Grants from the National Key Technology Research and Development Program of MOST (2016YFD0100300), the National High-tech R&D Program (863 Program) (2014AA10A601-5), the National Natural Science Foundations (31391632, 91535103 and 31601810), the Natural Science Foundations of Jiangsu Province (BK20150010), the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Natural Science Foundation of the Jiangsu Higher Education Institutions (14KJA210005), and the Innovative Research Team of Universities in Jiangsu Province.

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, 225009, China

    Shuangyi Yin, Pengcheng Li, Yang Xu, Jun Liu, Tiantian Yang, Zefeng Yang & Chenwu Xu

  2. Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong, 226541, China

    Lin Xue & Derong Hao

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  1. Shuangyi Yin
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  2. Pengcheng Li
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  3. Yang Xu
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  4. Lin Xue
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  5. Derong Hao
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  6. Jun Liu
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  7. Tiantian Yang
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  8. Zefeng Yang
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  9. Chenwu Xu
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Correspondence to Chenwu Xu.

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Yin, S., Li, P., Xu, Y. et al. Logistic model-based genetic analysis for kernel filling in a maize RIL population. Euphytica 214, 86 (2018). https://doi.org/10.1007/s10681-018-2162-y

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