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Improving Maize Trait through Modifying Combination of Genes

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Statistical Modeling in Biomedical Research

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

Abstract

In molecular breeding, trait improvement has been focused on exploring genetic variations of single genes. To explore the potential of modifying multiple genes simultaneously for trait improvement, we developed a systematic computational method aiming at detecting complex traits associated with gene interactions using a combination of gene expression and trait data across a set of maize hybrids. This method represents changes of expression patterns in a gene pair in uniform statistics and employs network topology to describe the inherent genotype-phenotype associations at the systems level. We applied and evaluated our method on several phenotypic traits measured on a set of maize hybrids across 2 years (2013 and 2014) and achieved consistent and biologically meaningful results. Our results provide a subset of candidate gene pairs that have the potential to improve several specific traits by gene expression enhancement or silence. Our work partially addresses the “missing heritability” problem in complex traits and offers an alternative way for improving crop traits via modifying a combination of multiple loci.

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Acknowledgements

The authors would like to acknowledge the support of Monsanto and the National Institutes of Health (R35-GM126985). The high-performance computing infrastructure is supported by the National Science Foundation under grant number CNS-1429294.

Author information

Author notes

  1. Yu Chen

    Present address: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA

  2. Duolin Wang and Juexin Wang contributed equally with all other contributors.

Authors and Affiliations

  1. Department of Electric Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA

    Duolin Wang, Juexin Wang & Dong Xu

  2. Bayer U.S. Crop Science, Monsanto Legal Entity, Chesterfield, MO, USA

    Yu Chen, Sean Yang, Qin Zeng & Jingdong Liu

Authors
  1. Duolin Wang
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  2. Juexin Wang
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  3. Yu Chen
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  4. Sean Yang
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  5. Qin Zeng
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  6. Jingdong Liu
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  7. Dong Xu
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Corresponding authors

Correspondence to Jingdong Liu or Dong Xu .

Editor information

Editors and Affiliations

  1. Math and Statistics, 1342, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. School of Social Work, University of North Carolina, Chapel Hill, NC, USA

    Ding-Geng (Din) Chen

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Wang, D. et al. (2020). Improving Maize Trait through Modifying Combination of Genes. In: Zhao, Y., Chen, DG. (eds) Statistical Modeling in Biomedical Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-33416-1_9

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