Improving Maize Trait through Modifying Combination of Genes

  • Duolin Wang
  • Juexin Wang
  • Yu Chen
  • Sean Yang
  • Qin Zeng
  • Jingdong LiuEmail author
  • Dong XuEmail author
Part of the Emerging Topics in Statistics and Biostatistics book series (ETSB)


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.


Maize Complex trait Yield improvement Gene expression data analysis Network biomarker 



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.

Supplementary material (3.1 mb)
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  1. 1.
    Alexandratos, N., Bruinsma, J. (2012). World agriculture towards 2030/2050: The 2012 revision. In: ESA Working Paper Rome, FAO.Google Scholar
  2. 2.
    Tokatlidis, I., & Koutroubas, S. (2004). A review of maize hybrids’ dependence on high plant populations and its implications for crop yield stability. Field Crops Research, 88(2), 103–114.CrossRefGoogle Scholar
  3. 3.
    Tollenaar, M., & Wu, J. (1999). Yield improvement in temperate maize is attributable to greater stress tolerance. Crop Science, 39(6), 1597–1604.CrossRefGoogle Scholar
  4. 4.
    Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PLoS One, 8(6), e66428.CrossRefGoogle Scholar
  5. 5.
    Matsuoka, Y., Vigouroux, Y., Goodman, M. M., Sanchez, J., Buckler, E., & Doebley, J. (2002). A single domestication for maize shown by multilocus microsatellite genotyping. Proceedings of the National Academy of Sciences, 99(9), 6080–6084.CrossRefGoogle Scholar
  6. 6.
    Doust, A. N., Lukens, L., Olsen, K. M., Mauro-Herrera, M., Meyer, A., & Rogers, K. (2014). Beyond the single gene: How epistasis and gene-by-environment effects influence crop domestication. Proceedings of the National Academy of Sciences, 111(17), 6178–6183.CrossRefGoogle Scholar
  7. 7.
    Bhattacharyya, M., & Bandyopadhyay, S. (2013). Studying the differential co-expression of microRNAs reveals significant role of white matter in early Alzheimer’s progression. Molecular BioSystems, 9(3), 457–466.CrossRefGoogle Scholar
  8. 8.
    de la Fuente, A. (2010). From ‘differential expression’to ‘differential networking’–identification of dysfunctional regulatory networks in diseases. Trends in Genetics, 26(7), 326–333.CrossRefGoogle Scholar
  9. 9.
    Brachi, B., Morris, G. P., & Borevitz, J. O. (2011). Genome-wide association studies in plants: The missing heritability is in the field. Genome Biology, 12(10), 1.CrossRefGoogle Scholar
  10. 10.
    Makowsky, R., Pajewski, N. M., Klimentidis, Y. C., Vazquez, A. I., Duarte, C. W., Allison, D. B., & de Los Campos, G. (2011). Beyond missing heritability: Prediction of complex traits. PLoS Genetics, 7(4), e1002051.CrossRefGoogle Scholar
  11. 11.
    Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., & Daly, M. J. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559–575.CrossRefGoogle Scholar
  12. 12.
    Zhang, J., Zhang, Q., Lewis, D., & Zhang, M. Q. (2011). A Bayesian method for disentangling dependent structure of epistatic interaction. American Journal of Biostatistics, 2(1), 1.Google Scholar
  13. 13.
    Ritchie, M. D., Hahn, L. W., Roodi, N., Bailey, L. R., Dupont, W. D., Parl, F. F., & Moore, J. H. (2001). Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. The American Journal of Human Genetics, 69(1), 138–147.CrossRefGoogle Scholar
  14. 14.
    Wang, J., Joshi, T., Valliyodan, B., Shi, H., Liang, Y., Nguyen, H. T., Zhang, J., & Xu, D. (2015). A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies. BMC Genomics, 16(1), 1.CrossRefGoogle Scholar
  15. 15.
    Kayano, M., Shiga, M., & Mamitsuka, H. (2014). Detecting differentially coexpressed genes from labeled expression data: A brief review. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(1), 154–167.CrossRefGoogle Scholar
  16. 16.
    Wang, D., Wang, J., Jiang, Y., Liang, Y., & Xu, D. (2017). BFDCA: A comprehensive tool of using Bayes factor for differential co-expression analysis. Journal of Molecular Biology, 429, 446–453.CrossRefGoogle Scholar
  17. 17.
    Mortazavi, A. W., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5(7), 621–628.CrossRefGoogle Scholar
  18. 18.
    Verhaak, R. G., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., & Mesirov, J. P. (2010). Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 17(1), 98–110.CrossRefGoogle Scholar
  19. 19.
    Fraley, C., & Raftery, A. E. (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification, 24(2), 155–181.CrossRefMathSciNetzbMATHGoogle Scholar
  20. 20.
    Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9(1), 1.CrossRefGoogle Scholar
  21. 21.
    Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2014). Gene sets net correlations analysis (GSNCA): A multivariate differential coexpression test for gene sets. Bioinformatics, 30(3), 360–368.CrossRefGoogle Scholar
  22. 22.
    Whitney, A. W. (1971). A direct method of nonparametric measurement selection. IEEE Transactions on Computers, 100(9), 1100–1103.CrossRefzbMATHGoogle Scholar
  23. 23.
    Leung, K. M. (2007). Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering.Google Scholar
  24. 24.
    Schaeffer, M. L., Harper, L. C., Gardiner, J. M., Andorf, C. M., Campbell, D. A., Cannon, E. K., Sen, T. Z., & Lawrence, C. J. (2011). MaizeGDB: Curation and outreach go hand-in-hand. Database, 2011, bar022.CrossRefGoogle Scholar
  25. 25.
    Du, Z., Zhou, X., Ling, Y., Zhang, Z., & Su, Z. (2010). agriGO: A GO analysis toolkit for the agricultural community. Nucleic Acids Research, 38, W64–W70.CrossRefGoogle Scholar
  26. 26.
    Plaxton, W. C. (1996). The organization and regulation of plant glycolysis. Annual Review of Plant Biology, 47(1), 185–214.CrossRefGoogle Scholar
  27. 27.
    Fu, J., Thiemann, A., Schrag, T. A., Melchinger, A. E., Scholten, S., & Frisch, M. (2010). Dissecting grain yield pathways and their interactions with grain dry matter content by a two-step correlation approach with maize seedling transcriptome. BMC Plant Biology, 10(1), 1.CrossRefGoogle Scholar
  28. 28.
    Brzobohaty, B., Moore, I., Kristoffersen, P., Bako, L., Campos, N., Schell, J., & Palme, K. (1993). Release of active Cytokinin by a -glucosidase localized to the maize root meristem. Science, 262, 1051–1054.CrossRefGoogle Scholar
  29. 29.
    Martin, R. C., Mok, M. C., & Mok, D. W. (1999). Isolation of a cytokinin gene, ZOG1, encoding zeatin O-glucosyltransferase from Phaseolus lunatus. Proceedings of the National Academy of Sciences, 96(1), 284–289.CrossRefGoogle Scholar
  30. 30.
    Ferreyra, M. L. F., Rius, S. P., & Casati, P. (2012). Flavonoids: Biosynthesis, biological functions, and biotechnological applications. Frontiers in Plant Science, 3, 222.Google Scholar
  31. 31.
    Owens, D. K., & McIntosh, C. A. (2009). Identification, recombinant expression, and biochemical characterization of a flavonol 3-O-glucosyltransferase clone from Citrus paradisi. Phytochemistry, 70(11), 1382–1391.CrossRefGoogle Scholar
  32. 32.
    Ratti, C. (2001). Hot air and freeze-drying of high-value foods: A review. Journal of Food Engineering, 49(4), 311–319.CrossRefGoogle Scholar
  33. 33.
    Lai, K., Dolan, K., & Ng, P. (2009). Inverse method to estimate kinetic degradation parameters of grape anthocyanins in wheat flour under simultaneously changing temperature and moisture. Journal of Food Science, 74(5), E241–E249.CrossRefGoogle Scholar
  34. 34.
    Yılmaz, F. M., Yüksekkaya, S., Vardin, H., & Karaaslan, M. (2017). The effects of drying conditions on moisture transfer and quality of pomegranate fruit leather (pestil). Journal of the Saudi Society of Agricultural Sciences, 16, 33–40.CrossRefGoogle Scholar
  35. 35.
    Yuan, H., & Liu, D. (2008). Signaling components involved in plant responses to phosphate starvation. Journal of Integrative Plant Biology, 50(7), 849–859.CrossRefGoogle Scholar
  36. 36.
    Ahmad, R., Khalid, A., Arshad, M., Zahir, Z. A., & Mahmood, T. (2008). Effect of compost enriched with N and L-tryptophan on soil and maize. Agronomy for Sustainable Development, 28(2), 299–305.CrossRefGoogle Scholar
  37. 37.
    Hammer, G. L., Dong, Z., McLean, G., Doherty, A., Messina, C., Schussler, J., Zinselmeier, C., Paszkiewicz, S., & Cooper, M. (2009). Can changes in canopy and/or root system architecture explain historical maize yield trends in the US corn belt? Crop Science, 49(1), 299–312.CrossRefGoogle Scholar
  38. 38.
    Zhu, G., Wu, A., Xu, X.-J., Xiao, P., Lu, L., Liu, J., Cao, Y., Chen, L., Wu, J., & Zhao, X.-M. (2015). PPIM: A protein-protein interaction database for maize. Plant Physiology, 02015, 01821.Google Scholar
  39. 39.
    Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, 3(02), 185–205.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electric Engineering and Computer Science, and Christopher S. Bond Life Sciences CenterUniversity of MissouriColumbiaUSA
  2. 2.Bayer U.S. Crop Science, Monsanto Legal EntityChesterfieldUSA
  3. 3.Eli Lilly and Company, Lilly Corporate CenterIndianapolisUSA

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