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Leveraging Non-Targeted Metabolite Profiling via Statistical Genomics

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  • Miaoqing Shen
  • Corey D Broeckling
  • Elly Yiyi Chu
  • Gregory Ziegler
  • Ivan R Baxter
  • Jessica E Prenni
  • Owen A Hoekenga

Abstract

One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.

Suggested Citation

  • Miaoqing Shen & Corey D Broeckling & Elly Yiyi Chu & Gregory Ziegler & Ivan R Baxter & Jessica E Prenni & Owen A Hoekenga, 2013. "Leveraging Non-Targeted Metabolite Profiling via Statistical Genomics," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-8, February.
    • Handle: RePEc:plo:pone00:0057667
    DOI: 10.1371/journal.pone.0057667
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