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Leveraging non-targeted metabolite profiling via statistical genomics.

Shen M, Broeckling CD, Chu EY, Ziegler G, Baxter IR, Prenni JE, Hoekenga OA - PLoS ONE (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.

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.

Show MeSH
Module eigenvalues do not obscure the importance of single compounds.MEorange was estimated from 81 molecular features, one of which was identified to be tyramine. GWAS on MEorange identified 27 significant SNPs at the FDR-corrected p<0.05 threshold. GWAS on tyramine alone identified 7 SNPs in common (red circles) with MEorange.
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pone-0057667-g004: Module eigenvalues do not obscure the importance of single compounds.MEorange was estimated from 81 molecular features, one of which was identified to be tyramine. GWAS on MEorange identified 27 significant SNPs at the FDR-corrected p<0.05 threshold. GWAS on tyramine alone identified 7 SNPs in common (red circles) with MEorange.

Mentions: A potential pitfall for the WGCNA procedure as a data condensation tool was the potential to excessively smooth the data, creating a false picture of the genetic regulation of the metabolome. In this instance, collapsing multiple metabolite markers into a single signal might obscure the effect of a particular locus for importance of a module constituent. To address this concern, we examined the orange module in greater detail. At 4SD, orange has 9 nodes, one of which we identified as tyramine (Figure S3). The abundance of tyramine alone was used as a trait for GWAS; this result was compared with the GWAS on the orange module eigenvalue (Figure 4). The orange module returned 27 significant SNPs, 7 of which were also identified as significant for tyramine (Table 1). While GWAS on tyramine returned more significant SNPs than for the orange module, our result does indicate that SNPs associated with a single compound can be identified from GWAS on the module eigenvalue.


Leveraging non-targeted metabolite profiling via statistical genomics.

Shen M, Broeckling CD, Chu EY, Ziegler G, Baxter IR, Prenni JE, Hoekenga OA - PLoS ONE (2013)

Module eigenvalues do not obscure the importance of single compounds.MEorange was estimated from 81 molecular features, one of which was identified to be tyramine. GWAS on MEorange identified 27 significant SNPs at the FDR-corrected p<0.05 threshold. GWAS on tyramine alone identified 7 SNPs in common (red circles) with MEorange.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585405&req=5

pone-0057667-g004: Module eigenvalues do not obscure the importance of single compounds.MEorange was estimated from 81 molecular features, one of which was identified to be tyramine. GWAS on MEorange identified 27 significant SNPs at the FDR-corrected p<0.05 threshold. GWAS on tyramine alone identified 7 SNPs in common (red circles) with MEorange.
Mentions: A potential pitfall for the WGCNA procedure as a data condensation tool was the potential to excessively smooth the data, creating a false picture of the genetic regulation of the metabolome. In this instance, collapsing multiple metabolite markers into a single signal might obscure the effect of a particular locus for importance of a module constituent. To address this concern, we examined the orange module in greater detail. At 4SD, orange has 9 nodes, one of which we identified as tyramine (Figure S3). The abundance of tyramine alone was used as a trait for GWAS; this result was compared with the GWAS on the orange module eigenvalue (Figure 4). The orange module returned 27 significant SNPs, 7 of which were also identified as significant for tyramine (Table 1). While GWAS on tyramine returned more significant SNPs than for the orange module, our result does indicate that SNPs associated with a single compound can be identified from GWAS on the module eigenvalue.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.

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.

Show MeSH