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Gigascience. 2018 Dec 1;7(12). doi: 10.1093/gigascience/giy137.

A network-based conditional genetic association analysis of the human metabolome.

Author information

1
Institute of Cytology and Genetics SB RAS, Novosibirsk, Lavrentieva Ave. 10, 630090, Russia.
2
Natural Scince Department, Novosibirsk State University, Novosibirsk, Pirogova Str. 1, 630090, Russia.
3
Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.
4
Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.
5
Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technical University of Munich, Freising-Weihenstephan, Arcisstrasse 21, 80333, Germany.
6
German Center for Diabetes Research, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.
7
Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.
8
Research Unit of Molecular Epidemiology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.
9
Institute of Epidemiology II, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.
10
Institute of Genetic Epidemiology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.
11
Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Butenandstrasse 5, 81377, Germany.
12
PolyOmica, 's-Hertogenbosch, Het Vlaggeschip 61, 5237 PA, The Netherlands.

Abstract

Background:

Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics ("omics"), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods.

Results:

To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites.

Conclusions:

We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement.

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