Network based conditional genome wide association analysis of human metabolomics

  1. Y. A. Tsepilov
  2. S. Zh. Sharapov
  3. O. O. Zaytseva
  4. J. Krumsek
  5. C. Prehn
  6. J. Adamski
  7. G. Kastenmüller
  8. R. Wang-Sattler
  9. K. Strauch
  10. C. Gieger
  11. Y. S. Aulchenko

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Abstract

Background

Genome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics (‘omics’) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.

Results

We propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific ‘contra-intuitive’ pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

Conclusions

We demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific ‘contra-intuitive’ pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.

Short abstract

We propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific ‘contra-intuitive’ pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

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  1. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giy137

    Y. A. Tsepilov 1Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteS. Zh. Sharapov 1Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia2Novosibirsk State University, 630090 Novosibirsk, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteO. O. Zaytseva 1Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia2Novosibirsk State University, 630090 Novosibirsk, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteJ. Krumsek 3Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteC. Prehn 4Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteJ. Adamski 4Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.5Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universität München, 85354 Freising-Weihenstephan, Germany6German Center for Diabetes Research, 85764 Neuherberg, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteG. Kastenmüller 7Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteR. Wang-Sattler 6German Center for Diabetes Research, 85764 Neuherberg, Germany8Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany9Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteK. Strauch 10Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany11Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, 80539, Munich, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteC. Gieger 6German Center for Diabetes Research, 85764 Neuherberg, Germany8Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany9Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteY. S. Aulchenko 1Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia2Novosibirsk State University, 630090 Novosibirsk, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: yurii@bionet.nsc.ru

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giy137 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101442 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101443

  2. Version published to 10.1101/096982v1 on bioRxiv