Y A Tsepilov1,2, S Z Sharapov1,2, O O Zaytseva1,2, J Krumsiek3, C Prehn4, J Adamski4,5,6, G Kastenmüller7, R Wang-Sattler6,8,9, K Strauch10,11, C Gieger6,8,9, Y S Aulchenko1,2,12. 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.
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.
Authors: Zhihong Zhu; Futao Zhang; Han Hu; Andrew Bakshi; Matthew R Robinson; Joseph E Powell; Grant W Montgomery; Michael E Goddard; Naomi R Wray; Peter M Visscher; Jian Yang Journal: Nat Genet Date: 2016-03-28 Impact factor: 38.330
Authors: Daniel J Schaid; Xingwei Tong; Beth Larrabee; Richard B Kennedy; Gregory A Poland; Jason P Sinnwell Journal: Genetics Date: 2016-08-15 Impact factor: 4.562
Authors: Thomas Illig; Christian Gieger; Guangju Zhai; Werner Römisch-Margl; Rui Wang-Sattler; Cornelia Prehn; Elisabeth Altmaier; Gabi Kastenmüller; Bernet S Kato; Hans-Werner Mewes; Thomas Meitinger; Martin Hrabé de Angelis; Florian Kronenberg; Nicole Soranzo; H-Erich Wichmann; Tim D Spector; Jerzy Adamski; Karsten Suhre Journal: Nat Genet Date: 2009-12-27 Impact factor: 38.330
Authors: Johannes Kettunen; Ayşe Demirkan; Peter Würtz; Harmen H M Draisma; Toomas Haller; Rajesh Rawal; Anika Vaarhorst; Antti J Kangas; Leo-Pekka Lyytikäinen; Matti Pirinen; René Pool; Antti-Pekka Sarin; Pasi Soininen; Taru Tukiainen; Qin Wang; Mika Tiainen; Tuulia Tynkkynen; Najaf Amin; Tanja Zeller; Marian Beekman; Joris Deelen; Ko Willems van Dijk; Tõnu Esko; Jouke-Jan Hottenga; Elisabeth M van Leeuwen; Terho Lehtimäki; Evelin Mihailov; Richard J Rose; Anton J M de Craen; Christian Gieger; Mika Kähönen; Markus Perola; Stefan Blankenberg; Markku J Savolainen; Aswin Verhoeven; Jorma Viikari; Gonneke Willemsen; Dorret I Boomsma; Cornelia M van Duijn; Johan Eriksson; Antti Jula; Marjo-Riitta Järvelin; Jaakko Kaprio; Andres Metspalu; Olli Raitakari; Veikko Salomaa; P Eline Slagboom; Melanie Waldenberger; Samuli Ripatti; Mika Ala-Korpela Journal: Nat Commun Date: 2016-03-23 Impact factor: 14.919
Authors: Y A Tsepilov; S Z Sharapov; O O Zaytseva; J Krumsiek; C Prehn; J Adamski; G Kastenmüller; R Wang-Sattler; K Strauch; C Gieger; Y S Aulchenko Journal: Gigascience Date: 2018-12-01 Impact factor: 6.524
Authors: Rosangela A Hoshi; Yanyan Liu; Mohit Jain; Daniel I Chasman; Olga V Demler; Samia Mora; Heike Luttmann-Gibson; Saumya Tiwari; Franco Giulianini; Allen M Andres; Jeramie D Watrous; Nancy R Cook; Karen H Costenbader; Olivia I Okereke; Paul M Ridker; JoAnn E Manson; I-Min Lee; Manickavasagar Vinayagamoorthy; Susan Cheng; Trisha Copeland Journal: Circ Res Date: 2022-07-19 Impact factor: 23.213
Authors: Olga N Leonova; Elizaveta E Elgaeva; Tatiana S Golubeva; Alexey V Peleganchuk; Aleksandr V Krutko; Yurii S Aulchenko; Yakov A Tsepilov Journal: PLoS One Date: 2022-05-13 Impact factor: 3.752
Authors: Y A Tsepilov; S Z Sharapov; O O Zaytseva; J Krumsiek; C Prehn; J Adamski; G Kastenmuller; R Wang-Sattler; K Strauch; C Gieger; Y S Aulchenko Journal: Gigascience Date: 2019-12-01 Impact factor: 6.524