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Bayesian reassessment of the epigenetic architecture of complex traits

Daniel Trejo Banos, Daniel L. McCartney, Tom Battram, Gibran Hemani, Rosie M. Walker, Stewart W. Morris, Qian Zhang, David J. Porteous, Allan F. McRae, Naomi R. Wray, Peter M. Visscher, Chris S. Haley, Kathryn L. Evans, Ian J. Deary, Andrew M. McIntosh, Riccardo E. Marioni, Matthew R. Robinson
doi: https://doi.org/10.1101/450288
Daniel Trejo Banos
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
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  • For correspondence: daniel.trejobanos@unil.ch matthew.robinson@unil.ch
Daniel L. McCartney
2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
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Tom Battram
3MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
4Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Gibran Hemani
3MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
4Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Rosie M. Walker
2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
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Stewart W. Morris
2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
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Qian Zhang
5Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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David J. Porteous
6Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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Allan F. McRae
5Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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Naomi R. Wray
5Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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Peter M. Visscher
5Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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Chris S. Haley
7MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
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Kathryn L. Evans
2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
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Ian J. Deary
6Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
8Department of Psychology, University of Edinburgh, Edinburgh, UK
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Andrew M. McIntosh
2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
6Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
9Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
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Riccardo E. Marioni
2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
6Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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Matthew R. Robinson
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
10Swiss Institute of Bioinformatics, Lausanne, Switzerland
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  • For correspondence: daniel.trejobanos@unil.ch matthew.robinson@unil.ch
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1 Abstract

Epigenetic DNA modification is partly under genetic control, and occurs in response to a wide range of environmental exposures. Linking epigenetic marks to clinical outcomes may provide greater insight into underlying molecular processes of disease, assist in the identification of therapeutic targets, and improve risk prediction. Here, we present a statistical approach, based on Bayesian inference, that estimates associations between disease risk and all measured epigenetic probes jointly, automatically controlling for both data structure (including cell-count effects, relatedness, and experimental batch effects) and correlations among probes. We benchmark our approach in simulation study, finding improved estimation of probe associations across a wide range of scenarios over existing approaches. Our method estimates the total proportion of disease risk captured by epigenetic probe variation, and when we applied it to measures of body mass index (BMI) and cigarette consumption behaviour in 5,101 individuals, we find that 66.7% (95% CI 60.0-72.8) of the variation in BMI and 67.7% (95% CI 58.4-76.9) of the variation in cigarette consumption can be captured by methylation array data from whole blood, independent of the variation explained by single nucleotide polymorphism markers. We find novel associations, with smoking behaviour associated with a methylation probe at the MNDA gene with >95% posterior inclusion probability, which is a myeloid cell nuclear differentiation antigen gene previously implicated as a biomarker for inflammation and non-Hodgkin lymphoma risk. We conduct unique genome-wide enrichment analyses, identifying blood cholesterol, lipid transport and sterol metabolism pathways for BMI, and response to xenobiotic stimulus and negative regulation of RNA polymerase II promoter transcription for smoking, all with >95% posterior inclusion probability of having methylation probes with associations >1.5 times larger than the average. Finally, we improve phenotypic prediction in two independent cohorts by 28.7% and 10.2% for BMI and smoking respectively over a LASSO model. These results imply that probe measures may capture large amounts of variance because they are likely a consequence of the phenotype rather than a cause. As a result, ‘omics’ data may enable accurate characterization of disease progression and identification of individuals who are on a path to disease. Our approach facilitates better understanding of the underlying epigenetic architecture of complex common disease and is applicable to any kind of genomics data.

Footnotes

  • ↵† These authors jointly supervised this work.

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Posted November 14, 2018.
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Bayesian reassessment of the epigenetic architecture of complex traits
Daniel Trejo Banos, Daniel L. McCartney, Tom Battram, Gibran Hemani, Rosie M. Walker, Stewart W. Morris, Qian Zhang, David J. Porteous, Allan F. McRae, Naomi R. Wray, Peter M. Visscher, Chris S. Haley, Kathryn L. Evans, Ian J. Deary, Andrew M. McIntosh, Riccardo E. Marioni, Matthew R. Robinson
bioRxiv 450288; doi: https://doi.org/10.1101/450288
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Bayesian reassessment of the epigenetic architecture of complex traits
Daniel Trejo Banos, Daniel L. McCartney, Tom Battram, Gibran Hemani, Rosie M. Walker, Stewart W. Morris, Qian Zhang, David J. Porteous, Allan F. McRae, Naomi R. Wray, Peter M. Visscher, Chris S. Haley, Kathryn L. Evans, Ian J. Deary, Andrew M. McIntosh, Riccardo E. Marioni, Matthew R. Robinson
bioRxiv 450288; doi: https://doi.org/10.1101/450288

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