Bayesian reassessment of the epigenetic architecture of complex traits.

Trejo Banos D; McCartney DL; Patxot M; Anchieri L; Battram T; Christiansen C; Costeira R; Walker RM; Morris SW; Campbell A; Zhang Q; Porteous DJ; McRae AF; Wray NR; Visscher PM; Haley CS; Evans KL; Deary IJ; McIntosh AM; Hemani G; Bell JT; Marioni RE; Robinson MR

doi:10.1038/s41467-020-16520-1

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Bayesian reassessment of the epigenetic architecture of complex traits.

Trejo Banos D Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. daniel.trejobanos@unil.ch.
McCartney DL Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Patxot M Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Anchieri L Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Battram T MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Christiansen C Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Costeira R Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Walker RM Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Morris SW Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Campbell A Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Zhang Q Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
Porteous DJ Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
McRae AF Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
Wray NR Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
Visscher PM Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
Haley CS MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Evans KL Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Deary IJ Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
McIntosh AM Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Hemani G MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Bell JT Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Marioni RE Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Robinson MR Institute of Science and Technology Austria, Klosterneuburg, Austria. matthew.robinson@ist.ac.at.

2020-06-10

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Nature communications. - 2020

English Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70-79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3-51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.

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