Bayesian reassessment of the epigenetic architecture of complex traits

Daniel Trejo Banos (), Daniel L. McCartney, Marion Patxot, Lucas Anchieri, Thomas Battram, Colette Christiansen, Ricardo Costeira, Rosie M. Walker, Stewart W. Morris, Archie Campbell, 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, Gibran Hemani, Jordana T. Bell, Riccardo E. Marioni and Matthew R. Robinson ()
Additional contact information
Daniel Trejo Banos: University of Lausanne
Daniel L. McCartney: Institute of Genetics and Molecular Medicine, University of Edinburgh
Marion Patxot: University of Lausanne
Lucas Anchieri: University of Lausanne
Thomas Battram: University of Bristol
Colette Christiansen: King’s College London
Ricardo Costeira: King’s College London
Rosie M. Walker: Institute of Genetics and Molecular Medicine, University of Edinburgh
Stewart W. Morris: Institute of Genetics and Molecular Medicine, University of Edinburgh
Archie Campbell: Institute of Genetics and Molecular Medicine, University of Edinburgh
Qian Zhang: University of Queensland
David J. Porteous: Institute of Genetics and Molecular Medicine, University of Edinburgh
Allan F. McRae: University of Queensland
Naomi R. Wray: University of Queensland
Peter M. Visscher: University of Queensland
Chris S. Haley: Institute of Genetics and Molecular Medicine, University of Edinburgh
Kathryn L. Evans: Institute of Genetics and Molecular Medicine, University of Edinburgh
Ian J. Deary: University of Edinburgh
Andrew M. McIntosh: Institute of Genetics and Molecular Medicine, University of Edinburgh
Gibran Hemani: University of Bristol
Jordana T. Bell: King’s College London
Riccardo E. Marioni: Institute of Genetics and Molecular Medicine, University of Edinburgh
Matthew R. Robinson: Institute of Science and Technology Austria

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-16520-1

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