@article{7999,
  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. },
  author       = {Trejo Banos, D and McCartney, DL and Patxot, M and Anchieri, L and Battram, T and Christiansen, C and Costeira, R and Walker, RM and Morris, SW and Campbell, A and Zhang, Q and Porteous, DJ and McRae, AF and Wray, NR and Visscher, PM and Haley, CS and Evans, KL and Deary, IJ and McIntosh, AM and Hemani, G and Bell, JT and Marioni, RE and Robinson, Matthew Richard},
  issn         = {2041-1723},
  journal      = {Nature Communications},
  publisher    = {Springer Nature},
  title        = {{Bayesian reassessment of the epigenetic architecture of complex traits}},
  doi          = {10.1038/s41467-020-16520-1},
  volume       = {11},
  year         = {2020},
}