Bayesian reassessment of the epigenetic architecture of complex traits

Trejo Banos, Daniel; McCartney, Daniel L.; Patxot, Marion; Anchieri, Lucas; Battram, Thomas; Christiansen, Colette; Costeira, Ricardo; Walker, Rosie M.; Morris, Stewart W.; Campbell, Archie; Zhang, Qian; Porteous, David J.; McRae, Allan F.; Wray, Naomi R.; Visscher, Peter M.; Haley, Chris S.; Evans, Kathryn L.; Deary, Ian J.; McIntosh, Andrew M.; Hemani, Gibran; Bell, Jordana T.; Marioni, Riccardo E. and Robinson, Matthew R. (2020). Bayesian reassessment of the epigenetic architecture of complex traits. Nature Communications, 11(1)

DOI: https://doi.org/10.1038/s41467-020-16520-1

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.

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