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 HemaniJordana T Bell, Riccardo E Marioni, Matthew R Robinson

Research output: Contribution to journalArticle (Academic Journal)peer-review

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.

Original languageEnglish
Pages (from-to)2865
JournalNature Communications
Volume11
Issue number1
DOIs
Publication statusPublished - 8 Jun 2020

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