期刊
NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-020-16520-1
关键词
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资金
- Swiss National Science Foundation [31003A-179380]
- Eccellenza Grant [PCEGP3-181181]
- IST Austria
- Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6]
- Scottish Funding Council [HR03006]
- Medical Research Council UK
- Wellcome Trust (Wellcome Trust Strategic Award STratifying Resilience and Depression Longitudinally (STRADL)) [104036/Z/14/Z]
- Age UK (Disconnected Mind programme)
- Medical Research Council [MR/M01311/1]
- Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award)
- Age UK
- University of Edinburgh
- University of Queensland
- Wellcome Trust Institutional Strategic Support Fund
- Biotechnology and Biological Sciences Research Council [MR/K026992/1]
- Alzheimer's Research UK [ARUK-PG2017B-10]
- UK BBSRC [BB/I025751/1, BB/I025263/1]
- European Community
- National Institute for Health Research (NIHR)
- JPI ERA-HDHL DIMENSION project [BBSRC BB/S020845/1]
- Wellcome Trust
- Wellcome Trust [208806/Z/17/Z]
- King's College London
- Swiss National Science Foundation (SNF) [31003A_179380] Funding Source: Swiss National Science Foundation (SNF)
- Wellcome Trust [208806/Z/17/Z] Funding Source: researchfish
- BBSRC [BB/I025263/1, BB/S020845/1, BB/I025751/1] Funding Source: UKRI
- MRC [MC_PC_U127592696, MC_UU_12013/2, MC_UU_00011/5, MC_UU_00007/10, G0700704, MC_PC_19009] Funding Source: UKRI
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. Linking epigenetic marks to clinical outcomes promises insight into the underlying processes. Here, the authors introduce a statistical approach to estimate associations between a phenotype and all epigenetic probes jointly, and to estimate the proportion of variation captured by epigenetic effects.
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