Bayesian reassessment of the epigenetic architecture of complex traits

Daniel Trejo Baños; 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; Matthew R. Robinson

doi:10.1038/s41467-020-16520-1

Bayesian reassessment of the epigenetic architecture of complex traits

Daniel Trejo Baños, 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 journal › Article › peer-review

Abstract / Description of output

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 language	English
Article number	2865
Journal	Nature Communications
Volume	11
DOIs	https://doi.org/10.1038/s41467-020-16520-1
Publication status	Published - 8 Jun 2020

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Final published version, 1.41 MBLicence: Creative Commons: Attribution (CC-BY)

Omics prediction of Alzheimer's disease
Marioni, R.
UK-based charities
1/10/17 → 31/01/21
Project: Research
Stratifying Resilience and Depression Longitudinally
McIntosh, A., Deary, I., Evans, K., Haley, C. & Porteous, D.
UK-based charities
1/01/15 → 30/06/21
Project: Research
RA2662 Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2.
Porteous, D.
1/09/13 → 31/08/19
Project: Research

Riccardo Marioni
- Deanery of Molecular, Genetic and Population Health Sciences - Personal Chair of Molecular Epidemiology of Ageing
- Centre for Genomic and Experimental Medicine
- Edinburgh Neuroscience
Person: Academic: Research Active

Cite this

Trejo Baños, D., McCartney, D. L., Patxot, M., Anchieri, L., Battram, T., Christiansen, C., Costeira, R., Walker, R. M., Morris, S. W., Campbell, A., Zhang, Q., Porteous, D. J., McRae, A. F., Wray, N. R., Visscher, P. M., Haley, C. S., Evans, K. L., Deary, I. J., McIntosh, A. M., ... Robinson, M. R. (2020). Bayesian reassessment of the epigenetic architecture of complex traits. Nature Communications, 11, Article 2865. https://doi.org/10.1038/s41467-020-16520-1

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title = "Bayesian reassessment of the epigenetic architecture of complex traits",

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 Ba{\~n}os}, Daniel and McCartney, {Daniel L.} and Marion Patxot and Lucas Anchieri and Thomas Battram and Colette Christiansen and Ricardo Costeira and Walker, {Rosie M.} and Morris, {Stewart W.} and Archie Campbell and Qian Zhang and Porteous, {David J.} and McRae, {Allan F.} and Wray, {Naomi R.} and Visscher, {Peter M.} and Haley, {Chris S.} and Evans, {Kathryn L.} and Deary, {Ian J.} and McIntosh, {Andrew M.} and Gibran Hemani and Bell, {Jordana T.} and Marioni, {Riccardo E.} and Robinson, {Matthew R.}",

year = "2020",

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day = "8",

doi = "10.1038/s41467-020-16520-1",

language = "English",

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Trejo Baños, D, McCartney, DL, Patxot, M, Anchieri, L, Battram, T, Christiansen, C, Costeira, R, Walker, RM, Morris, SW, Campbell, A, Zhang, Q, Porteous, DJ, McRae, AF, Wray, NR, Visscher, PM, Haley, CS , Evans, KL , Deary, IJ , McIntosh, AM, Hemani, G, Bell, JT, Marioni, RE & Robinson, MR 2020, 'Bayesian reassessment of the epigenetic architecture of complex traits', Nature Communications, vol. 11, 2865. https://doi.org/10.1038/s41467-020-16520-1

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T1 - Bayesian reassessment of the epigenetic architecture of complex traits

AU - Trejo Baños, Daniel

AU - McCartney, Daniel L.

AU - Patxot, Marion

AU - Anchieri, Lucas

AU - Battram, Thomas

AU - Christiansen, Colette

AU - Costeira, Ricardo

AU - Walker, Rosie M.

AU - Morris, Stewart W.

AU - Campbell, Archie

AU - Zhang, Qian

AU - Porteous, David J.

AU - McRae, Allan F.

AU - Wray, Naomi R.

AU - Visscher, Peter M.

AU - Haley, Chris S.

AU - Evans, Kathryn L.

AU - Deary, Ian J.

AU - McIntosh, Andrew M.

AU - Hemani, Gibran

AU - Bell, Jordana T.

AU - Marioni, Riccardo E.

AU - Robinson, Matthew R.

PY - 2020/6/8

Y1 - 2020/6/8

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-020-16520-1

DO - 10.1038/s41467-020-16520-1

M3 - Article

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

M1 - 2865

ER -

Bayesian reassessment of the epigenetic architecture of complex traits

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Projects

Omics prediction of Alzheimer's disease

Stratifying Resilience and Depression Longitudinally

RA2662 Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2.

Profiles

Riccardo Marioni

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