Accounting for population stratification in DNA methylation studies

Barfield, Richard T., Almli, Lynn M., Kilaru, Varun, Smith, Alicia K., Mercer, Kristina B., Duncan, Richard, Klengel, Torsten, Mehta, Divya, Binder, Elisabeth B., Epstein, Michael P., Ressler, Kerry J. and Conneely, Karen N. (2014) Accounting for population stratification in DNA methylation studies. Genetic Epidemiology, 38 3: 231-241. doi:10.1002/gepi.21789

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Author Barfield, Richard T.
Almli, Lynn M.
Kilaru, Varun
Smith, Alicia K.
Mercer, Kristina B.
Duncan, Richard
Klengel, Torsten
Mehta, Divya
Binder, Elisabeth B.
Epstein, Michael P.
Ressler, Kerry J.
Conneely, Karen N.
Title Accounting for population stratification in DNA methylation studies
Journal name Genetic Epidemiology   Check publisher's open access policy
ISSN 0741-0395
Publication date 2014
Year available 2014
Sub-type Article (original research)
DOI 10.1002/gepi.21789
Open Access Status File (Author Post-print)
Volume 38
Issue 3
Start page 231
End page 241
Total pages 11
Place of publication Hoboken, NJ United States
Publisher John Wiley and Sons, Inc.
Collection year 2015
Language eng
Abstract DNA methylation is an important epigenetic mechanism that has been linked to complex diseases and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome-wide association studies, issues such as population stratification will need to be addressed. It is well-documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components (PCs) from different subsets of genome-wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our PC-based approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that (1) all of the methods considered are effective at removing inflation due to population stratification, and (2) maximum power can be obtained with single-nucleotide polymorphism (SNP)-based PCs, followed by methylation-based PCs, which outperform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation-based PCs, we find that PCs based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjust for population stratification in DNA methylation studies when genome-wide SNP data are unavailable.
Keyword Association studies
DNA methylation
Population stratification
Principal components
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
Queensland Brain Institute Publications
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Citation counts: TR Web of Science Citation Count  Cited 19 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 21 times in Scopus Article | Citations
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Created: Fri, 24 Oct 2014, 16:57:02 EST by Sylvie Pichelin on behalf of Queensland Brain Institute