Accounting for Population Stratification in DNA Methylation Studies. Issue 3 (29th January 2014)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Genetic epidemiology. Volume 38:Issue 3(2014)
• Page Start: 231
• Page End: 241
• Publication Date: 2014-01-29
• Subjects: Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21789
• Languages: English
• ISSNs: 0741-0395

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>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.</p> </abstract>