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Author Notes:

Karen N. Conneely,Dept. of Human Genetics, Emory University School of Medicine Atlanta, GA kconnee@emory.edu

Epigenotyping was supported in part by the Max-Planck Society, and we thank Anne Löschner for excellent technical assistance.

We would also like to thank the participants who made this work possible; as well as the staff of the Grady Trauma Project.

Finally, we thank two anonymous reviewers whose comments have led to substantial improvements in our manuscript.


Research Funding:

This work was primarily supported by National Institutes of Mental Health (MH071537 and MH096764).

Salary support was provided by MH085806 (for AKS); and HG007508 (for MPE and RD).

Simulations were performed on Emory's high-powered computing cluster, which is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award UL1TR000454.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Genetics & Heredity
  • Mathematical & Computational Biology
  • association studies
  • principal components
  • population stratification
  • DNA methylation

Accounting for Population Stratification in DNA Methylation Studies

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Journal Title:

Genetic Epidemiology


Volume 38, Number 3


, Pages 231-241

Type of Work:

Article | Post-print: After Peer Review


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.

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