Dataset Information

Accounting for population stratification in DNA methylation studies.

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.

SUBMITTER: Barfield RT

PROVIDER: S-EPMC4090102 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Accounting for population stratification in DNA methylation studies.

Barfield Richard T RT Almli Lynn M LM Kilaru Varun V Smith Alicia K AK Mercer Kristina B KB Duncan Richard R Klengel Torsten T Mehta Divya D Mehta Divya D Binder Elisabeth B EB Epstein Michael P MP Ressler Kerry J KJ Conneely Karen N KN

Genetic epidemiology 20140129 3

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 studie ...[more]

PMID: 24478250

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Project description:Abstract Background: The development of successful DNA-driven, molecular subtyping methods for SZ is highly desirable because identifying subtypes that are predictive of symptoms, outcome, or treatment response could lead to better understanding of the etiology of SZ. Here, a molecular subtyping method was developed and tested based on data from the largest collection of SZ case–control samples available. The method developed and tested here accounts for sex and ancestry. Methods: Model-based recursive partitioning was applied to (1) 346 human, biological, pathway-based polygenic risk scores based on extant GWAS enrichment data from the PGC, (2) C4 gene expression data, (3) corresponding major histocompatibility complex single-nucleotide polymorphisms (MHC SNPs) rs210133 and rs123360412, (4) 10 empirically derived ancestry principle components, and (5) binary sex data. Models were tested using the Wellcome Trust case–control consortium (WTCCC) SZ data. Results: Resulting molecular subtypes explain significant heterogeneity in schizophrenia symptoms and age at onset. In this application, sex and factors pertaining to general DNA/RNA function and repair were as relevant as factors related to axon development. C4 expression, MHC SNPs, and ancestry components were dropped in the best step-wise fitting model, in addition to all two-way interactions. Molecular subtypes accounted for significant variation in AAO (P = 5.8*10–5) and in both negative and positive symptoms (P = .027 and 5.5*10–4, respectively). Conclusion: Based on a partitioning method using biological pathway data, we were able to derive molecular subtypes that are phenotypically informative. It was unexpected that (1) axonal pathways are overall less important than their cell RMR homologues and (2) C4 expression is not involved in the resulting subtypes. Application of partitioning methods to empirically derived, pathway-based polygenic risk scores, accounting for critical covariates, can lead to important insights into complex, heterogeneous disorders.

Dataset Information

Accounting for population stratification in DNA methylation studies.

Publications

Accounting for population stratification in DNA methylation studies.

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