title: Imputation of missing covariate values in epigenome-wide analysis of DNA methylation data.
abstract: DNA methylation is a widely studied epigenetic mechanism and alterations in methylation patterns may be involved in the development of common diseases. Unlike inherited changes in genetic sequence, variation in site-specific methylation varies by tissue, developmental stage, and disease status, and may be impacted by aging and exposure to environmental factors, such as diet or smoking. These non-genetic factors are typically included in epigenome-wide association studies (EWAS) because they may be confounding factors to the association between methylation and disease. However, missing values in these variables can lead to reduced sample size and decrease the statistical power of EWAS. We propose a site selection and multiple imputation (MI) method to impute missing covariate values and to perform association tests in EWAS. Then, we compare this method to an alternative projection-based method. Through simulations, we show that the MI-based method is slightly conservative, but provides consistent estimates for effect size. We also illustrate these methods with data from the Atherosclerosis Risk in Communities (ARIC) study to carry out an EWAS between methylation levels and smoking status, in which missing cell type compositions and white blood cell counts are imputed.
authors.affiliation: a Division of Biostatistics, School of Public Health, University of Minnesota , Minneapolis , MN , USA.
authors.forename: Chong
authors.initials: C
authors.lastname: Wu
authors.affiliation: b Division of Epidemiology & Community Health, School of Public Health, University of Minnesota , Minneapolis , MN , USA.
authors.forename: Ellen W
authors.initials: EW
authors.lastname: Demerath
authors.affiliation: b Division of Epidemiology & Community Health, School of Public Health, University of Minnesota , Minneapolis , MN , USA.
authors.forename: James S
authors.initials: JS
authors.lastname: Pankow
authors.affiliation: c Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston , Houston , TX , USA.
authors.forename: Jan
authors.initials: J
authors.lastname: Bressler
authors.affiliation: c Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston , Houston , TX , USA.
authors.forename: Myriam
authors.initials: M
authors.lastname: Fornage
authors.affiliation: c Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston , Houston , TX , USA.
authors.forename: Megan L
authors.initials: ML
authors.lastname: Grove
authors.affiliation: d Division of Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh , Pittsburgh , PA , USA.|e Departments of Human Genetics and Biostatistics , University of Pittsburgh Graduate School of Public Health , Pittsburgh , PA , USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: a Division of Biostatistics, School of Public Health, University of Minnesota , Minneapolis , MN , USA.
authors.forename: Weihua
authors.initials: W
authors.lastname: Guan
mesh_terms: D019295:Computational Biology; D003198:Computer Simulation; D018899:CpG Islands; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D005260:Female; D006801:Humans; D007958:Leukocyte Count; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012907:Smoking / Q000009:adverse effects
keywords: DNA methylation; Illumina 450K; epigenome-wide association; missing data; phenotype imputation