Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression

• Type of Material: Article
• Authors: Guosheng Zhang, University of North Carolina; Kuan-Chieh Huang, University of North Carolina; Zheng Xu, University of North Carolina; Jung-Ying Tzeng, North Carolina State University; Karen Conneely, Emory University; Weihua Guan, University of Minnesota; Jian Kang, University of Michigan; Yun Li, University of North Carolina
• Language: English
• Date: 2016-05-01
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.21969
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 40
• Issue: 4
• Start Page: 333
• End Page: 340
• Grant/Funding Information: The research is supported by R01HG006292, R01HG006703 (awarded to Y.L.) R01MH105561 (awarded to J.K.) and P01 CA142538 (awarded to J.Y.T.).
• Keywords: Science & Technology; HUMAN TISSUES; AFRICAN-AMERICANS; GENOMIC CHARACTERIZATION; penalized functional regression; imputation; MISSING VALUE ESTIMATION; Genetics & Heredity; GENE-EXPRESSION DATA; DNA methylation; epigenome-wide association study; PREDICTION; DESIGNS; SEQUENCES; HEALTH; GENOTYPE IMPUTATION; Public, Environmental & Occupational Health; Life Sciences & Biomedicine
• Research Categories: Biology, Biostatistics; Biology, Genetics

Abstract

DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS).