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

• 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
• Journal Title: Genetic Epidemiology
• Volume: Volume 40, Number 4
• Publisher: Wiley | 2016-05-01, Pages 333-340
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1002/gepi.21969
• Subjects: Biology, Biostatistics; Biology, Genetics
• Keywords: Science & Technology; Life Sciences & Biomedicine; Genetics & Heredity; Public, Environmental & Occupational Health; DNA methylation; imputation; penalized functional regression; epigenome-wide association study; MISSING VALUE ESTIMATION; GENE-EXPRESSION DATA; GENOTYPE IMPUTATION; HUMAN TISSUES; GENOMIC CHARACTERIZATION; AFRICAN-AMERICANS; SEQUENCES; PREDICTION; DESIGNS; HEALTH

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).