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

TitleAcross-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression.
Publication TypeJournal Article
Year of Publication2016
AuthorsZhang G, Huang K-C, Xu Z, Tzeng J-Y, Conneely KN, Guan W, Kang J, Li Y
JournalGenet Epidemiol
Volume40
Issue4
Pagination333-40
Date Published2016 May
ISSN1098-2272
KeywordsCpG Islands, DNA Methylation, Epigenesis, Genetic, Genetic Association Studies, Humans, Leukemia, Myeloid, Acute, Linear Models, Models, Genetic
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).

DOI10.1002/gepi.21969
Alternate JournalGenet. Epidemiol.
Original PublicationAcross-platform imputation of DNA methylation levels incorporating nonlocal information using penalized functional regression.
PubMed ID27061717
PubMed Central IDPMC4862742
Grant ListR01 HG006292 / HG / NHGRI NIH HHS / United States
R01 HG006703 / HG / NHGRI NIH HHS / United States
R01 MH105561 / MH / NIMH NIH HHS / United States
R01MH105561 / MH / NIMH NIH HHS / United States
R01HG006292 / HG / NHGRI NIH HHS / United States
R01HG006703 / HG / NHGRI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: