Title | Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Zhang, Guosheng, Kuan-Chieh Huang, Zheng Xu, Jung-Ying Tzeng, Karen N. Conneely, Weihua Guan, Jian Kang, and Yun Li |
Journal | Genet Epidemiol |
Volume | 40 |
Issue | 4 |
Pagination | 333-40 |
Date Published | 2016 May |
ISSN | 1098-2272 |
Keywords | CpG 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). |
DOI | 10.1002/gepi.21969 |
Alternate Journal | Genet Epidemiol |
Original Publication | Across-platform imputation of DNA methylation levels incorporating nonlocal information using penalized functional regression. |
PubMed ID | 27061717 |
PubMed Central ID | PMC4862742 |
Grant List | R01 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 |
Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression.
Project: