Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression
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- 作者：Guosheng Zhang ; Kuan-Chieh Huang ; Zheng Xu ; Jung-Ying Tzeng ; Karen N. Conneely ; Weihua Guan ; Jian Kang and Yun Li
- 关键词：DNA methylation ; imputation ; penalized functional regression ; epigenome-wide association study
- 刊名：Genetic Epidemiology
- 出版年：2016
- 出版时间：May 2016
- 年：2016
- 卷：40
- 期：4
- 页码：333-340
- 全文大小：616K
- ISSN：1098-2272
文摘
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).