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Across‐Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression
Author(s) -
Zhang Guosheng,
Huang KuanChieh,
Xu Zheng,
Tzeng JungYing,
Conneely Karen N.,
Guan Weihua,
Kang Jian,
Li Yun
Publication year - 2016
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21969
Subject(s) - imputation (statistics) , dna methylation , regression , regression analysis , computational biology , statistics , methylation , biology , dna , computer science , mathematics , genetics , missing data , gene , gene expression
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).