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Author Notes:

Correspondence to: Yun Li, Department of Genetics, Campus Box 7264, University of North Carolina, Chapel Hill, NC 27599. yunli@med.unc.edu; Or Jian Kang, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109; jiankang@umich.edu.

We thank the Cancer Genome Atlas (TCGA) project for generating the data.

The authors declare no conflict of interest.

Subjects:

Research Funding:

The research is supported by R01HG006292, R01HG006703 (awarded to Y.L.) R01MH105561 (awarded to J.K.) and P01 CA142538 (awarded to J.Y.T.).

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

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

Tools:

Journal Title:

Genetic Epidemiology

Volume:

Volume 40, Number 4

Publisher:

, Pages 333-340

Type of Work:

Article | Post-print: After Peer Review

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).

Copyright information:

© 2016 Wiley Periodicals, Inc.

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