MethylPCA: a toolkit to control for confounders in methylome-wide association studies

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  • Chen, Wenan
    • Other Affiliation: Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
  • Gao, Guimin
    • Other Affiliation: Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
  • Nerella, Srilaxmi
    • Other Affiliation: Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA, USA
  • Hultman, Christina M
    • Other Affiliation: Swedish Schizophrenia Consortium, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
  • Magnusson, Patrik KE
    • Other Affiliation: Swedish Schizophrenia Consortium, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
  • Sullivan, Patrick
    • Affiliation: School of Medicine, Department of Genetics
    • Other Affiliation: Swedish Schizophrenia Consortium, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
  • Aberg, Karolina A
    • Other Affiliation: Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA, USA
  • van den Oord, Edwin JCG
    • Other Affiliation: Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA, USA
Abstract
  • Abstract Background In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. Result We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. Conclusions MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS.
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Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Wenan Chen et al.; licensee BioMed Central Ltd.
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Journal title
  • BMC Bioinformatics
Journal volume
  • 14
Journal issue
  • 1
Page start
  • 74
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1471-2105
Bibliographic citation
  • BMC Bioinformatics. 2013 Mar 02;14(1):74
Publisher
  • BioMed Central Ltd
Access right
  • Open Access
Date uploaded
  • March 13, 2013

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file details 1471-2105-14-74.pdf 2019-05-06 Public Download
file details Supplemental material with a brief proof of the Equations (1)-(3), detail on the simulation study, and description of laboratory procedures and QC of the real methylation data. 2019-05-06 Public Download
file details 1471-2105-14-74.xml 2019-05-06 Public Download
file details User guide of the software. 2019-05-06 Public Download