Cell-type deconvolution in epigenome-wide association studies: a review and recommendations

Author:

Teschendorff Andrew E123,Zheng Shijie C14

Affiliation:

1. CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

2. Department of Women's Cancer, University College London, 74 Huntley Street, London WC1E 6AU, UK

3. Statistical Cancer Genomics, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK

4. University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China

Abstract

A major challenge faced by epigenome-wide association studies (EWAS) is cell-type heterogeneity. As many EWAS have already demonstrated, adjusting for changes in cell-type composition can be critical when analyzing and interpreting findings from such studies. Because of their importance, a great number of different statistical algorithms, which adjust for cell-type composition, have been proposed. Some of the methods are ‘reference based’ in that they require a priori defined reference DNA methylation profiles of cell types that are present in the tissue of interest, while other algorithms are ‘reference free.’ At present, however, it is unclear how best to adjust for cell-type heterogeneity, as this may also largely depend on the type of tissue and phenotype being considered. Here, we provide a critical review of the major existing algorithms for correcting cell-type composition in the context of Illumina Infinium Methylation Beadarrays, with the aim of providing useful recommendations to the EWAS community.

Publisher

Future Medicine Ltd

Subject

Cancer Research,Genetics

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