cross-tissue methylation

Predicting DNA methylation level across human tissues

The difference in methylation across tissues is critical to cell differentiation and the key to understand epigenetics for complex diseases. We developed novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue. The method was evaluated in publicly available data and two studies using the latest Illumina BeadChips: A childhood asthma study with methylation measured in both peripheral blood and lymphoblastoid cell lines; and a study of post-operation atrial fibrillation with methylation in atrium, internal mammary artery and peripheral blood. Our results suggest that large scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve the understanding of epigenetics in hard-to-access tissues (e.g. atrium) and might enable non-invasive disease screening using epigenetic profiles. This method is applicable to other samples with other tissues. In order for other investigators to use our method, we have developed an R package which can be used to build the prediction model based on a training dataset with paired surrogate and target tissues, and generate predicted target tissue methylation in samples with only surrogate tissues. The program as well as detailed instruction and example dataset can be found in the ZIP package below.

Download: Package with R function and instructions prediction software package 20130820.

Additional supplementary figures: Online_additional_supplementary_figures_20131116