TY - JOUR T1 - Genome-wide methylation data mirror ancestry information JF - bioRxiv DO - 10.1101/066340 SP - 066340 AU - Elior Rahmani AU - Liat Shenhav AU - Regev Schweiger AU - Paul Yousefi AU - Karen Huen AU - Brenda Eskenazi AU - Celeste Eng AU - Scott Huntsman AU - Donglei Hu AU - Joshua Galanter AU - Sam Oh AU - Melanie Waldenberger AU - Konstantin Strauch AU - Harald Grallert AU - Thomas Meitinger AU - Christian Gieger AU - Nina Holland AU - Esteban Burchard AU - Noah Zaitlen AU - Eran Halperin Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/12/10/066340.abstract N2 - Genetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data. We demonstrate, using three large cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data, and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data. EPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io. ER -