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/2016/07/27/066340.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 correct epigenome-wide association studies (EWAS) for confounding due to population structure. ER -