PT  - JOURNAL ARTICLE
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
TI  - Genome-wide methylation data mirror ancestry information
AID  - 10.1101/066340
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 066340
4099  - http://biorxiv.org/content/early/2016/07/27/066340.short
4100  - http://biorxiv.org/content/early/2016/07/27/066340.full
AB  - 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.