Year: 2016
Type: preprint
Abstract: 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 .
DOI: 10.1101/066340
Authors: Elior Rahmani, Liat Shenhav, Regev Schweiger, Paul Yousefi, Karen Huen, Brenda Eskenazi, Celeste Eng, Scott Huntsman, Donglei Hu, Joshua Galanter, Sam Oh, Melanie Waldenberger, Konstantin Strauch, Harald Grallert, Thomas Meitinger, Christian Gieger, Nina Holland, Esteban Burchard, Noah Zaitlen, Eran Halperin
Institutions: Tel Aviv University, University of California, Berkeley, University of California, San Francisco, Helmholtz Munich, Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München, German Center for Diabetes Research, Institut für Humangenetik, Technical University of Munich, University of California, Los Angeles
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: National Science Foundation, Tobacco-Related Disease Research Program, American Asthma Foundation, European Commission, German-Israeli Foundation for Scientific Research and Development, Bundesministerium für Bildung und Forschung, Bundesministerium für Gesundheit, Israel Science Foundation, National Institutes of Health, Münchner Zentrum für Gesundheitswissenschaften, National Heart, Lung, and Blood Institute, National Institute of Environmental Health Sciences
Awards: G1846019065, BSF:2012304:Methods for Preprocessing Population Sequence Data, Richer Models of Asthma Risk: Bridging the Environment-Genetics Divide, G2026444946, G2802321639, G3084499258, G3494851284, G3757124753, G393177772, Biobank Standardisation and Harmonisation for Research Excellence in the European Union, G4650774565, G4718930887, G5035092382, Methods for Integrated analysis of Multiple Omics datasets, G6703504239, Systems Biology to Identify Molecular Targets for Vascular Disease Treatment, Center for Children's Environmental Health Research, G6966137936, G7278018548, G7292241015, G7307112236, G7805851174, G7884706843, G866381394, G9000096963, G9000143814
OpenAlex ID: W2488409386