Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation


Josine L. Min, University of Bristol
Gibran Hemani, University of Bristol
Eilis Hannon, University of Exeter Medical School
Koen F. Dekkers, Leids Universitair Medisch Centrum
Juan Castillo-Fernandez, King's College London
René Luijk, Leids Universitair Medisch Centrum
Elena Carnero-Montoro, King's College London
Daniel J. Lawson, University of Bristol
Kimberley Burrows, University of Bristol
Matthew Suderman, University of Bristol
Andrew D. Bretherick, The University of Edinburgh
Tom G. Richardson, University of Bristol
Johanna Klughammer, Osterreichische Akademie Der Wissenschaften
Valentina Iotchkova, MRC Weatherall Institute of Molecular Medicine
Gemma Sharp, University of Bristol
Ahmad Al Khleifat, Maurice Wohl Clinical Neuroscience Institute
Aleksey Shatunov, Maurice Wohl Clinical Neuroscience Institute
Alfredo Iacoangeli, Maurice Wohl Clinical Neuroscience Institute
Wendy L. McArdle, Bristol Medical School
Karen M. Ho, Bristol Medical School
Ashish Kumar, Karolinska Institutet
Cilla Söderhäll, Karolinska Institutet
Carolina Soriano-Tárraga, Hospital del Mar
Eva Giralt-Steinhauer, Hospital del Mar
Nabila Kazmi, University of Bristol
Dan Mason, Bradford Institute for Health Research
Allan F. McRae, The University of Queensland
David L. Corcoran, Duke University
Karen Sugden, Duke University
Silva Kasela, Tartu Ülikooli Genoomika Instituut
Alexia Cardona, School of Clinical Medicine
Felix R. Day, School of Clinical Medicine
Giovanni Cugliari, Università degli Studi di Torino, Scuola di Medicina


Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15–17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype–phenotype map than previously anticipated.

Publication Title

Nature Genetics