Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Lu Chen(University of Cambridge), Bing Ge(McGill University), Francesco Paolo Casale(European Bioinformatics Institute), Louella Vasquez(Wellcome Sanger Institute), Tony Kwan(McGill University), Diego Garrido-Martín(Universitat Pompeu Fabra), Stephen Watt(Wellcome Sanger Institute), Yan Yan(Wellcome Sanger Institute), Kousik Kundu(University of Cambridge), Simone Ecker(Spanish National Cancer Research Centre), Avik Datta(European Bioinformatics Institute), David Richardson(European Bioinformatics Institute), Frances Burden(National Health Service), Daniel G. Mead(Wellcome Sanger Institute), Alice Mann(Wellcome Sanger Institute), José M. Fernández(Spanish National Cancer Research Centre), Sophia Rowlston(National Health Service), Steven P. Wilder(European Bioinformatics Institute), Samantha Farrow(National Health Service), Xiaojian Shao(McGill University), John Lambourne(National Health Service), Adriana Redensek(McGill University), Cornelis A. Albers(Radboud University Nijmegen), Vyacheslav Amstislavskiy(Max Planck Institute for Molecular Genetics), Sofie Ashford(National Health Service), Kim Berentsen(Radboud University Nijmegen), Lorenzo Bomba(Wellcome Sanger Institute), Guillaume Bourque(McGill University), David Bujold(McGill University), Stephan Busche(McGill University), Maxime Caron(McGill University), Shu‐Huang Chen(McGill University), Warren Cheung(McGill University), Oliver Delaneau(University of Geneva), Emmanouil T. Dermitzakis(University of Geneva), Heather Elding(Wellcome Sanger Institute), Irina Colgiu(Wellcome Sanger Institute), Frederik Otzen Bagger(National Health Service), Paul Flicek(European Bioinformatics Institute), Ehsan Habibi(Radboud University Nijmegen), Valentina Iotchkova(European Bioinformatics Institute), Eva M. Janssen‐Megens(Radboud University Nijmegen), Bowon Kim(Radboud University Nijmegen), Hans Lehrach(Max Planck Institute for Molecular Genetics), Ernesto Lowy(European Bioinformatics Institute), Amit Mandoli(Radboud University Nijmegen), Filomena Matarese(Radboud University Nijmegen), Matthew T. Maurano(NYU Langone Health), John Morris(McGill University), Véra Pancaldi(Spanish National Cancer Research Centre), Farzin Pourfarzad(Sanquin), Karola Rehnström(National Health Service), Augusto Rendon(University of Cambridge), Thomas S. Risch(Max Planck Institute for Molecular Genetics), Nilofar Sharifi(Radboud University Nijmegen), Marie-Michelle Simon(McGill University), Marc Sultan(Max Planck Institute for Molecular Genetics), Alfonso Valencia(Spanish National Cancer Research Centre), Klaudia Walter(Wellcome Sanger Institute), Shuang-Yin Wang(Radboud University Nijmegen), Mattia Frontini(National Health Service), Stylianos E. Antonarakis(University of Geneva), Laura Clarke(European Bioinformatics Institute), Marie‐Laure Yaspo(Max Planck Institute for Molecular Genetics), Stephan Beck(University College London), Roderic Guigó(Universitat Pompeu Fabra), Daniel Rico(Spanish National Cancer Research Centre), Joost H.A. Martens(Radboud University Nijmegen), Willem H. Ouwehand(National Health Service), Taco W. Kuijpers(University of Cambridge), Dirk S. Paul(University of Cambridge), Hendrik G. Stunnenberg(Radboud University Nijmegen), Oliver Stegle(European Bioinformatics Institute), Kate Downes(National Health Service), Tomi Pastinen(McGill University), Nicole Soranzo(University of Cambridge)
Cited by 776Open Access
Abstract
T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.
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