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Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci.


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Authors

Martin, Paul 
McGovern, Amanda 
Orozco, Gisela 
Duffus, Kate 
Yarwood, Annie 

Abstract

Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions. We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. Here we report numerous looping interactions and provide evidence that only a minority of interactions are common to both B- and T-cell lines, suggesting interactions may be highly cell-type specific; some disease-associated SNPs do not interact with the nearest gene but with more compelling candidate genes (for example, FOXO1, AZI2) often situated several megabases away; and finally, regions associated with different autoimmune diseases interact with each other and the same promoter suggesting common autoimmune gene targets (for example, PTPRC, DEXI and ZFP36L1).

Description

Keywords

Adolescent, Autoimmune Diseases, B-Lymphocytes, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, T-Lymphocytes

Journal Title

Nat Commun

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

6

Publisher

Springer Science and Business Media LLC
Sponsorship
Wellcome Trust (089989/Z/09/Z)
Wellcome Trust (100140/Z/12/Z)
European Commission (241447)
Wellcome Trust (091157/Z/10/B)
Biotechnology and Biological Sciences Research Council (BBS/E/B/000C0405)
Biotechnology and Biological Sciences Research Council (BBS/E/B/000C0404)
We thank Frank Dudbridge for providing the R scripts to analyse the interaction data. We would like to acknowledge the Faculty of Life Sciences Genomics Facility, the assistance given by IT Services and the use of the Computational Shared Facility at The University of Manchester. This work was funded by Arthritis Research UK (grant numbers 20385, 20571 (K.D.)); Wellcome Trust Research Career Development Fellowship (G.O., AM 095684); Wellcome Trust (097820/Z/11/B); S.E. is supported through the European Union’s FP7 Health Programme, under the grant agreement FP7-HEALTH-F2-2012-305549 (Euro-TEAM). A.Y. is supported by the Innovative Medicines Initiative (BeTheCure project 115142); C.W. by the Wellcome Trust (089989); C.W. and N.C. by the Wellcome Trust (091157), JDRF (9-2011-253) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). The research leading to these results has received funding from the European Union’s 7th Framework Programme (FP7/2007-2013) under grant agreement no.241447 (NAIMIT) and supported by the National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit (S.E., A.B., J.W.). P.F. and S.S. were supported by Biotechnology and Biological Sciences Research Council UK grant BBS/E/B/000C0405.