Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease
AuthorsBaillie, J. Kenneth
Haley, Christopher S.
Neyton, Lucile P. A.
Stahl, Eli A.
Brown, J. Ben
Faulkner, Geoffrey J.
Bajic, Vladimir B.
Wells, Christine A.
Freeman, Thomas C.
Forrest, Alistair R. R.
Hume, David A.
KAUST DepartmentApplied Mathematics and Computational Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/656596
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AbstractGenetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.
CitationBaillie, J. K., Bretherick, A., Haley, C. S., Clohisey, S., Gray, A., … Neyton, L. P. A. (2018). Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease. PLOS Computational Biology, 14(3), e1005934. doi:10.1371/journal.pcbi.1005934
SponsorsJKB gratefully acknowledges funding support from a Wellcome Trust Intermediate Clinical Fellowship (103258/Z/13/Z) and a Wellcome-Beit Prize (103258/Z/13/A), BBSRC Institute Strategic Programme Grants to the Roslin Institute (BBS/E/D/20211551, BBS/E/D/20211552, BBS/E/D/20211553, BBS/E/D/20231760), the UK Intensive Care Foundation, and the Edinburgh Clinical Academic Track (ECAT) scheme. Funds were provided to the Roslin Institute through a BBSRC Strategic Programme Grant (JKB, SC, CSH, GJF, TCF, DAH; BBS/E/D/20211551, BBS/E/D/20231760). We acknowledge the financial support provided by the MRC-HGU Core Fund (CSH, AT). FANTOM5 was made possible by a Research Grant for RIKEN Omics Science Center from MEXT to YH and a Grant of the Innovative Cell Biology by Innovative Technology (Cell Innovation Program) from the MEXT, Japan to YH. RIKEN Centre for Life Science Technologies, Division of Genomic Technologies members (RIKEN CLST (DGT)) are supported by institutional funds from the MEXT, Japan. RIKEN Preventive Medicine and Diagnosis Innovation Program members are supported by funding from MEXT, Japan. ARRF is supported by a Senior Cancer Research Fellowship from the Cancer Research Trust and funds raised by the Ride to Conquer Cancer. JB is supported by Wellcome Trust grant WT098051.1. GJF acknowledges the support of an NHMRC Career Development Fellowship (GNT1045237), NHMRC Project Grants (GNT1042449, GNT1045991, GNT1067983 and GNT1068789), and the EU FP7 under grant agreement No. 259743 underpinning the MODHEP consortium. MR was supported by grants from the Deutsch Forschungsgemeinschaft, the German Cancer Aid and the Rudolf Bartling Foundation. RA was supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 638273). US and VBB are supported by the KAUST Base Research Fund to VBB and KAUST CBRC Base Fund. RA and AS were supported by funds from FP7/2007-2013/ERC grant agreement 204135, the Novo Nordisk foundation, and the Lundbeck Foundation and the Danish Cancer Society. CAW is supported by a Queensland Government Smart Futures Fellowship, and samples were collected under Australian National Health and Medical Research council project grants 455947 and 597452, under agreement from the Australian Red Cross 11-02QLD-10 and the University of QLD ethics committee. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip
PublisherPublic Library of Science (PLoS)
JournalPLOS Computational Biology