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PLoS Comput Biol. 2018 Mar 1;14(3):e1005934. doi: 10.1371/journal.pcbi.1005934. eCollection 2018 Mar.

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Author information

1
Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom.
2
Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom.
3
Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, United Kingdom.
4
Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom.
5
Edinburgh Parallel Computing Centre, The University of Edinburgh, Edinburgh, United Kingdom.
6
Statistical Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
7
Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, United States of America.
8
The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark.
9
Department of Statistics, University of California, Berkeley, United States of America.
10
Mater Research Institute, University of Queensland, University of Queensland, Brisbane, Australia.
11
RIKEN Omics Science Center, Yokohama, Japan, Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan.
12
Department for Infectious Disease Informatics, Public Health England, Colindale, United Kingdom.
13
RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan.
14
King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center, Thuwal, Kingdom of Saudi Arabia.
15
German Center for Neurodegenerative Diseases, Tübingen, Germany.
16
Dept. Hematology, University Hospital Regensburg, Regensburg, Germany.
17
Australian Institute for Bioengineering and Nanotechnology, University of Queensland, St Lucia, Brisbane Australia.
18
Broad Institute of Harvard and MIT, Cambridge, United States of America.
19
Harry Perkins Institute of Medical Research, and the Centre for Medical Research, University of Western Australia, QEII Medical Centre, Nedlands, Perth, Western Australia, Australia.

Abstract

Genetic 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.

PMID:
29494619
PMCID:
PMC5849332
DOI:
10.1371/journal.pcbi.1005934
[Indexed for MEDLINE]
Free PMC Article

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