Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.
By
John Kenneth Baillie,
Andrew Bretherick,
Christopher S. Haley,
Sara Mary Rose Clohisey,
Alan Gray,
Jeffrey Barret,
Eli A. Stahl,
Albert Tenesa,
Robin Andersson,
J. Ben Brown,
Geoffrey J Faulkner,
Marina Lizio,
Ulf Schaefer,
Carsten Daub,
Masayoshi Itoh,
Naoto Kondo,
Timo Lassmann,
Jun Kawai,
IIBDGC Consortium,
FANTOM5 Consortium,
Vladimir B. Bajic,
Peter Heutink,
Michael Rehli,
Hideya Kawaji,
Albin Sandelin,
Harukazu Suzuki,
Jack Satsangi,
Christine Wells,
Nir Hacohen,
Thomas C Freeman,
Yoshihide Hayashizaki,
Piero Carninci,
Alistair R.R. Forrest,
David A Hume
Posted 19 Dec 2016
bioRxiv DOI: 10.1101/095349
(published DOI: 10.1371/journal.pcbi.1005934)
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 patterns of transcriptional regulation. Accordingly, shared transcriptional regulation (coexpression) may help prioritise loci associated with a given trait, and help to identify the biological processes underlying it. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have quantified coexpression of RNAs originating from trait-associated regulatory regions using a novel analytical method (network density analysis; NDA). For most traits studied, sequence variants in regulatory regions were linked to tightly coexpressed networks that are likely to share important functional characteristics. These networks implicate particular cell types and tissues in disease pathogenesis; for example, variants associated with ulcerative colitis are linked to expression in gut tissue, whereas Crohn's disease variants are restricted to immune cells. We show that this coexpression signal provides additional independent information for fine mapping likely causative variants. This approach identifies additional genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. This approach enables a deeper biological understanding of the causal basis of complex traits.
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