Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.
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.
|Year of Publication
PLoS Comput Biol
|PubMed Central ID
MC_UU_00007/10 / MRC_ / Medical Research Council / United Kingdom
BBS/E/D/20211553 / BB_ / Biotechnology and Biological Sciences Research Council / United Kingdom
BBS/E/D/20211551 / BB_ / Biotechnology and Biological Sciences Research Council / United Kingdom
BBS/E/D/20211552 / BB_ / Biotechnology and Biological Sciences Research Council / United Kingdom
BBS/E/D/20211554 / BB_ / Biotechnology and Biological Sciences Research Council / United Kingdom
MC_PC_U127592696 / MRC_ / Medical Research Council / United Kingdom
MR/P008887/1 / MRC_ / Medical Research Council / United Kingdom
R00 HG006698 / HG / NHGRI NIH HHS / United States
BB/I001107/1 / BB_ / Biotechnology and Biological Sciences Research Council / United Kingdom