Context-specific regulatory networks identify key regulators of complex traits. G. Quon1,2, D. Marbach1,2, S. Feizi1,2, M. Grzadkowski1, M. Kellis1,2 1) CSAIL, MIT, Cambridge, MA; 2) Broad Institute, Cambridge, MA.

   Genome-wide association studies (GWAS) have identified thousands of single nucleotide variants associated with diverse human traits, but understanding their combined action in complex systems remains an open challenge. With more than 80% of lead GWAS SNPs located in non-coding regions of the genome rich in regulatory elements, functionally characterizing these variants necessitates knowledge of (1) the locations of cell type specific enhancers; (2) the identity of the target genes of those enhancers; and (3) the interactions between these target genes to identify disrupted pathways and subnetworks. Using enhancer and promoter maps for 111 cell types constructed by the Roadmap Epigenomics Consortium, we have constructed directed context-specific and cell type specific networks, where nodes represent both genes and non-coding regulatory elements (enhancers, promoters), and edges lead from transcription factors to regulatory elements, and regulatory elements to genes. These meta-networks, where nodes consist of both genes and non-coding regulatory elements, enable context-specific network analysis that can yield insight into the role of different cell types in complex traits, and to our knowledge have not been previously explored. To leverage these networks for GWAS analysis, we developed an efficient probabilistic model to map GWAS variants to candidate disrupted regulatory elements, and use each context-specific network to (1) identify trait-associated genes whose regulation is disrupted by non-coding variants; (2) identify master TF regulators of the trait-associated genes; and (3) identify other genes (not proximal to GWAS variants) involved in the trait. We predicted modules of non-coding variants associated with brain, cardiovascular, lipid, and immune-mediated disorders, as well as their regulators. Predicted regulatory modules and transcription factors involved in HDL and LDL cholesterol levels replicated across multiple studies and are most highly expressed in liver cell types. Furthermore, gene mutations reported in the MGI database lead to abnormalities including perturbed circulating lipid levels and susceptibility to atherosclerosis. Modules and regulators predicted for multiple sclerosis and Crohns disease are most highly expressed in CD4+,CD8+, and CD34+ cells, and their mutants lead to defects in B-cell and NK-cell morphology and circulating levels.

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