High_resolution_genetic_mapping_of_causal_regulatory_interactions_in_the_human_genome-sc-5488
High resolution genetic mapping of putatively causal interactions between regions of open chromatin.
Physical interaction of distal regulatory elements in three-dimensional space poses a challenge for studies of common disease because noncoding risk variants may be substantial distances from the genes they regulate. Experimental methods to capture these interactions, such as chromosome conformation capture (CCC), usually cannot assign causal direction of effect between regulatory elements, an important component of disease fine-mapping. Here, we developed a Bayesian hierarchical approach that uses two-stage least squares to capture causal interactions between regions of open chromatin. Applied to a novel ATAC-seq data from 100 individuals, the model mapped over 15,000 high confidence causal interactions. Over 60% of interactions occurred over <20Kb, where CCC-based methods typically perform poorly. For a fraction of loci, we identified a single variant that alters accessibility across multiple peaks, and experimentally validated an interaction at a locus associated with rheumatoid arthritis risk using CRISPR engineering. Our study highlights how association genetics of chromatin state is a powerful approach for identifying interactions between regulatory elements. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/.
Physical interaction of distal regulatory elements in three-dimensional space poses a challenge for studies of common disease because noncoding risk variants may be substantial distances from the genes they regulate. Experimental methods to capture these interactions, such as chromosome conformation capture (CCC), usually cannot assign causal direction of effect between regulatory elements, an important component of disease fine-mapping. Here, we developed a Bayesian hierarchical approach that uses two-stage least squares to capture causal interactions between regions of open chromatin. Applied to a novel ATAC-seq data from 100 individuals, the model mapped over 15,000 high confidence causal interactions. Over 60% of interactions occurred over <20Kb, where CCC-based methods typically perform poorly. For a fraction of loci, we identified a single variant that alters accessibility across multiple peaks, and experimentally validated an interaction at a locus associated with rheumatoid arthritis risk using CRISPR engineering. Our study highlights how association genetics of chromatin state is a powerful approach for identifying interactions between regulatory elements. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/.