Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Resolving the molecular processes that mediate genetic risk remains a challenge because most disease-associated variants are non-coding and functional characterization of these signals requires knowledge of the specific tissues and cell-types in which they operate. To address this challenge, we developed a framework for integrating tissue-specific gene expression and epigenomic maps to obtain "tissue-of-action" (TOA) scores for each association signal by systematically partitioning posterior probabilities from Bayesian fine-mapping. We applied this scheme to credible set variants for 380 association signals from a recent GWAS meta-analysis of type 2 diabetes (T2D) in Europeans. The resulting tissue profiles underscored a predominant role for pancreatic islets and, to a lesser extent, adipose and liver, particularly among signals with greater fine-mapping resolution. We incorporated resulting TOA scores into a rule-based classifier and validated the tissue assignments through comparison with data from cis-eQTL enrichment, functional fine-mapping, RNA co-expression, and patterns of physiological association. In addition to implicating signals with a single TOA, we found evidence for signals with shared effects in multiple tissues as well as distinct tissue profiles between independent signals within heterogeneous loci. Lastly, we demonstrated that TOA scores can be directly coupled with eQTL colocalization to further resolve effector transcripts at T2D signals. This framework guides mechanistic inference by directing functional validation studies to the most relevant tissues and can gain power as fine-mapping resolution and cell-specific annotations become richer. This method is generalizable to all complex traits with relevant annotation data and is made available as an R package.

Original publication




Journal article


Am J Hum Genet

Publication Date





1011 - 1028


GWAS, TACTICAL, chromatin, complex traits, eQTL, fine-mapping, gene expression, molecular epigenomics, multi-omic, type 2 diabetes, Adipose Tissue, Chromosome Mapping, Cluster Analysis, Computational Biology, Diabetes Mellitus, Type 2, Enhancer Elements, Genetic, Epigenomics, Gene Expression Regulation, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Islets of Langerhans, Linkage Disequilibrium, Liver, Models, Statistical, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Principal Component Analysis, Probability, Quantitative Trait Loci