A Multi-omic Integrative Scheme Characterizes Tissues of Action at Loci Associated with Type 2 Diabetes

Abstract

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 coexpression, 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.

Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

“…Previous work has used tissue specificity to inform tissues of action for causal genes [ 53 ], and others have further partitioned cardiometabolic risk loci into groups with primary roles in the pancreas, liver, adipose tissues, and others [ 14 ]. We hypothesized that genes colocalized exclusively in a single tissue might similarly form functional subgroups.…”

Associations between Serum Betaine, Methyl-Metabolizing Genetic Polymorphisms and Risk of Incident Type 2 Diabetes: A Prospective Cohort Study in Community-Dwelling Chinese Adults

“…Several genetic variants in human genes have been recognized as risk factors of type 2 diabetes [28]. MTHFR plays an important role in one-carbon cycle and DNA methylation, genetic variations of which may result in disturbances of one-carbon metabolism and DNA methylation.…”

Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

“…The respective directionality of the effect sizes and the presence-absence pattern of meQTLs, eQTLs and eQTMs can therefore provide clues as to whether methylation of the respective CpG sites leads to an increase or decrease in gene expression. While sometimes hard to interpret, especially as not all associations replicate under all conditions and in all studies, meQTLs, eQTLs and eQTMs are key elements to be considered in the interpretation of any association with DNA methylation [40]. Histograms presented in the margins of the scatterplot show that most CpG sites are either fully or unmethylated, and this is consistent between both populations (see [28] for cohort details).…”