A multi-omic integrative scheme characterizes tissues of action at loci associated with type 2 diabetes

Abstract

<jats:title>Abstract</jats:title><jats:p>Resolving the molecular processes that mediate genetic risk remains a challenge as most disease-associated variants are non-coding and functional and bioinformatic 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 (primarily from tissues involved in insulin secretion and action) to obtain <jats:italic>tissue-of-action</jats:italic> (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, subcutaneous adipose and liver, that was largely attributable to enhancer elements and transcribed regions, 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 <jats:italic>cis</jats:italic>-eQTL enrichment, functional fine-mapping, RNA co-expression, and patterns of physiological association. In addition to implicating signals with a single tissue-of-action, we also 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.</jats:p>

Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease

“…However, these approaches have been limited due to the fact that regulatory elements can regulate the expression of multiple genes over hundreds of kilobases 5,6 ; in a GWAS loci there can be multiple regulatory elements and hundreds of neutral variants in linkage disequilibrium with the causal regulatory variant; and regulatory variants frequently work in a spatiotemporal context not captured in previous eQTL analyses [7][8][9][10][11] . While for some diseases, such as diabetes, efforts have been made to fine map each GWAS locus and find potential associations between genetic variation, gene expression and disease at a genome-wide level [12][13][14] , the cardiac field still lacks a comprehensive resource for conducting in depth annotations of GWAS loci. Therefore, combining GWAS signals for multiple cardiac traits on hundreds of thousands of individuals with intermediate phenotypes, such as gene expression in multiple cardiac developmental stages, tissues, and cell types, would provide a powerful approach for fine mapping causal regulatory variants and understanding the molecular mechanisms underlying cardiovascular GWAS traits and disease.…”

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).…”

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 [28], and others have further partitioned cardiometabolic risk loci into groups with primary roles in the pancreas, liver, adipose tissues, and others [6]. We hypothesized that genes colocalized exclusively in a single tissue might similarly form functional subgroups.…”