Resources to interpret genetic signals and multi-tissue mechanisms for type 2 diabetes and related traits

Univ Of North Carolina Chapel Hill, Chapel Hill NC

Investigators

Abstract

ABSTRACT Genome-wide association studies (GWAS) have successfully detected thousands of association signals for type 2 diabetes (T2D) and related metabolic traits, a key step toward identifying genes and causal pathways that modify individual-level heterogeneity in disease risk. Most genetic associations are driven by noncoding variants, motivating discovery of regulatory mechanisms and potential effector genes, especially in pancreatic islets, adipose, skeletal muscle, and liver. However, variant effects on regulatory elements, genes, and gene functions are often ambiguous and not comparable across studies due to inconsistently defined association signals and non-standardized study designs and analysis strategies. To capitalize on investments in genetic research for T2D, the community needs harmonized cross-tissue data to prioritize variants and genes for in-depth mechanistic studies, risk prediction, and identification of drug targets. To guide detailed mechanistic studies, harmonized cross-cell type data on plausible gene functions are needed, but are challenging for any one lab to perform at scale. Substantial evidence shows that mitochondrial dysfunction plays a key role in multiple tissues involved in T2D pathogenesis through defects in pancreatic beta-cell dysfunction, insulin resistance, adipose tissue beiging, obesity, and complications. Mitochondria play a key role in cellular energy metabolism through the production of ATP from oxidative phosphorylation, generation of reactive oxygen species, and apoptosis, and T2D GWAS signals are enriched for mitochondrial genes. Demonstration of mitochondrial defects for genes not previously shown to influence energy metabolism would motivate future studies of specific mechanisms. Here, we will provide resources to meet these needs. We will detect regulatory variants and elements at T2D GWAS signals in disease-relevant cells/tissues by developing a uniform pipeline to discover genetic variants associated with chromatin accessibility (caQTL) and colocalizing caQTL and T2D signals. We will identify candidate effector genes for T2D GWAS signals by systematically integrating molecular quantitative trait loci and chromatin looping data to predict which genes are altered by noncoding risk variants, and we will integrate these results, by signal, with predictive models and data about tissues of action at cell-type resolution. To assess gene function, we will perform harmonized genome-wide assays to identify effects on mitochondrial function for genes at T2D GWAS signals and at pathway hubs in beta cells, adipocytes, myocytes and hepatocytes. Together, these analyses and data about T2D GWAS signals will provide valuable resources to the community, including the most likely regulatory variants and elements, tissues of action, and effector genes, as well as evidence of gene effects on mitochondrial function. We will collate these comprehensive resources by signal and assimilate them into data portals for the community.