The genetic regulatory signature of type 2 diabetes in human skeletal muscle

Abstract

Type 2 diabetes (T2D) results from the combined effects of genetic and environmental factors on multiple tissues over time. Of the >100 variants associated with T2D and related traits in genome-wide association studies (GWAS), >90% occur in non-coding regions, suggesting a strong regulatory component to T2D risk. Here to understand how T2D status, metabolic traits and genetic variation influence gene expression, we analyse skeletal muscle biopsies from 271 well-phenotyped Finnish participants with glucose tolerance ranging from normal to newly diagnosed T2D. We perform high-depth strand-specific mRNA-sequencing and dense genotyping. Computational integration of these data with epigenome data, including ATAC-seq on skeletal muscle, and transcriptome data across diverse tissues reveals that the tissue-specific genetic regulatory architecture of skeletal muscle is highly enriched in muscle stretch/super enhancers, including some that overlap T2D GWAS variants. In one such example, T2D risk alleles residing in a muscle stretch/super enhancer are linked to increased expression and alternative splicing of muscle-specific isoforms of ANK1.

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

“…Members of the NKX6 transcription factor family (including NKX6.3) are implicated in islet development and function44. A recent study describing the integration of skeletal muscle regulatory annotations with T2D association data highlighted the relationship between variants including rs515071 and rs508419 and the expression and splicing of ANK1 in skeletal muscle45; however, all variants influencing ANK1 have minimal impact on T2D-risk based on the genetic fine-mapping (PPA<1% in all three conditionally-decomposed signals). Collectively, these data indicate that the mechanism of T2D predisposition at this locus is more likely to be mediated through reduced islet expression of NKX6.3 than altered muscle expression of ANK1 .…”

10 Years of GWAS Discovery: Biology, Function, and Translation

“…75,80 However, as increasingly diverse populations are genotyped and sequenced, more ethnic-specific alleles 82 From GWAS to Biology. Regulatory information on the key tissues of insulin action (fat, muscle, and liver) 82,83 and equivalent data from pancreatic islet material 67,84 have provided compelling evidence that the variants most strongly associated with T2D (as well as fasting glucose and other related quantitative traits) are preferentially located at active enhancers (particularly stretch enhancers) in pancreatic islets 67,84 and, to a lesser extent, at enhancers active in fat, muscle, and liver. 83,85 Increasing refinement of regulatory annotation has brought more precise localization of these global regulatory effects, for example, emphasizing specific transcription factor genes (such as FOXA2 [MIM: 600288]).…”

“…Regulatory information on the key tissues of insulin action (fat, muscle, and liver) 82,83 and equivalent data from pancreatic islet material 67,84 have provided compelling evidence that the variants most strongly associated with T2D (as well as fasting glucose and other related quantitative traits) are preferentially located at active enhancers (particularly stretch enhancers) in pancreatic islets 67,84 and, to a lesser extent, at enhancers active in fat, muscle, and liver. 83,85 Increasing refinement of regulatory annotation has brought more precise localization of these global regulatory effects, for example, emphasizing specific transcription factor genes (such as FOXA2 [MIM: 600288]). 86 These patterns of tissue-specific genomic enrichment tie in with studies of the physiological correlates of T2D risk alleles, as observed in physiological data from non-diabetic subjects; these have indicated that, whereas some T2D risk alleles have a primary effect on insulin action, most appear to be associated with reduced insulin secretion.…”

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

“…We used an information theory approach to define the islet expression specificity index (iESI) (Fig. S4) (7). iESI values near zero represent lowly and/or ubiquitously expressed genes, whereas values near one represent genes that are highly and specifically expressed in islets.…”

“…Chromatin-state maps identify regulatory regions, such as promoters and enhancers, but lack the resolution to pinpoint specific sites that may be bound and regulated by a TF. To refine the link between genetic variation, TF binding sites, and gene expression, we leveraged the highresolution ATAC-seq data to identify in vivo putative TF binding sites using CENTIPEDE as previously described (7,12). This approach detected high-quality footprints for many TFs, including the general CCCTC-binding factor (CTCF) and the TF Regulatory Factor X (RFX) (Fig.…”