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Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P< 5 × 10−8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.

Translation of noncoding common variant association signals into meaningful molecular and biological mechanisms explaining disease susceptibility remains challenging. For the type 2 diabetes association signal in JAZF1 intron 1, we hypothesized that the underlying risk variants have cis-regulatory effects in islets or other type 2 diabetes--relevant cell types. We used maps of experimentally predicted open chromatin regions to prioritize variants for functional follow-up studies of transcriptional activity. Twelve regions containing type 2 diabetes--associated variants were tested for enhancer activity in 832/13 and MIN6 insulinoma cells. Three regions exhibited enhancer activity and only rs1635852 displayed allelic differences in enhancer activity; the type 2 diabetes risk allele T showed lower transcriptional activity than the nonrisk allele C. This risk allele showed increased binding to protein complexes, suggesting that it functions as part of a transcriptional repressor complex. We applied DNA affinity capture to identify factors in the complex and determined that the risk allele preferentially binds the pancreatic master regulator PDX1. These data suggest that the rs1635852 region in JAZF1 intron 1 is part of a cis-regulatory complex and that maps of open chromatin are useful to guide identification of variants with allelic differences in regulatory activity at type 2 diabetes loci., Genome-wide association studies (GWAS) have identified >50 genome-wide significant loci associated with type 2 diabetes to date (1). For many of these loci, association signals are localized to nonprotein-coding intronic [...]

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.

Genome-wide association studies (GWAS) have identified hundreds of risk loci for cardiometabolic and lipid-related traits. GWAS loci are enriched in tissue-specific transcriptional regulatory elements that affect target gene expression driving physiological changes. Most GWAS variants are located in non-coding regions, which makes identifying functional variants and target genes challenging. Previously, we identified GWAS signals colocalized with molecular quantitative trait loci for liver gene expression (eQTL) and liver accessible chromatin (caQTL) to predict regulatory variants for lipid and cardiometabolic traits. These caQTLs are rs13395911, rs11644920, rs34003091 and rs9556404 associated with liver enzymes, LDL-cholesterol, and triglycerides. To evaluate the predicted mechanisms, we tested these variants with surrounding regulatory elements for allelic effects on transcriptional reporter activity and for effects on target gene expression in HepG2 cell line. Along with the two previously reported intronic caQTL variants, rs13395911 and rs11644920,we observed significant (P<0.05) allelic differences in transcriptional activity for rs9556404 and rs34003091, located at the promoters of GPR180and ZNF329, respectively. Based on eQTL and HiC data, the predicted genes for intronic enhancers rs13395911 and rs11644920 are EFHD1and LITAF, respectively. To confirm the target genes for these two variants, we deployed inducible CRISPRi system by stably expressing dCas9 fused to a KRAB-repressor domain in HepG2 and targeting guide RNAs (gRNAs) to the DNA elements that span caQTL variants. For each element, we introduced ≥6 gRNAs into cells via lentiviruses and measured gene expression using qPCR. Compared to pools of non-targeted control gRNAs, preliminary analyses using pools of enhancer-targeting gRNAs surrounding rs13395911 and rs11644920 led to 30.3% lower EFHD1and 38.6% lower LITAFexpression at 7 days post-transduction, respectively. Thus, we identified and validated mechanisms by which DNA variants alter transcriptional activity and identified the target genes for lipid-related GWAS loci. These results suggest that this framework can be scaled up to map thousands of enhancers to genes in their native genomic state.