“…3 c). Among tissue-specific colocalizations, the most muscle- and pancreas-specific colocalizations were associated with the glycemic traits T2D, fasting insulin, and fasting glucose; the most adipose-specific colocalizations with WHR; and the most liver-specific colocalizations with levels of HDL and TG (Additional file 1 : Table S8), in accordance with heritability enrichments for the same traits in a recent study [ 47 ]. Fig.…”
Section: Resultssupporting
confidence: 83%
“…To answer such questions, we tested every candidate causal gene for differential expression under 21 physiological and pharmacological cardiometabolic regulators in human adipocytes, hepatocytes, and skeletal muscle cells [ 47 ] (Additional file 1 : Table S3). We thus generated a canvas of upstream molecular signals controlling the expression of candidate causal genes in relevant metabolic contexts (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The experiment was carried out in triplicate for each cell line-perturbation combination. RNA isolation, sequencing, quality control, and differential expression analysis were performed as previously described [ 47 ].…”
Section: Methodsmentioning
confidence: 99%
“…The code for colocalization analysis is given at https://www.github.com/mikegloudemans/insulin-resistance-colocalization [ 74 ]. The RNA-seq data for perturbation experiments have been uploaded to GEO with accession number GSE179347 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179347 ) [ 47 ]. Colocalization heatmaps depicting all genes tested at every locus in the style of Fig.…”
Background
Identification of causal genes for polygenic human diseases has been extremely challenging, and our understanding of how physiological and pharmacological stimuli modulate genetic risk at disease-associated loci is limited. Specifically, insulin resistance (IR), a common feature of cardiometabolic disease, including type 2 diabetes, obesity, and dyslipidemia, lacks well-powered genome-wide association studies (GWAS), and therefore, few associated loci and causal genes have been identified.
Methods
Here, we perform and integrate linkage disequilibrium (LD)-adjusted colocalization analyses across nine cardiometabolic traits (fasting insulin, fasting glucose, insulin sensitivity, insulin sensitivity index, type 2 diabetes, triglycerides, high-density lipoprotein, body mass index, and waist-hip ratio) combined with expression and splicing quantitative trait loci (eQTLs and sQTLs) from five metabolically relevant human tissues (subcutaneous and visceral adipose, skeletal muscle, liver, and pancreas). To elucidate the upstream regulators and functional mechanisms for these genes, we integrate their transcriptional responses to 21 relevant physiological and pharmacological perturbations in human adipocytes, hepatocytes, and skeletal muscle cells and map their protein-protein interactions.
Results
We identify 470 colocalized loci and prioritize 207 loci with a single colocalized gene. Patterns of shared colocalizations across traits and tissues highlight different potential roles for colocalized genes in cardiometabolic disease and distinguish several genes involved in pancreatic β-cell function from others with a more direct role in skeletal muscle, liver, and adipose tissues. At the loci with a single colocalized gene, 42 of these genes were regulated by insulin and 35 by glucose in perturbation experiments, including 17 regulated by both. Other metabolic perturbations regulated the expression of 30 more genes not regulated by glucose or insulin, pointing to other potential upstream regulators of candidate causal genes.
Conclusions
Our use of transcriptional responses under metabolic perturbations to contextualize genetic associations from our custom colocalization approach provides a list of likely causal genes and their upstream regulators in the context of IR-associated cardiometabolic risk.
“…3 c). Among tissue-specific colocalizations, the most muscle- and pancreas-specific colocalizations were associated with the glycemic traits T2D, fasting insulin, and fasting glucose; the most adipose-specific colocalizations with WHR; and the most liver-specific colocalizations with levels of HDL and TG (Additional file 1 : Table S8), in accordance with heritability enrichments for the same traits in a recent study [ 47 ]. Fig.…”
Section: Resultssupporting
confidence: 83%
“…To answer such questions, we tested every candidate causal gene for differential expression under 21 physiological and pharmacological cardiometabolic regulators in human adipocytes, hepatocytes, and skeletal muscle cells [ 47 ] (Additional file 1 : Table S3). We thus generated a canvas of upstream molecular signals controlling the expression of candidate causal genes in relevant metabolic contexts (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The experiment was carried out in triplicate for each cell line-perturbation combination. RNA isolation, sequencing, quality control, and differential expression analysis were performed as previously described [ 47 ].…”
Section: Methodsmentioning
confidence: 99%
“…The code for colocalization analysis is given at https://www.github.com/mikegloudemans/insulin-resistance-colocalization [ 74 ]. The RNA-seq data for perturbation experiments have been uploaded to GEO with accession number GSE179347 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179347 ) [ 47 ]. Colocalization heatmaps depicting all genes tested at every locus in the style of Fig.…”
Background
Identification of causal genes for polygenic human diseases has been extremely challenging, and our understanding of how physiological and pharmacological stimuli modulate genetic risk at disease-associated loci is limited. Specifically, insulin resistance (IR), a common feature of cardiometabolic disease, including type 2 diabetes, obesity, and dyslipidemia, lacks well-powered genome-wide association studies (GWAS), and therefore, few associated loci and causal genes have been identified.
Methods
Here, we perform and integrate linkage disequilibrium (LD)-adjusted colocalization analyses across nine cardiometabolic traits (fasting insulin, fasting glucose, insulin sensitivity, insulin sensitivity index, type 2 diabetes, triglycerides, high-density lipoprotein, body mass index, and waist-hip ratio) combined with expression and splicing quantitative trait loci (eQTLs and sQTLs) from five metabolically relevant human tissues (subcutaneous and visceral adipose, skeletal muscle, liver, and pancreas). To elucidate the upstream regulators and functional mechanisms for these genes, we integrate their transcriptional responses to 21 relevant physiological and pharmacological perturbations in human adipocytes, hepatocytes, and skeletal muscle cells and map their protein-protein interactions.
Results
We identify 470 colocalized loci and prioritize 207 loci with a single colocalized gene. Patterns of shared colocalizations across traits and tissues highlight different potential roles for colocalized genes in cardiometabolic disease and distinguish several genes involved in pancreatic β-cell function from others with a more direct role in skeletal muscle, liver, and adipose tissues. At the loci with a single colocalized gene, 42 of these genes were regulated by insulin and 35 by glucose in perturbation experiments, including 17 regulated by both. Other metabolic perturbations regulated the expression of 30 more genes not regulated by glucose or insulin, pointing to other potential upstream regulators of candidate causal genes.
Conclusions
Our use of transcriptional responses under metabolic perturbations to contextualize genetic associations from our custom colocalization approach provides a list of likely causal genes and their upstream regulators in the context of IR-associated cardiometabolic risk.
“…4 M and N). IL6 treatment in cultured human adipocytes, a mimetic model of inflammatory human adipocytes in vitro [74] , significantly increased the gene expression of IRF1 ( Fig. 4 O), suggesting direct involvement in the regulation of the IRF1 gene.…”
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