Genetic analysis of type II diabetes in Finnish population

National Human Genome Research Institute

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Abstract

FUSION has enrolled > 30,000 Finnish individuals with and without diabetes. We have contributed to the identification of > 240 loci for T2D and >1000 quantitative trait loci (QTL) with effects on obesity, fasting glucose, LDL and HDL cholesterol, triglycerides, proinsulin levels, blood pressure, and adult height. We have also performed whole-exome and whole-genome sequencing of > 2657 diabetics and controls, to look for rare variants of large effect that contribute to disease risk. Since >90% of the identified T2D risk variants are in non-coding regions, we have focused on the epigenome of human pancreatic islets and other diabetes-relevant tissues. We have shown that stretch enhancers (regions of regulatory enhancers greater than 3kb in length) correlate with gene expression in a tissue-specific manner and are enriched in disease-associated GWAS variants. To further define the human islet epigenome, we integrated whole genome transcriptomic data (RNA-seq) from 185 cadaveric islets and chromatin accessibility profiles (ATAC-seq) from two islet samples and show that T2D associated genetic variants are enriched in islet-specific regulatory regions. We also contributed our pancreatic islet reference chromatin analyses and gene expression data to the international InsPIRE consortium. This integrated meta-analysis of 420 pancreatic islet samples led to the identification of candidate effector transcripts at 23 T2D loci. The human pancreatic islet is composed of several cell types, with the insulin secreting beta cells representing only 40% of the total cell population. To assess cell type specific gene expression under basal or environmental stimuli (i.e. high/low glucose), we are performing single-cell RNA-seq (scRNA-seq) of islets from individual cadaveric donors. Furthermore, we are utilizing spatial transcriptomics to determine the location of cell types within the structure of the pancreatic islet and the impact of cellular architecture on gene expression profiles. We have collected skin, muscle, and adipose biopsies from > 300 well phenotyped and genotyped individuals with normal glucose tolerance, impaired glucose tolerance, or T2D. The skin tissue has been used to generate induced pluripotent stem cell lines (iPSC), which in turn are being differentiated into islet beta cells. In collaboration with the New York Stem Cell Foundation, we have generated 52 IPSC lines. We are currently differentiating 12 of the 52 iPSC lines to mature beta cells and performing transcriptomic analysis (bulk and scRNA-seq) and open chromatin structure (bulk and scATAC-seq) analyses at critical stages of the differentiation process. This will allow comparative analysis of the effects of genetic background on beta cell development and function. These human tissue biopsies have also yielded important insights. Analyses of the muscle RNA-seq data set have identified many expression quantitative trait loci (eQTLs), including some that link T2D-GWAS variants to their target genes. Current analyses of muscle and adipose RNA-seq data from the same individuals have identified eQTLs from both tissues colocalizing with T2D-GWAS signals. We have collected global metabolomics data on 318 muscle and 309 adipose biopsy samples as well as global metabolomics plus complex lipid analysis of more than 1390 plasma samples taken during an oral glucose tolerance. For the majority of these same samples, we also have RNA-seq data, and are integrating metabolomics data with genetic architecture, T2D-related traits and eQTLs to investigate potential dynamic metabolic interactions to identify and clarify disease mechanisms. To date, GWAS with the plasma metabolite data has identified >2000 genetic associations with T2D and/or T2D-related traits. We have performed detailed analysis of DNA methylation in a subset of these human tissue samples. We have integrated genomic sequence (>7 million genetic variants), gene expression and methylation data from 265 skeletal muscle biopsies with their corresponding phenotypes for eight physiological traits (height, waist, weight, waist hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and T2D). We utilized a novel approach, Mendelian randomization, to ascertain whether DNA methylation drives variation in gene expression, or the other way around. We identified gene and DNA methylation site relationships that may underlie 534 disease/quantitative traits. We have also collected liver samples, another diabetes-relevant tissue. Similar to pancreatic islet cell analyses, we integrated RNA-seq and ATAC-seq data from 20 liver samples to identify critical regulators of genes relevant to diabetes risk. We identified over 3000 QTLs within regions of open chromatin (caQTL) with an enrichment of QTL variants in liver promoter and enhancer states. Using a combination of various genomic data sets. we were also able to predict target genes for 861 of the caQTL signals. A major obstacle to studying T2D pathogenesis is defining the functional consequences of > 240 T2D GWAS loci and >1000 QTLs, many of which are in non-coding regions. One approach we have taken involves a novel machine learning strategy that models pancreatic islets enhancers to increase the accuracy of the predicted impact of islet regulatory variants. We have rigorously tested this approach using existing datasets and have performed assays, which have validated the accuracy of the machine learning predictions. A second approach involves the analysis of microRNA (miRNA) classes and abundance. miRNA expression is important for pancreatic development and is altered by disease states, such as diabetes. We currently have limited knowledge of the miRNA landscape in pancreatic islets. Thus, we have characterized miRNA expression in pancreatic islets from 74 donors, and identified several miRNAs associated with T2D or T2D-related traits. We are also exploring the role of T2D genetic risk variants in isogenic lines. In collaboration with Dr Shuibing Chen (Weill Cornell Medicine) we are using CRISPR prime editing to generate iPSC lines harboring T2D genetic risk or non-risk variants. The edited isogenic lines will then be differentiated into beta cell lines for functional in vitro analyses including glucose stimulating insulin secretion assays and potential high throughput drug screens under various exposures/treatments. To date, we have focused on optimizing the efficiency of CRISPR prime editing in IPSCs and have completed editing for one gene and in the process of editing two additional genes. In an extension of our investigation of the genetic influence on pancreatic islet function, we are collaborating with Drs. Shuibing Chen and Stephen Parker to investigate the intrinsic and environmental network signature dynamics of pancreatic beta cell function common in type 1 and type 2 diabetes. Both intrinsic (beta cell in T2D) and environmental (immune cell in T1D) signals play critical roles in pancreatic dysfunction and cell death. Over the next four years we aim to contribute 100 cadaveric pancreatic islet samples to the comparative analysis of islet function under basal conditions for T2D model as well as inflammatory cytokine and viral perturbation for T1D model and generate single-cell resolution multi-omic (scRNA-seq, scATAC-seq) reference maps of cell/context-specific molecular genetic (e/caQTL) network and hub signatures of intrinsic and environmental signals in human pancreatic islets. Our goal is to identity genetic and epigenetic changes in multiple tissues relevant to T2D and to determine their correlation with diabetes and diabetes-related phenotypes. This will provide insight into diabetes risk and possible novel avenues for prevention and treatment.