Genetic analysis of type II diabetes in Finnish population

National Human Genome Research Institute

Investigators

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Abstract

We have made significant contributions to identifying the mechanism by which non-coding variants in the genome confer increased risk for T2D. We have shown that regulatory enhancers >3kb in length correlate with gene expression in a tissue-specific manner and are enriched with disease-associated GWAS variants. Additionally, we have integrated whole genome transcriptomic data (RNA-seq) from 185 cadaveric islets and chromatin accessibility profiles (ATAC-seq) from 11 islet samples and show that T2D associated variants are enriched in islet-specific regulatory regions. Our group has also contributed pancreatic islet data to the international InsPIRE consortium and the resulting meta-analysis (420 islets) led to the identification of candidate effector transcripts at 23 T2D loci. We have collected and performed RNA sequencing of skin, muscle, and adipose biopsies from >300 well phenotyped and genotyped individuals with normal glucose tolerance, impaired glucose tolerance, or T2D. We have identified many expression quantitative trait loci (eQTLs), and in some cases muscle and adipose RNA-seq data from the same individuals have identified eQTLs from both tissues colocalizing with T2D-GWAS signals. We have collected metabolomics data on 318 muscle and 309 adipose samples as well as complex lipid analysis of >1390 plasma samples taken during an oral glucose tolerance test and have identified >2000 genetic associations with T2D and/or T2D-related traits. Additionally, we have identified genetic association for 1400 plasma metabolites sampled from another cohort of >6000 Finnish men (METSIM)1. Over 300 of these are novel associations potentially identifying new genes contributing to risk for diabetes or related traits. We have collected 20 liver samples and have integrated RNA-seq and ATAC-seq data to identify critical regulators of genes relevant to diabetes risk. We identified >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 predicted target genes for 861 of the caQTL signals. We have also performed DNA methylation analysis on 265 skeletal muscle samples and have integrated this data with genomic sequence, gene expression data and phenotypes for eight physiological traits, including T2D. We identified gene and DNA methylation site relationships that may underlie 534 disease/quantitative traits. To assess cell type specific gene expression during high/low glucose stimulation over a 24hr period, we performed single-cell RNA-seq (scRNA-seq) of islets from cadaveric donors. We modeled time to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. We nominate 363 candidate effector genes that may underlie genetic associations for T2D and related traits and a manuscript describing these results is under review. Defining the functional consequences of >240 T2D GWAS loci and >1000 QTLs is challenging. One approach is to use machine learning to model pancreatic islets enhancers to increase the accuracy of the predicted impact of islet regulatory variants. We have rigorously tested and validated the accuracy of our predictions using existing datasets and laboratory assays and have recently published a paper in PNAS describing our findings. Another approach involves the analysis of microRNA (miRNA) classes and abundance. miRNA expression is important for pancreatic development and is altered by disease states. Thus, we have characterized miRNA expression in islets from 74 donors, and identified several miRNAs associated with T2D or T2D-related traits. These findings have also recently been published in PNAS. In a collaboration with Dr Shuibing Chen (Weill Cornell Medicine), we generated isogenic human embryonic stem cell lines, each with a knockout in one of twenty genes linked to T2D risk. We conducted RNA-seq and ATAC-seq analyses of beta cells derived from these lines to investigate the effects of each KO gene on beta-cell differentiation, insulin production and secretion, and cell survival. Through integrated analyses, we identified four genes (CP, RNASE1, PCSK1N, and GSTA2) associated with insulin production and two genes (TAGLN3 and DHRS2) with lipotoxicity. We also assessed allele-specific imbalance at heterozygous T2D GWAS variants sites and identified a single likely functional variant for each of 23 GWAS signals. A manuscript describing this research is currently under review. In collaboration with the New York Stem Cell Foundation, we are pursuing a bold project to identify genetic factors associated with differentiation of induced pluripotent stem cells (iPSCs) to insulin secreting beta cells. This automated platform is being used for multiple purposes. One is to assess differences in beta cell development in 43 HipSci iPSC lines, each of which harbors a gene mutation known to cause a form of monogenic diabetes. To understand genetic effects that drive beta cell differentiation, we are also undertaking a GWAS study with several hundred iPSC lines, including those previously generated by other groups (i.e., GENESIS and HipSci). IPSC lines from many donors will be pooled together and differentiated. The resulted fraction of mature beta-cells from each donor will be quantified. The genetic makeup of the donor lines producing the highest fraction of mature beta-cells can thus be associated with the highest differentiation potential. To date, we have almost completed pilot studies to validate the experimental design of this study and anticipate launching the primary study soon. To further understand the specific genes required for beta cell differentiation, and complement the GWAS studies, we are also preparing to perform a genome-wide CRISPR/Cas9 interference (CRISPRi) screen. Single genes (targeted by a guide RNA) will be inhibited during the differentiation process. Cells not expressing critical genes required for beta-cell differentiation and maturation will be depleted in beta-cells compared to all other cell types. In a more targeted approach to identify variants critical to beta-cell differentiation or function, we are generating isogenic lines that differ genetically at only one T2D risk locus, using CRISPR prime editing to generate iPSC lines harboring T2D risk or non-risk variants. The edited lines will be differentiated into beta cells for functional in vitro analyses including glucose stimulating insulin secretion assays and high throughput drug screens under various exposures/treatments. To date, we have successfully edited the sequence at six risk variants. In a collaboration with Drs. Shuibing Chen and Stephen Parker, we are investigating dynamic regulatory mechanisms of pancreatic beta cell function in T1D and T2D. We aim to profile 100 cadaveric pancreatic islet samples under basal conditions and after inflammatory cytokine and viral perturbation. To date, we have performed perturbation experiments on islets from 33 unique donors. We will generate single-cell resolution multi-omic (scRNA-seq, scATAC-seq) maps of cell- and context-specific molecular quantitative trait loci (molQTLs). Integrating this data with diabetes genetics will allow us to nominate causal mechanisms at diabetes risk loci for functional validation with gene editing in iPSC-derived beta cells. These analyses will reveal molecular mechanisms of beta cell response to inflammatory stress underlying diabetes risk. 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.