Publications by authors named "Arushi Varshney"

12 Publications

  • Page 1 of 1

The trans-ancestral genomic architecture of glycemic traits.

Nat Genet 2021 06 31;53(6):840-860. Epub 2021 May 31.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-021-00852-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610958PMC
June 2021

A Transcription Start Site Map in Human Pancreatic Islets Reveals Functional Regulatory Signatures.

Diabetes 2021 Jul 13;70(7):1581-1591. Epub 2021 Apr 13.

Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI

Identifying the tissue-specific molecular signatures of active regulatory elements is critical to understand gene regulatory mechanisms. Here, we identify transcription start sites (TSS) using cap analysis of gene expression (CAGE) across 57 human pancreatic islet samples. We identify 9,954 reproducible CAGE tag clusters (TCs), ∼20% of which are islet specific and occur mostly distal to known gene TSS. We integrated islet CAGE data with histone modification and chromatin accessibility profiles to identify epigenomic signatures of transcription initiation. Using a massively parallel reporter assay, we validated the transcriptional enhancer activity for 2,279 of 3,378 (∼68%) tested islet CAGE elements (5% false discovery rate). TCs within accessible enhancers show higher enrichment to overlap type 2 diabetes genome-wide association study (GWAS) signals than existing islet annotations, which emphasizes the utility of mapping CAGE profiles in disease-relevant tissue. This work provides a high-resolution map of transcriptional initiation in human pancreatic islets with utility for dissecting active enhancers at GWAS loci.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2337/db20-1087DOI Listing
July 2021

Chromatin information content landscapes inform transcription factor and DNA interactions.

Nat Commun 2021 02 26;12(1):1307. Epub 2021 Feb 26.

Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, USA.

Interactions between transcription factors and chromatin are fundamental to genome organization and regulation and, ultimately, cell state. Here, we use information theory to measure signatures of organized chromatin resulting from transcription factor-chromatin interactions encoded in the patterns of the accessible genome, which we term chromatin information enrichment (CIE). We calculate CIE for hundreds of transcription factor motifs across human samples and identify two classes: low and high CIE. The 10-20% of common and tissue-specific high CIE transcription factor motifs, associate with higher protein-DNA residence time, including different binding site subclasses of the same transcription factor, increased nucleosome phasing, specific protein domains, and the genetic control of both chromatin accessibility and gene expression. These results show that variations in the information encoded in chromatin architecture reflect functional biological variation, with implications for cell state dynamics and memory.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-21534-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910283PMC
February 2021

Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D.

Nat Commun 2020 09 30;11(1):4912. Epub 2020 Sep 30.

Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-18581-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528108PMC
September 2020

Multiomic Profiling Identifies cis-Regulatory Networks Underlying Human Pancreatic β Cell Identity and Function.

Cell Rep 2019 01;26(3):788-801.e6

The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA. Electronic address:

EndoC-βH1 is emerging as a critical human β cell model to study the genetic and environmental etiologies of β cell (dys)function and diabetes. Comprehensive knowledge of its molecular landscape is lacking, yet required, for effective use of this model. Here, we report chromosomal (spectral karyotyping), genetic (genotyping), epigenomic (ChIP-seq and ATAC-seq), chromatin interaction (Hi-C and Pol2 ChIA-PET), and transcriptomic (RNA-seq and miRNA-seq) maps of EndoC-βH1. Analyses of these maps define known (e.g., PDX1 and ISL1) and putative (e.g., PCSK1 and mir-375) β cell-specific transcriptional cis-regulatory networks and identify allelic effects on cis-regulatory element use. Importantly, comparison with maps generated in primary human islets and/or β cells indicates preservation of chromatin looping but also highlights chromosomal aberrations and fetal genomic signatures in EndoC-βH1. Together, these maps, and a web application we created for their exploration, provide important tools for the design of experiments to probe and manipulate the genetic programs governing β cell identity and (dys)function in diabetes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.celrep.2018.12.083DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389269PMC
January 2019

Cell Specificity of Human Regulatory Annotations and Their Genetic Effects on Gene Expression.

Genetics 2019 02 28;211(2):549-562. Epub 2018 Dec 28.

Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109

Epigenomic signatures from histone marks and transcription factor (TF)-binding sites have been used to annotate putative gene regulatory regions. However, a direct comparison of these diverse annotations is missing, and it is unclear how genetic variation within these annotations affects gene expression. Here, we compare five widely used annotations of active regulatory elements that represent high densities of one or more relevant epigenomic marks-"super" and "typical" (nonsuper) enhancers, stretch enhancers, high-occupancy target (HOT) regions, and broad domains-across the four matched human cell types for which they are available. We observe that stretch and super enhancers cover cell type-specific enhancer "chromatin states," whereas HOT regions and broad domains comprise more ubiquitous promoter states. Expression quantitative trait loci (eQTL) in stretch enhancers have significantly smaller effect sizes compared to those in HOT regions. Strikingly, chromatin accessibility QTL in stretch enhancers have significantly larger effect sizes compared to those in HOT regions. These observations suggest that stretch enhancers could harbor genetically primed chromatin to enable changes in TF binding, possibly to drive cell type-specific responses to environmental stimuli. Our results suggest that current eQTL studies are relatively underpowered or could lack the appropriate environmental context to detect genetic effects in the most cell type-specific "regulatory annotations," which likely contributes to infrequent colocalization of eQTL with genome-wide association study signals.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1534/genetics.118.301525DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366912PMC
February 2019

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues.

BMC Genomics 2018 May 23;19(1):390. Epub 2018 May 23.

National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.

Background: Bisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power.

Results: Here we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation.

Conclusions: Our findings support the use of BoostMe as a preprocessing step for WGBS analysis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12864-018-4766-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5966887PMC
May 2018

Interactions between genetic variation and cellular environment in skeletal muscle gene expression.

PLoS One 2018 16;13(4):e0195788. Epub 2018 Apr 16.

National Human Genome Research Institute, National Institutes of Health, Bethesda, United States of America.

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis-acting genotype-environment interactions (GxE)-genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate environmental response expression quantitative trait loci (reQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195788PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5901994PMC
July 2018

Strategies to develop endogenous stem cell-recruiting bioactive materials for tissue repair and regeneration.

Adv Drug Deliv Rev 2017 10 19;120:50-70. Epub 2017 Jul 19.

Department of Chemical and Petroleum Engineering, Bioengineering Graduate Program, University of Kansas, Lawrence, KS, USA. Electronic address:

A leading strategy in tissue engineering is the design of biomimetic scaffolds that stimulate the body's repair mechanisms through the recruitment of endogenous stem cells to sites of injury. Approaches that employ the use of chemoattractant gradients to guide tissue regeneration without external cell sources are favored over traditional cell-based therapies that have limited potential for clinical translation. Following this concept, bioactive scaffolds can be engineered to provide a temporally and spatially controlled release of biological cues, with the possibility to mimic the complex signaling patterns of endogenous tissue regeneration. Another effective way to regulate stem cell activity is to leverage the inherent chemotactic properties of extracellular matrix (ECM)-based materials to build versatile cell-instructive platforms. This review introduces the concept of endogenous stem cell recruitment, and provides a comprehensive overview of the strategies available to achieve effective cardiovascular and bone tissue regeneration.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.addr.2017.07.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5705585PMC
October 2017

A Type 2 Diabetes-Associated Functional Regulatory Variant in a Pancreatic Islet Enhancer at the Locus.

Diabetes 2017 09 6;66(9):2521-2530. Epub 2017 Jul 6.

Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC

Molecular mechanisms remain unknown for most type 2 diabetes genome-wide association study identified loci. Variants associated with type 2 diabetes and fasting glucose levels reside in introns of , a gene that encodes adenylate cyclase 5. Adenylate cyclase 5 catalyzes the production of cyclic AMP, which is a second messenger molecule involved in cell signaling and pancreatic β-cell insulin secretion. We demonstrated that type 2 diabetes risk alleles are associated with decreased expression in human islets and examined candidate variants for regulatory function. rs11708067 overlaps a predicted enhancer region in pancreatic islets. The type 2 diabetes risk rs11708067-A allele showed fewer H3K27ac ChIP-seq reads in human islets, lower transcriptional activity in reporter assays in rodent β-cells (rat 832/13 and mouse MIN6), and increased nuclear protein binding compared with the rs11708067-G allele. Homozygous deletion of the orthologous enhancer region in 832/13 cells resulted in a 64% reduction in expression level of , but not adjacent gene , and a 39% reduction in insulin secretion. Together, these data suggest that rs11708067-A risk allele contributes to type 2 diabetes by disrupting an islet enhancer, which results in reduced expression and impaired insulin secretion.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2337/db17-0464DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860374PMC
September 2017

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes.

Proc Natl Acad Sci U S A 2017 02 13;114(9):2301-2306. Epub 2017 Feb 13.

The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032.

Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense -expression quantitative trait loci (-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that -eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet -eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.1621192114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5338551PMC
February 2017

Super Enhancers in Cancers, Complex Disease, and Developmental Disorders.

Genes (Basel) 2015 Nov 9;6(4):1183-200. Epub 2015 Nov 9.

Medical Scientist Training Program, Medical School, University of Michigan, Ann Arbor, MI 48109, USA.

Recently, unique areas of transcriptional regulation termed super-enhancers have been identified and implicated in human disease. Defined by their magnitude of size, transcription factor density, and binding of transcriptional machinery, super-enhancers have been associated with genes driving cell differentiation. While their functions are not completely understood, it is clear that these regions driving high-level transcription are susceptible to perturbation, and trait-associated single nucleotide polymorphisms (SNPs) occur within super-enhancers of disease-relevant cell types. Here we review evidence for super-enhancer involvement in cancers, complex diseases, and developmental disorders and discuss interactions between super-enhancers and cofactors/chromatin regulators.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/genes6041183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4690034PMC
November 2015
-->