Arushi Varshney

The paper maps cell type-specific genetic effects on nucleosome-free regions and nucleosome occupancy using snATAC-seq data from human muscle biopsies, showing causal relationships between these elements and GWAS traits.

The paper uses single-nucleus multi-omics to study skeletal muscle changes in HFpEF patients and shows some molecular changes are reversible with lifestyle interventions.

The paper identifies age-associated changes in the skeletal muscle epigenome and transcriptome, revealing shifts in chromatin accessibility and gene expression linked to metabolic and senescence pathways.

The paper identifies dysregulated pathways and candidate driver genes in skeletal muscle from individuals with type 2 diabetes using co-expression networks and single-nucleus RNA-seq data.

The paper uses donor-matched iPSC-derived FAPs to show that T2D GWAS signals are enriched in adipogenic FAP subtypes under high-insulin exposure.

This study demonstrates nanopore sequencing of 10x Genomics multiome libraries to capture full-length transcripts and identify TSS, showing concordance with short-read methods despite higher error rates.

The paper uses MPRAs to assess context-dependent regulatory activity in type 2 diabetes-linked noncoding regions across different promoters and cell types.

This paper introduces a method to deliver synthetic haplotypes containing T2D GWAS variants to hiPSCs to study their functional impact on differentiation.

This paper combines multi-trait fine-mapping and functional annotation to improve the identification of causal variants at glycaemic trait loci, reducing the number of likely causal variants significantly.

The paper examines how physical activity and insulin resistance relate to skeletal muscle cell type abundance and gene expression in humans, finding inverse associations with specific cell types and gene expression patterns.

The paper uses MPRA to assess context-dependent activity of transcription start sites, finding that promoter, position, and strand effects influence regulatory element function.

The paper performs population-scale single-nucleus multi-omic profiling of skeletal muscle to identify genetic regulation across cell types and traits.

This study integrates single-cell gene expression and chromatin accessibility data from human pancreatic islets to identify T1D risk mechanisms involving multiple cell types and genetic variants.

The paper performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

The paper integrates snATAC-seq and snRNA-seq data from human and rat skeletal muscle to identify cell types, regulatory elements, and SNPs associated with complex traits.

The paper maps transcription start sites in human pancreatic islets and identifies functional regulatory elements linked to type 2 diabetes GWAS signals.

The paper uses information theory to analyze chromatin architecture and identifies TF motifs with high chromatin information enrichment, linking them to functional biological variation.

The paper identifies rare non-coding variants from whole genome sequencing in the TOPMed program, showing they contribute significantly to type 2 diabetes heritability.

The paper investigates how genetic variants affect gene expression in human pancreatic islets and identifies candidate genes linked to type 2 diabetes.

This study identifies 242 genetic loci for glycaemic traits using trans-ancestry meta-analysis, finding 99 novel loci and improved fine-mapping resolution compared to single-ancestry analyses.

The paper provides multi-genomic maps of the EndoC-βH1 cell model to define gene regulatory programs in human pancreatic β cells and identifies allelic effects on cis-regulatory elements.

BoostMe improves DNA methylation imputation in WGBS data, enhancing concordance with the MethylationEPIC array and identifying tissue-specific regulatory features.

The paper identifies genetic-environment interactions in skeletal muscle gene expression using allele-specific expression and clinical traits as environmental proxies.