Stephen C.J. Parker

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 investigates how nucleosome positioning, DNA structure, and transcription factor binding contribute to the function of ultra-conserved developmental enhancers.

The paper analyzes expression and splicing quantitative trait loci (e/sQTL) across multiple tissues in the TOPMed dataset and finds that secondary e/sQTL signals are still discoverable, with many GWAS signals colocalizing with these e/sQTL.

The paper integrates multiple scRNA-seq datasets to create a comprehensive atlas of human pancreatic islets, identifying 13 cell populations and providing a resource for diabetes research.

The paper constructs a healthy human pancreas reference atlas to contextualize disease states and benchmark models.

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.

CelLink integrates single-cell multi-omics data by addressing weak feature linkage and imbalanced cell populations, outperforming existing methods in imputation accuracy and cell subtype annotation.

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 identifies enhancer architecture differences in skeletal muscle between high and low cardiorespiratory fitness rats, linking them to gene expression and metabolic traits.

This study compares the acute effects of three exercise intensities on subcutaneous adipose tissue transcriptome in regular exercisers, finding intensity-specific gene expression changes and common pathways involved in cardiometabolic health.

The paper identifies genetic and phenotypic correlates of mosaic loss of chromosome Y in blood using whole genome sequencing data from the TOPMed program.

This study identifies context-specific regulatory effects of metabolic trait-associated variants in human skeletal muscle cells under different conditions.

The paper develops a knowledge graph framework to extract biomedical knowledge from PubMed, creating a database that enhances transparency in biomedical AI applications.

The paper develops vascularized macrophage-islet organoids to study immune-mediated β cell pyroptosis during viral infection and identifies TNFSF12-TNFRSF12A as a key mechanism.

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

The paper identifies additional eQTL signals in adipose tissue and shows they contribute to cardiometabolic traits through gene dosage effects.

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 introduces FixItFelix and a modified GRCh38 reference to improve variant calling and expression analysis in genes with reference errors, showing benefits across multiple sequencing types and populations.

The paper examines the association between adipose tissue ACE2 expression and cardio-metabolic risk factors, finding lower ACE2 levels linked to T2D, obesity, and inflammation-related cell type changes.

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 identifies combinatorial transcription factor expression in human islet cells and shows it correlates with functional maturity and electrophysiological activity.