Peter Orchard

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 develops a model to predict NMD outcomes for protein-truncating variants using RNA-seq data, achieving ~78% ROC-AUC and identifying non-canonical determinants.

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 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 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 defines a 3D chromatin compartment blueprint that programs histone-to-protamine exchange during spermiogenesis using ATAC-seq, CUT&Tag, and Hi-C.

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

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

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 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 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 develops genome-wide enhancer-to-target gene assignments across >500 cell types and shows they outperform naive methods in gene set enrichment analysis.

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

The paper investigates epigenomic and transcriptomic changes in Danforth’s short tail mice, finding minimal chromatin changes but coordinated dysregulation of hedgehog signaling.

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.