Heather M. Stringham

Integrated Analysis of Skeletal Muscle Transcriptional Networks Characterizes Dysregulation in Pathways and Trait-Associated Regulatory Regions in Type 2 Diabetes

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.

Inverse directions of association of higher physical activity and higher insulin resistance with human skeletal muscle cell type abundance and fiber type-level gene expression

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.

Large-Scale Gene-Smoking Interactions and Fine Mapping Study Identifies Multiple Novel Blood Pressure Loci in over 1 Million Individuals

This study identifies twelve novel blood pressure loci and shows smoking modifies genetic effects on BP through immune-related pathways in over 1 million individuals.

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

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

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

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

ACE2 expression in adipose tissue is associated with COVID-19 cardio-metabolic risk factors and cell type composition

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.

Genome-wide association study of 1,391 plasma metabolites in 6,136 Finnish men identifies 303 novel signals and provides biological insights into human diseases

This study identifies 303 novel genetic signals associated with plasma metabolites in Finnish men, revealing biological insights into disease mechanisms.

Random glucose GWAS in 493,036 individuals provides insights into diabetes pathophysiology, complications and treatment stratification

This study performs a GWAS on random glucose measurements in 493,036 individuals, identifying 128 loci associated with glycaemic traits and providing insights into diabetes biology and treatment.

Tissue-Specific Alteration of Metabolic Pathways Influences Glycemic Regulation

The paper identifies genetic variants and transcripts associated with glycemic traits by analyzing exome-array data from 144,600 individuals, highlighting tissue-specific roles in glucose regulation.

Exome sequencing identifies high impact trait-associated alleles enriched in Finns

Exome sequencing in Finnish populations identifies rare trait-associated alleles with higher frequencies and geographical clustering, revealing 30 novel associations with quantitative traits.

Exome chip meta-analysis elucidates the genetic architecture of rare coding variants in smoking and drinking behavior

The paper analyzes rare coding variants in smoking and drinking behavior using exome chip data, finding that they contribute to phenotypic variation but require larger samples for individual variant associations.

Genetic discovery and translational decision support from exome sequencing of 20,791 type 2 diabetes cases and 24,440 controls from five ancestries

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.

Subset-Based Analysis using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes

This paper introduces a method to enhance genetic association discovery by incorporating gene-environment interactions across multiple studies or phenotypes.