Heikki A. Koistinen

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 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.

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 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.

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.

This study identifies 35 genes associated with circulating lipid levels using rare coding variants from 170,000 exomes across multiple ancestries.

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

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.

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

This study identifies 16 causal coding variants in type 2 diabetes using fine-mapping and shows that one third of signals are false leads.