William W. Young Greenwald

The paper identifies multi-omic QTLs in early developmental tissues and shows that GWAS loci often involve complex regulatory variants not captured by eQTLs alone.

The paper identifies regulatory network modules in human stem cells, showing epigenetic and genetic factors influence pluripotency state differences.

The paper maps eQTLs in fetal-like pancreatic progenitor cells, identifying regulatory variants that influence diabetes risk by affecting gene expression and splicing in early development.

This study identifies causal genetic variants for cardiac traits by integrating spatiotemporal eQTL analysis with GWAS data, showing stage- and tissue-specific effects.

The paper examines how structural variants and short tandem repeats affect gene expression and complex traits, identifying high-impact variants associated with multiple genes and GWAS traits.

The paper discovers and genotypes a comprehensive set of structural variants and short tandem repeats using multiple algorithms and replicates, creating a well-characterized map of these variants.

The paper investigates how non-genetic variability in iPSC lines affects their response to WNT inhibition and cardiac differentiation, identifying 91 signature genes and X chromosome dosage differences as key factors.

The paper identifies allele-specific NKX2-5 binding variants associated with EKG traits by integrating iPSC-CM data and GWAS, showing their role in heart rate loci and cardiac enhancer activity.

The paper uses iPSC-derived retinal pigment epithelium to identify and functionally characterize causal variants at AMD risk loci, finding rs943080 as the probable causal variant at VEGFA.

The paper identifies distal enhancer networks in pancreatic islets associated with type 2 diabetes risk by integrating chromatin accessibility, conformation, and genetic data.

The paper analyzes co-accessibility across chromosomes using ATAC-seq and identifies its regulatory effects on gene expression and disease-relevant genes.

The paper investigates how genetic and epigenetic factors influence chromatin looping strength across cell types and haplotypes, finding that loop strength changes correlate with significant gene expression differences.

This study analyzes the mutational burden in human iPSCs using whole genome sequencing and integrates transcriptome and epigenome data to identify mutation patterns and their impact on gene expression.