Surya B. Chhetri

APEX infers cancer gene expression from plasma cfChIP-seq data, outperforming existing methods and enabling subtype classification and biomarker identification.

The paper maps cis- and trans-eQTLs in blood samples to infer gene regulatory networks and prioritize trait-relevant pathways.

SNAP improves classification performance for cell-free chromatin data while maintaining data retention through optimized quality filters and modular design.

The paper identifies key transcription factors and their target proteins in renal cell carcinoma using multi-omic integration of ChIP-seq and GWAS data.

The paper identifies cell-free chromatin QTLs (cfcQTLs) from cancer patients' blood to uncover regulatory effects of genetic variants across diverse tissues and diseases.

The paper infers enhancer activity from cfDNA fragmentation patterns and proposes a chromatin fiber model to explain these signatures.

The paper identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

This paper introduces GLEANR, a method for sparse matrix factorization of GWAS summary statistics that accounts for sample sharing and identifies interpretable genetic factors.

This study identifies pleiotropic variants and trait-specific susceptibility variants using GWAS data from 116 traits, revealing widespread pleiotropy and unique genetic contributions to specific traits.

The paper analyzes gene expression and splicing variation across diverse human populations, identifying population-specific and shared genetic regulatory elements.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

The paper introduces the EN-TEx resource, which provides personal epigenomes and variant-impact models across multiple tissues, revealing allele-specific loci and their associations with GWAS loci.

The paper uses matrix factorization to analyze eQTLs across 49 human tissues, identifying tissue-specific and shared genetic regulation patterns and their potential transcription factor drivers.

The paper presents ChIP-seq data for 208 DNA-associated proteins in HepG2 cells, revealing occupancy patterns and DNA motifs, and distinguishing promoters from enhancers.