Craig P. Hersh

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 identifies smoking-associated miRNA regulatory networks in blood and tissue, revealing NDUFA12 as a key target linked to mitochondrial dysfunction in smokers.

This study identifies 38 COPD GWAS loci with evidence of sQTLs, showing that sQTL analysis in whole blood and lung tissue provides novel insights into COPD mechanisms.

This paper introduces SGTCCA-Net, a framework for multi-omics network inference that addresses limitations of existing methods by incorporating higher-order correlations and improving computational efficiency.

This study identifies LIFR as a master amplifier of fibroblast activation in IPF, showing that blocking LIFR reduces fibrosis in ex vivo tissue.

The paper identifies proteomic networks and genetic variants associated with smoking and COPD phenotypes using multi-ethnic proteomic data and network-based GWAS.

This study shows that common splice-regulatory variants can reduce the pathogenicity of rare exonic variants by modulating mRNA splicing.

The paper finds that somatic mutations in diseased lung tissue correlate with reduced lung function and cell type proportions in COPD and IPF.

This study identifies eight novel genetic signals associated with asthma-COPD overlap through a genome-wide association analysis involving multiple cohorts.

The paper identifies a genetic variant in NPNT associated with COPD risk and lung function through splicing analysis using long-read sequencing.

This study identifies peripheral blood microbial signatures associated with COPD and exacerbation phenotypes in smokers.

The paper uses SWIM software to analyze COPD transcriptomic networks, identifying key modules and switch genes associated with immune response, inflammation, and hypoxia.

The paper identifies FBXO38 as a novel COPD susceptibility gene by analyzing genetically driven alternative splicing and finding a protective exon associated with COPD risk.

The paper proposes eight RNA-seq data transformations to improve analysis accuracy, showing that limma+rv and limma+l2 perform best in simulations and real data.

This paper introduces MONSTER, a method to identify transcription factor drivers of cell state transitions using gene regulatory network models, applied to COPD studies to find persistent regulatory signals.

The paper identifies ACVR1B as a candidate causal gene for emphysema distribution by integrating GWAS, eQTL, and epigenomic data.