Brian D. Hobbs

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 paper introduces a deep learning framework, REGLE, that improves genomic discovery and risk prediction for respiratory and circulatory diseases by learning low-dimensional embeddings from high-dimensional clinical data.

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 performs a multi-ancestry GWAS on lung function and COPD, identifying 559 genes and 29 pathways, and showing genetic risk scores are associated with COPD across ancestries.

RITSS identifies gene-environment interactions in quantitative traits using sample splitting and robust statistics, finding significant interactions in lung function and height data.

This paper trains a deep neural network on noisy COPD labels to predict COPD status from spirograms, improving genetic discovery by identifying new loci and replicating existing ones.

This study shows that proteomic data can be linked to specific genomes using pQTL knowledge, highlighting privacy risks and the need for protections similar to genomic data.

This study investigates whether smoking modifies the effect of a polygenic risk score on reduced lung function, finding significant gene-by-smoking interactions in FEV1/FVC.

This study identifies three new genetic loci associated with idiopathic pulmonary fibrosis and confirms 11 previously reported ones, highlighting mTOR signaling and mitotic spindle-assembly genes in disease susceptibility.

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.

This study expands the genetic landscape of COPD by identifying 82 risk loci, revealing associations with lung function, cell types, and comorbidities.

The paper identifies 279 new genetic signals for lung function across multiple ancestries, showing associations with COPD and pleiotropic effects.

This study identifies over 50 new genetic loci associated with pulmonary function through a multiethnic meta-analysis of 90,715 individuals.

The paper identifies 12 novel COPD loci and candidate causal genes using GWAS and lung eQTL data from 129 risk loci.