Air pollutant multiomics improves functional annotation of SNPs associated with lung disease

Asthma and chronic obstructive pulmonary disease (COPD) are lung diseases strongly influenced by interactions between genetic background and environmental exposures, particularly air pollutants. However, the biological mechanisms by which genetic variation and pollutant exposure impact disease remain poorly understood. Interpretation of genome-wide association studies is limited because most disease-associated variants occur in noncoding regions and are difficult to connect to functional regulatory mechanisms and downstream gene targets. Here, we integrate nascent RNA run-on sequencing with regulatory network inference to identify pollutant-responsive transcriptional regulatory elements and their upstream regulators in lung cells. Using newly generated and published datasets, we analyze multiomics responses to multiple air pollutants, including wood smoke particles, urban particulate matter, and Afghan dust particles. These analyses provide the first characterization of the nascent transcriptional response to urban particulate matter in primary lung cells and reveal both shared and pollutant-specific regulatory dynamics. We then integrate pollutant-responsive regulatory networks with genetic associations for asthma and COPD to prioritize noncoding variants for experimental validation. By linking these variants to candidate transcription factors and target genes, we identify mechanisms through which environmental exposures may interact with noncoding genetic variants to influence disease risk. This framework enables functional interpretation of noncoding variants through environmentally responsive transcriptional networks.