The lung carries one of the highest somatic mutational loads among tissues in healthy individuals, however whether somatic mutations in the lung are enriched among patients with chronic lung diseases is unknown. We hypothesized that somatic mutations would be increased in smoking-related chronic lung diseases. To identify somatic mutations, we identified somatic single nucleotide variants (SNV) in lung tissue from RNA sequencing paired with blood-derived whole genomes from the Lung Tissue Research Consortium (n=1,364). SNVs were called with GATK MuTect and filtered to a set of 285,000 high confidence mutations. To test for associations with phenotypes, we selected subjects with confirmed histopathology, smoking history, and lung function data which included 29 normal (based on histology and lung function), 352 with chronic obstructive pulmonary disease (COPD), 164 with idiopathic pulmonary fibrosis (IPF).
Surprisingly, mutational burden (the number of SNVs) was not associated with age or smoking history, overall or within subgroups (P > 0.2). In addition, we did not find significant enrichment for somatic mutations in known lung cancer driver genes. However, we did find mutation burden significantly associated with reduced lung function (forced expiratory volume in 1 second (FEV1) % predicted) in COPD (R -0.16, p < 0.01) and IPF (R -0.29, p < 0.001), but not in controls (R -0.069, p = 0.7). In addition, mutation burden was associated with an increased proportion of airway versus alveolar epithelial cells (p <0.001 across all groups). There was no association of mutational burden with immune cell or stromal cell abundance. In multivariable regression analysis adjusted for age, sex, race and smoking history, both airway/alveolar epithelial ratio and lung function (FEV1% predicted) remained statistically significantly associated with mutational burden (Beta 16.7 and -7.27 respectively, p <10-4). Our study suggests that somatic mutations are associated with reduced lung function and cell types with high turnover rate in chronic lung disease.

Clinically-diagnosed rheumatoid arthritis (RA) is associated with interstitial lung disease (ILD). We hypothesize that biomarkers related to RA risk are associated with more lung parenchymal abnormalities and lung function decline, particularly among carriers of the MUC5B (rs35705950) risk allele (T), in a population-based cohort.

In the Multi-Ethnic Study of Atherosclerosis, linear-mixed effects models examined associations of baseline RA serologies (2000-2002) and presence of HLA-DRB1*04:01 allele (strongest genetic factor for RA) with longitudinal changes in computed tomography (CT)-derived lung high attenuation areas (HAAs, percentage of lung within -250 to -600 Hounsfield units, 2000-2018) and forced vital capacity (FVC, 2004-2018). Analyses were stratified by MUC5B (rs35705950) risk allele (T) carrier status and adjusted for age, sex, race/ethnicity, genetic ancestry, height, weight, smoking status, cigarette pack-years, and scanner parameters for HAAs analysis. Association between HLA-DRB1*04:01 and interstitial lung abnormalities (ILA) was examined using logistic regression.

There were 4,512 participants with HAAs and 2,866 with FVC assessments. A doubling of serum IgA rheumatoid factor (RF) was associated with a 0.64% increase in HAAs/10yr (95%CI 0.11-1.18) and a decrease in FVC of 10.6 mL/10yr (95%CI 2.7-18.5) among those with MUC5B risk allele (p-interaction=0.02). HLA-DRB1*04:01 carriers had a 1.22%/10yr (95%CI 0.18-2.27) increase in HAAs and a FVC decline of 58.7 mL/10yr (95%CI 4.9-112.5) in those with MUC5B risk allele (p-interaction=0.08). Associations of other RA serologies were weaker. HLA-DRB1*04:01 was not associated with ILA.

RA-related autoimmunity may contribute to increased lung densities on CT and lung function decline, particularly among MUC5B risk allele carriers.

Background: Chronic Obstructive Pulmonary Disease (COPD), diagnosed by reduced lung function, is a highly heterogeneous disease. Existing polygenic risk scores (PRS) enable early identification of genetic risk for COPD. However, predictive performance of the PRS is limited when the discovery and target populations are not well matched.
Methods: To improve cross-ethnic portability of risk prediction, we introduced a PrediXcan-derived polygenic transcriptome risk scores (PTRS), developed under the hypothesis that the biological mechanisms of disease are shared across ancestry groups. We constructed the PTRS using summary statistics from application of PrediXcan on large scale GWAS of lung function (Forced Expiratory Volume in 1 second (FEV1) and its ratio to Forced Vital Capacity (FEV1/FVC)) from the UK Biobank, which representing primarily European ancestry-based cohort. To examine prediction performance and cross-ethnic portability of the proposed PTRS candidates, we performed smoking-stratified analyses on multi-ethnic training data for 29,381 participants from TOPMed population/family-based cohorts (NHW=14,727, AA=7,025, HIS=7,629). The best risk score candidates were then tested for 11,771 multi-ethnic participants from TOPMed COPD-enriched studies (NHW=8,144, AA=3,627). Analyses were carried out for two dichotomous traits of COPD (Moderate-to-Severe and Severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC).
Results: While the novel PTRS had lower prediction accuracy for European ancestry participants than PRS, the PTRS performed slightly better than PRS for predicting COPD in heavy smoking African Americans (OR=1.24 [95%CI: 1.08-1.43] from PTRS and OR=1.10 [95% CI: 0.96-1.26] from PRS for Moderate-to-Severe COPD; for Severe COPD, OR=1.51 [95%CI: 1.04-12.19] from PTRS and OR=1.31 [95% CI: 0.87-1.96] from PRS). In addition, as hypothesized, the cross-ethnic portability was significantly higher for PTRS than for PRS (p<2.2e-16) for both dichotomous COPD traits and across all smoking strata.
Conclusions: Our study demonstrates the value of PTRS for improved prediction of COPD risk in African Americans. Future work will strengthen the PTRS framework by leveraging multi-ethnic gene expression reference data of larger sample sizes and from disease relevant tissues.