Ortega, V.E.

Abstract Rationale: Adverse social determinants of health (SDOH) are common in COPD and associated with increased mortality. However, cause-specific mortality is understudied. We evaluated associations of neighborhood-level SDOH with all-cause and cause-specific mortality among individuals with or at-risk for COPD. Methods: SPIROMICS is an ongoing, multi-center, longitudinal observational study of individuals across the US with current or former personal smoking histories (≥20 pack-years), with or without spirometrically-defined COPD. We established geocoded neighborhood-level SDOH at baseline using 2010 census tract data, including specifically, area deprivation index (ADI) (range 0-100, 100 = most disadvantaged), poverty level (percentage of households below the poverty line), poor food access (>1/3 of residents with no access to fresh food stores within 1 mile in urban areas and 10 miles in rural areas), and food desert (low income plus poor food access). We ascertained all-cause and cause-specific mortality through 12/31/2021 using the National Death Index. Using Cox proportional hazards models, we evaluated the risk of all-cause mortality for each SDOH. We utilized competing-risk regression models to evaluate the risk of cardiovascular, respiratory, COPD (a subset of respiratory), and neoplastic causes of death individually with each SDOH. Models were adjusted for demographics, smoking status, pack-years, BMI categories, coronary heart disease, FEV1% predicted and study site. Effect modification by lung function was assessed using multiplicative interactions between SDOH and FEV1% predicted. Results: Among 2977 participants, mean ADI was 40.1±27.8. Within a participant's neighborhood, on average 10.4±10.4% of households were below the poverty line, 933 (31.3%) had poor food access, and 220 (7.4%) resided in a food desert. During a median follow-up of 7.8 years (IQR 5.6-9.2), 744 (25.0%) participants died, most commonly from respiratory causes including COPD. Neither ADI nor neighborhood poverty level were associated with all-cause mortality or any specific cause of death (Table). Despite lack of associations between food access and mortality (Table), individuals residing in a food desert had elevated risks of all-cause (HR: 1.36 [95%CI: 1.03-1.79]) and cardiovascular (HR: 2.62 [95%CI: 1.5-4.35]) deaths. There was no significant effect modification by lung function. Conclusions: In a large cohort of individuals with personal smoking histories, with or without COPD, living in a food desert was associated with increased all-cause and cardiovascular mortality. However, neighborhood poverty level and food access, the two components of food desert, were not independently associated with mortality, suggesting the association of multimodal SDOH variables with mortality in this population.

Abstract Background:Chronic obstructive pulmonary disease (COPD) is a major global health burden, with genome-wide association studies explaining only a fraction of its heritability. Recent advancements, including large-scale, multi-ancestry whole-genome sequencing and a focus on severe, early-onset cases, offer promising avenues to identify additional COPD-related variants. Furthermore, recent studies show that adjusting for polygenic risk scores (PRS) enhances statistical power to detect rare variants, advancing efforts to understand COPD's genetic basis. Methods:We performed a whole genome sequencing association study of severe, early-onset COPD (FEV1 < 50%, age ≤ 55), FEV1 and FEV1/FVC in eight population-based studies (ARIC, CARDIA, CFS, CHS, FHS, HCHS/SOL, JHS, MESA) and seven COPD-enriched studies (COPDGene, ECLIPSE, Boston Early-Onset COPD Study, LTRC, TCGS (Poland), ICGN, SPIROMICS). We performed single-variant analyses using regenie software, applying a genome-wide significance threshold of P < 5.0 x 10-8. For rare variant set (MAF <1%) analysis, we annotated variants with loss of function or deleterious missense using WGS Annotator and prediction tools LOFTEE, SIFT, and PolyPhen2, then analyzed these variants with SKATO-ACAT method in regenie, adjusting for covariates including sex, sequencing center, and principal components of ancestry. We additionally evaluated adjusting the analyses for PRSs for lung function traits. Results: We analyzed 53,028 participants including 34,751 European, 12,508 African, 1,088 Latino American, and 674 East Asian ancestry. Across cohorts, we analyzed 5,063 severe (FEV1 < 50%) COPD cases, among whom 1,103 were early-onset (age ≤ 55). Excluding variants within 1 Mbp of known variants, single-variant analysis identified 68 novel associations (MAC ≥ 30), with 8 common associations (MAF ≥ 1%) in 6 genomic regions. Our most significant novel association for severe early-onset COPD was rs74297390 (intergenic to POM121L12, MAF = 9.6%, Beta = 0.41, P = 3.5 x 10-8). For FEV1 and FEV1/FVC ratio, variants near DKK2 and AKR1E2 were significantly associated and were replicated in an independent dataset (Shrine et al., 2019). DKK2 is an antagonist in the Wnt pathway. In a preliminary variant set analysis, CHRM3-AS1 was associated with severe early-onset COPD, GPAT with FEV1, and COL8A1I and HMCN1 with FEV1/FVC. With PRS adjustment, ST6GALNAC5 was associated with FEV1/FVC. Conclusions: In a whole genome sequencing study of lung function and severe early-onset COPD, we identified new loci for COPD-related phenotypes, including two novel loci that replicated in an independent dataset. Ongoing work involves variant set-based analyses with additional covariate adjustments, as well as PRS adjustments for binary traits.

Abstract Rationale: We have previously shown reduced total IgG, IgG subclasses, and levels/function of pneumococcal-specific antibodies are associated with increased risk for exacerbations of COPD (ECOPD). The binding of IgG to its receptor, the Fc receptor for IgG (FcγR) on immune cells, has a critical role in determining antibody levels and function. In addition to our research demonstrating that IgG heavy chain gene (IGHG) variants are associated with ECOPD, single nucleotide variants (SNVs) in genes encoding FcγR (FCGR1-3, which encode receptor subtypes FcγRI, FcγRIIa/b/c, and FcγRIIIa/b) may also result in antibody deficiency and increased exacerbation risk. Due to technical challenges in accurately identifying SNVs in FCGR, this region has not been well-characterized. The limited data that exist are from people of European descent, and prior studies have not included African American participants. We therefore sought to investigate the hypothesis that distinct SNVs in genes encoding FcγR are associated with disease severity and history of exacerbations in African American people with COPD. Methods: We developed a novel pipeline to identify SNVs in the coding (CDS) regions of chromosome 1 using short-read whole genome sequence data. The pipeline was benchmarked using paired long-read samples from Genome in a Bottle. SNVs were called within CDS regions and analyzed in a meta-analysis of self-reported African American participants in SPIROMICS and COPDGene (n=3063). Genetic variants were tested for association with COPD severity (GOLD spirometry stage 1-2 vs 3-4) as well as COPD exacerbations, adjusting for age, sex and principal components controlling for population structure. Results: Eight SNVs were significantly associated with frequent ECOPD (≥2 in one year) in the meta-analysis of African American COPDGene and SPIROMICS participants (Table). Seven of these 8 SNVs were found in FCGR3; 4 in the activating FCGR3A subtype, and 3 in FCGR3B. Seven of 11 SNVs associated with COPD severity were in FCGR3 (4 in FCGR3A; 3 in FCGR3B). One SNV (rs371849) was associated with both frequent exacerbations (P=0.002) and COPD severity (P=0.044). Conclusions: We used a cutting-edge variant calling pipeline to identify multiple SNVs in FCGR, primarily involving FcγRIII receptor subtypes, associated with COPD severity and exacerbations in African American participants in SPIROMICS and COPDGene. FcγRIII variants are known to cause impaired antibody levels and function in people of European descent. The structural and functional importance of newly recognized variants in African American cohorts require further study, as they may represent immunogenetic risk factors for antibody deficiency and exacerbations.

Abstract RATIONALE: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality worldwide. We previously published the polygenic transcriptome risk score (PTRS) which uses the cumulative effect of predicted gene expression to construct genetic predictors for complex diseases, and we demonstrated the value of PTRS for COPD built on Genotype-Tissue Expression (GTEx) Lung tissue with significantly improved cross-ancestry portability. However, the postmortem collection and lack of lung disease status on GTEx samples likely affect results. We hypothesized that the performance of PTRS will be improved by using disease-relevant expression quantitative trait loci (eQTLs) to construct the score. Here, we aimed to improve performance of PTRS by leveraging disease-relevant eQTLs. METHODS: We constructed two transcriptome prediction models, Elastic Net (EN) and Prediction Using Models Informed by Chromatin conformation and Epigenomics (PUMICE), using TOPMed lung RNA-seq data from the Lung Tissue Research Consortium (LTRC), which included COPD patients undergoing lung surgery. We compared their performance with two published models built on RNA-seq from GTEx Lung tissue, GTEx-EN and GTEx-PUMICE. We first integrated each of four transcriptome models with multi-ancestry GWAS of FEV1/FVC ratio (Shrine et al. 2023) to generate transcriptome-wide association study (TWAS) results, which produced trait-associated genes. Pathway analysis was then conducted on significant TWAS genes. Finally, the PTRS was computed on TWAS genes that were included in the top 20 significantly enriched pathways. We tested performance of PTRS in race-stratified analysis for moderate-to-severe and severe COPD in COPDGene. RESULTS: Our models produced more significant TWAS genes (FDR<0.05) and had more overlaps with genes identified for FEV1/FVC ratio from Shrine et al. 2023 compared to two GTEx models (Fig. A). Additionally, our models had significantly higher AUC for predicting both moderate-to-severe and severe COPD in race-stratified analysis. The mean AUC from LTRC-EN for predicting severe COPD was 0.57 and 0.55 respectively for EUR and AFA, which was significantly higher than mean AUC of 0.54 and 0.51 from GTEx-EN (Delong p-value=8.15x10-10 for EUR and 1.42x10-3 for AFA) (Fig. B). Similarly, the PTRS derived from our models produced higher Odds Ratios (OR) per standard deviation. The mean OR from LTRC-PUMICE for moderate-to-severe COPD was 1.25 and 1.18 respectively for EUR and AFA, while it was 1.17 and 1.12 from GTEx-PUMICE (Fig. C). CONCLUSIONS: Our study demonstrates the value of leveraging disease-relevant RNA-seq to construct transcriptome prediction models and their improvement on identifying lung function genes and predicting COPD across ancestries.