Meyers, D.A.

Abstract Rationale Chronic obstructive pulmonary disease (COPD) manifests later in life and is a leading cause of death worldwide. While smoking-related lung pathology is a long-recognized COPD etiotype, emerging evidence suggests that early-life growth conditions also play an important role. A recently-developed index of early-life growth conditions – the difference between attained height and genotype-predicted height (height-GaP) – has been validated against prospectevely ascertained adverse early-life growth conditions. This study used height-GaP to evaluate the association of early-life growth conditions with COPD phenotype and prognosis later in life. Methods Among current and former smokers with spirometry-defined COPD in the SubPopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS), we computed height-GaP as the difference between attained height and genotype-predicted height. Regression models were fit for respiratory mortality (defined by primary diagnostic code), dyspnea prevalence (Medical Research Council dyspnea rating >1), chronic bronchitis prevalence, airflow obstruction severity as quantified by FEV1Q, and functional capacity (six-minute walk distance), adjusting for age, age2, sex, race-ethnicity, genotype-predicted height, cigarette smoking status and pack-years. Sensitivity analyses accounted for competing risks of death (Fine-Gray subdistribution hazard model) and accounted for later-life height loss (restricting sample to participants under 50 years). Results Among 1,258 participants with COPD (mean±SD age: 65.4±8.0 years, 42.4% female, mean±SD pack-years: 53.6±28.2, mean±SD FEV1%: 63.3±23.9%), there were 202 respiratory deaths over a median 7.5 years of follow-up (Figure). Larger height-GaP deficit was associated with higher respiratory mortality (adjusted hazard ratio [aHR]: 1.30 [95%CI: 1.12 to 1.50] per 1-SD increment in height-GaP deficit). This association remained significant after accounting for competing events (p=0.001) and when restricted to participants under 50 years (p=0.032). Larger height-GaP deficit was associated with higher dyspnea prevalence (adjusted prevalence ratio: 1.13 [95%CI: 1.02 to 1.25] per 1-SD increment in height-GaP deficit), lower FEV1Q (adjusted mean difference: -0.37 [95%CI: -0.45 to -0.29] per 1-SD height-GaP deficit), and shorter 6-minute walk (adjusted mean difference: -16.6 m [95%CI: -23.1 m to -10.0 m] per 1-SD increment in height-GaP deficit), but not chronic bronchitis or CAT score ≥10 (p>0.05 for each). Conclusions Among current and former smokers with COPD, a quantitative index of early-life conditions was associated with higher respiratory mortality, in addition to dyspnea, airflow obstruction severity and reduced functional capacity. These results suggest that early-life growth conditions may influence COPD phenotype and prognosis later in life.

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: 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.