Abstract
Background
The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression.
Methods
We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants.
Results
We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections.
Conclusions
These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13073-021-00866-2.
Keywords: COVID-19, SARS-CoV-2, ACE2, eQTL, Bronchial epithelium
Background
Coronavirus disease 2019 (COVID-19), the clinical syndrome caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has led to a global crisis. As a respiratory virus, SARS-CoV-2 is hypothesized to gain entry into humans via the airway epithelium, where it initiates a host response that leads to the subsequent clinical syndrome. Despite an immense global burden of disease, the manifestations of SARS-CoV-2 infection vary enormously, from asymptomatic infection to progressive acute respiratory failure and death. The viral or host features that determine the course of disease in each individual are poorly understood. Multiple clinical risk factors for severe COVID-19 have been identified, including older age, male sex, African American race, smoking, and comorbidities such as hypertension, obesity, diabetes, cardiovascular disease, and chronic airway diseases [1–5], as well as host genetics [5–8].
The expression levels of genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response are hypothesized to be an important host factor that could partially underlie the substantial inter-individual variability in COVID-19 susceptibility and progression [9–11]. To this end, we investigate genetic and non-genetic factors influencing the expression of human genes that have been implicated in COVID-19 (study design in Fig. 1). We analyze RNA-sequencing (RNA-seq) data from bronchial brushing samples obtained from the SPIROMICS cohort (n = 163) [12], notable for the high burden of COVID-19-relevant comorbidities and rich phenotype and whole genome sequencing (WGS) data from the TOPMed Project [13]. For replication, we use two asthma RNA-seq data sets, SARP (n = 156) and MAST (n = 35) as well as expression quantitative trait loci (eQTL) data from GTEx [14]. Our analysis provides insights of the contribution of host factors and host genetics in the expression of COVID-19-related genes in the large airway epithelium for understanding inter-individual variation of COVID-19.
Methods
Study population
SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS)
SPIROMICS is a multi-site prospective cohort study in which the main objective is to identify subpopulations of chronic obstructive pulmonary disease (COPD) as well as markers of disease severity to enable targeted treatment and disease modification. Data were obtained from participants who underwent research bronchoscopy within SPIROMICS between February 1, 2012, and May 31, 2016. Participants ages 40–80 were enrolled across four strata (never smokers, smokers without COPD, mild/moderate COPD, and severe COPD). Full SPIROMICS study details including inclusion and exclusion criteria have been previously published [12]. Participants enrolled in SPIROMICS who consented to a research bronchoscopy and met all local requirements (e.g., any laboratory tests that are required by institutional policy to be administered prior to a bronchoscopy) were deemed eligible. Additional exclusion criteria for the SPIROMICS bronchoscopy sub-study [15] included history of cardiac disease or other comorbid condition severe enough to significantly increase risks based on investigator discretion, requirement of supplementary oxygen at rest based on arterial oxygen pressure less than 60 mmHg or arterial oxygen saturation less than 88%, severe lung function impairment defined as post-bronchodilator forced expiratory volume in 1 s (FEV1) less than 30% predicted, and use of anticoagulation or antiplatelet therapies.
Severe Asthma Research Program (SARP)
SARP is a prospective multi-center cohort study with a primary goal of improving the mechanistic and clinical understanding of severe asthma [16]. Adult and pediatric patients with and without asthma were recruited to the SARP III cohort between November 1, 2012, and October 1, 2014, by seven clinical research centers in the USA. The SARP protocol is an ongoing, six-visit, 3-year, longitudinal cohort study in which 60% of participants have severe asthma as defined by the European Respiratory Society/American Thoracic Society (ERS/ATS) criteria [17]. A subset of participants underwent research bronchoscopy. Exclusion criteria included history of smoking (> 5 pack year smoking history), co-existing lung disease, and uncontrolled comorbidities. All healthy control subjects had to have no history of asthma and normal lung function and methacholine bronchoprovocation testing. Participants with asthma had to meet ERS/ATS criteria for asthma (bronchodilator response to albuterol or positive methacholine bronchoprovocation test). Asthma had to be clinically stable at the time of bronchoscopy.
Mechanisms of ASThma study (MAST)
MAST is a single-center clinical study with a primary objective of understanding asthma biology through detailed analyses of airway secretions and tissues [18]. Mild steroid-naive asthmatics and healthy controls underwent research bronchoscopy between April 2007 and December 2011. All healthy control subjects had to have no history of asthma or allergies. Participants with asthma had to have a positive methacholine bronchoprovocation test and could not have used steroids in 6 weeks prior to enrollment. Additional exclusion criteria included respiratory infection within 4 weeks of enrollment and pregnancy.
Whole genome sequencing data
Trans-Omics for Precision Medicine (TOPMed) Project [13] data freeze 9 consist of whole genome sequences of 160,974 samples with at least 15x average coverage, including 2710 individuals from the SPIROMICS study. We obtained unphased genotypes for all individuals from the SPIROMICS study at sites with at least 10x sequencing depth (minDP10 call set) aligned to the human reference genome build GRCh38. Details regarding the DNA sample handling, quality control, library construction, clustering and sequencing, read processing, and sequence data quality control are described on the TOPMed website (https://www.nhlbiwgs.org/genetic). Variants passing all quality control (QC) filters were retained.
Derivation of airway epithelial transcriptomic data in SPIROMICS, SARP, and MAST
Cytological brushings of the airway epithelium were obtained from lower lobe bronchi at the segmental or subsegmental carina. RNA was isolated with miRNeasy extraction kits (Qiagen Inc., Valencia, CA). RNA quantity and quality were evaluated using a NanoDrop Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), respectively. Library preparation with multiplexing was performed using Illumina TruSeq Stranded Total RNA with Ribo-zero GOLD kit (SPIROMICS, SARP) or Human/Mouse/Rat kit (MAST) per manufacturer’s protocol. Samples were sequenced using one-hundred-fifty base-pair (SPIROMICS) or one-hundred base-pair (SARP, MAST) paired end reads via the Illumina HiSeq platform at the UCSF Sandler Genomics core. FASTQ files were quality filtered and aligned to the Ensembl GRCh38 genome build using STAR [19]. Read counts were normalized using the regularized logarithm transformation function of the DESeq2 package in R [20] and batch corrected using the Combat function in the SVA package in R [21]. Outlying samples with low quality (low raw read counts, high percentage of reads mapped to multiple loci, high percentage of unmapped reads) were identified by hierarchical clustering and principal component analyses and excluded from the final data sets.
Differential expression analysis of ACE2 in relation to host/environmental factors
Linear regression models were fitted to evaluate associations between ACE2 expression (based on normalized count) and clinical variables in the SPIROMICS, SARP, and MAST cohorts with and without adjustments for covariates (see Additional file 1 for additional details).
Differential exon usage
Following alignment, we indexed and sliced the SPIROMICS BAM files to include 51.6 kb of the ACE2 genomic region (chrX:15,556,393-15,608,016 in the hg38 genome build) using samtools [22]. GTF files were manually curated to include the three exons that contribute to differential isoform expression of ACE2 [23]. Of them, the truncated ACE2 transcript (dACE2) that does not bind the SARS-CoV-2 virus but is associated with an interferon-stimulated gene response in experimental models originates from Exon 1c. The exons were counted using the ASpli package in R [24]. After correcting for overall gene counts and differences in sequence depth, linear models adjusting for batch were used to analyze differences in exon usage in association with interferon-stimulated gene signature and clinical covariates. Interpretation of differential exon usage requires consideration of the necessary adjustment for variation in total transcript count. Thus, if overall ACE2 expression is decreased in association with an outcome, a differential increase in one exon adjusts the expression of that isoform away from the overall negative association, but does not necessarily mean that the isoform is not negatively associated with the outcome to a lesser extent. Further details are provided in Additional file 1.
Gene set enrichment analysis of expression changes induced by COVID-19
We built COVID-19-relevant gene sets from publicly available differential gene expression data from participants who underwent nasal/oropharyngeal swab sampling at the time of acute respiratory illness for COVID-19 diagnosis (94 participants with COVID-19, 41 with other viral illness, 103 with no virus identified, viruses identified by metagenomic sequencing analysis) using Supplementary File 1 from Mick et al. [25]. Biological pathway gene sets were built by inputting the genes differentially downregulated between SARS-CoV-2 infection and other viral illness (P < 0.05) into the Ingenuity Pathway Analysis canonical pathway function. Gene set enrichment analyses were then performed using FGSEA [26] and the CAMERA function [27] in limma against gene lists ranked by their log fold change differential expression in association with comorbid clinical risk factors. Barcode plots were made using CAMERA. Findings were considered significant at P < 0.05 and false discovery rate (FDR) < 0.05 if multiple corrections were necessary. Additional details are provided in Additional file 1.
COVID-19-related genes
We selected 514 candidate genes implicated in COVID-19 from six different sources: Hoffmann et al. [28], Gordon et al. [29], Blanco-Melo et al. [30], COVID-19 Cell Atlas (https://www.covid19cellatlas.org/), Gassen et al. [31], and Wang et al. [32]. Of them, 496 genes were expressed in bronchial epithelium in the SPIROMICS cohort. Further details are provided in Additional file 1.
Expression quantitative trait mapping
Expression quantitative trait locus (eQTL) mapping was performed in 144 unrelated individuals from the SPIROMICS bronchoscopy sub-study with WGS genotype data from TOPMed and gene expression from bronchial epithelium profiled with RNA-seq following the analysis pipeline from the Genotype-Tissue Expression (GTEx) Consortium [14]. In short, gene expression data was normalized as follows: (1) read counts were normalized between samples using TMM [33] with edgeR [34], (2) genes with TPM ≥ 0.1 and unnormalized read count ≥6 in at least 20% of samples were retained, and (3) expression values were transformed using rank-based inverse normal transformation across samples.
Cis-eQTL mapping was performed using tensorQTL [35] across 22,738 genes and 6,605,907 variants with minor allele frequency (MAF) ≥ 0.05 and variant call rate ≥ 0.9 within ± 1 Mb from the transcription start site (TSS) of the gene. As covariates in the model, we used 15 PEER factors [36], 4 genotype principal components and sex imputed from genotype data. To control for multiple testing, 10,000 permutations were performed and FDR < 0.05 was used to identify genes with statistically significant eQTLs (eGenes). Lead cis-eQTL effect size was quantified as allelic fold change (aFC) [37], ratio of expression of the haplotype carrying the alternative allele to expression of the haplotype carrying the reference allele of an eQTL.
Additionally, cell type interacting eQTLs (ieQTLs) were mapped using an interaction model: p ~ g + i + g × i + C, where p is the expression vector (normalized as described above), g is the genotype vector, i is the normalized cell type enrichment score from xCell [38], g × i is the interaction term, and C is the covariates matrix as used in standard eQTL mapping. Only variants with MAF > 0.1 in the samples belonging to the top and bottom halves of the distribution of cell type abundance were included in the analyses. Multiple testing correction was done at the gene level using eigenMT [39], followed by Benjamini-Hochberg procedure across genes at FDR 5%. Additional details are provided in Additional file 1.
Replication of cis-eQTLs and pathway analysis
We performed replication of cis-eQTLs (gene-variant pairs) found from bronchial epithelium in 49 tissues from the GTEx project v8 release [14] based on the proportion of true positives [40], π1, and concordance rate, the proportion of gene-variant pairs with the same allelic direction for variants with nominal P value < 1 × 10−4 in the given GTEx tissue. Then, we analyzed the replication and concordance measure as a function of sample size and median cell type enrichment scores for seven cell types [41]. Further details are provided in Additional file 1.
Also, we performed gene-level lookup in GTEx v8 and eQTLGen Consortium [42] and used the functional profiling webtool g:GOSt from g:Profiler [43] to perform pathway analysis of the 492 significant eGenes in SPIROMICS not tested in GTEx v8 Lung.
pheWAS of lead COVID-19 cis-eQTLs in SPIROMICS and querying PhenoScanner
We performed a phenome-wide association study (pheWAS) in 1980 non-Hispanic White and 696 individuals from other ethnic and racial groups from SPIROMICS for the 108 lead cis-eQTLs to evaluate for phenotypic associations with spirometric measures, cell count differentials, and other variables. PheWAS regression-based models were performed using PLINK 2/0 adjusting for principal components of ancestry, sex, body mass index (BMI), age, and smoking pack-years. Significance threshold was set for the number of eQTLs tested across phenotypes (P < 4.63 × 10−4).
Additionally, PhenoScanner v2 [44, 45] was used to lookup phenotype associations of the cis-eQTL variants from large-scale genome-wide association studies (GWAS) with association P value < 10−5. The phenoscanner R package (https://github.com/phenoscanner/phenoscanner) was used to perform the queries. Additional details are provided in Additional file 1.
Colocalization analysis
To assess evidence for shared causal variant of a cis-eQTL and a GWAS trait, we used the Bayesian statistical test for colocalization, coloc [46], with conditioning and masking to overcome one single causal variant assumption. Coloc was run on a 500-kb region centered on the lead cis-eQTL with priors set to p1 = 10−4, p2 = 10−4, p3 = 5 × 10−6. We used the coloc.signals() function with mode = iterative, method = mask for GWAS traits with linkage disequilibrium (LD) data from the 1000 Genomes Project, and method = single for the eQTLs. Posterior probability for colocalization (PP4) > 0.5 was used as evidence for colocalization (see Additional file 1 for further details).
Results
Smoking, obesity, and hypertension are associated with increased airway epithelial expression of functional ACE2 isoforms
We first analyzed expression levels of ACE2, the receptor of the SARS-CoV-2 Spike protein that is the key host gene for viral entry [28, 47], in relation to non-genetic host factors (Additional file 2: Table S1). Corroborating previous reports [11, 48–50], we found that current smoking, when compared to non-smoking, had the largest overall effect on ACE2 expression of any phenotypic feature studied in SPIROMICS, before and after adjustments for covariates (log2 fold change (FC) = 0.30 ± 0.06, P = 1.7 × 10−7, Fig. 2a). This effect was absent in former smokers. In similarly adjusted models, we found no association between ACE2 levels and COPD (Additional file 3: Figure S1a), nor with asthma in MAST [50] (Additional file 3: Figure S1c). In SARP, ACE2 levels were slightly lower in asthmatics compared to healthy controls (Additional file 3: Figure S1b), which was largely driven by decreased expression of ACE2 only in asthmatics on oral steroids (Additional file 3: Figure S1d). African American race was associated with increased ACE2 expression in both SPIROMICS and SARP, but no association after adjusting for covariates suggests that this was due to a higher prevalence of comorbid conditions (Additional file 3: Figure S1e-f). However, ACE2 expression was significantly higher across data sets in association with two relevant comorbidities, obesity and hypertension (Fig. 2b-c, Additional file 3: Figure S2a-e, Additional file 3: Figure S3a-b). Of note, we further found that use of anti-hypertensives in SPIROMICS attenuates the association between ACE2 and hypertension towards levels seen in non-hypertensive participants (Fig. 2c). When stratified by anti-hypertensive class, angiotensin receptor blockers (ARBs) and diuretics, but not ACE inhibitors or calcium channel blockers, were associated with lower ACE2 levels, partially dependent on smoking status (Additional file 3: Figure S3c). Counterintuitively, modest decreases in ACE2 expression were seen in SPIROMICS in association with age (log2 FC = − 0.064 ± 0.02, P = 0.005 for every 10-year age increase, Additional file 3: Figure S4a) and male sex (log2 FC = − 0.076 ± 0.035, P = 0.03, Fig. 2d) before and after adjustments, although similar associations were not seen in SARP or MAST. Although there were no significant differences in the above reported outcomes between males and females in SPIROMICS, former smokers were older (9.1 ± 1.3 years compared to current smokers, P = 3.19 × 10−10) as were participants with hypertension (4.6 ± 1.4 years, P = 0.002, Additional file 3: Figure S5). Sex and age were, however, both adjusted for in our analyses.
As chronic airway inflammation, prevalent but heterogeneous in the airway diseases studied in the included cohorts, can influence gene expression and the host response to infections, we next studied how stereotypic adaptive airway immune responses affect ACE2 expression. We used our previously validated gene expression signatures to quantify type 2-, interferon-, and IL-17-associated inflammation [18, 51, 52]. We found that ACE2 expression was associated with increased interferon-related inflammation, as previously reported [9], as well as IL-17-related but not type 2 inflammation across data sets (Fig. 2e). Corroborating the association with IL-17 inflammation, genes highly co-expressed with ACE2 expression included genes in our IL-17 signature across data sets (Additional file 2: Table S2).
Recent reports suggested that ACE2 induction by interferon stimulation may be explained by expression of a truncated ACE2 isoform (dACE2, initiated from exon 1c instead of 1a/b) that does not bind the SARS-CoV-2 spike protein [23, 53]. We first corroborated this finding, showing that our interferon-stimulated gene signature is associated with increased exon 1c but not exons 1a or 1b usage (Fig. 2f). We also identified an increase in exon 1a usage with age. This result suggests that although overall ACE2 expression is decreased in association with age, the full length transcript initiated from exon 1a is not decreased to the same extent or is even potentially increased with age (Additional file 3: Figure S4b). Importantly, differential exon 1c usage was not associated with any other clinical/biological outcomes of interest, suggesting that the full length transcript is responsible for the observed associations.
These results overall indicate that smoking, obesity, and hypertension affect airway epithelial expression of functional ACE2 isoforms, as previously shown for smoking [11, 48–50]. The ACE2 association with interferon-related inflammation appears to be explained by the truncated version of ACE2 [23, 53]. Together, these findings suggest that smoking, obesity, and hypertension may contribute to COVID-19 severity through an association with increased ACE2 expression, while other risk factors such as male sex and airway disease likely contribute via other mechanisms, corroborating recent evidence on sex differences in the immune response to COVID-19 [54].
Obesity, hypertension, and cardiovascular disease are associated with a relative COVID-19-relevant immunosuppression at the airway epithelium
As the host’s ability to mount an appropriate response to respiratory viruses may alter susceptibility to severe infection, we next performed gene set enrichment analyses (GSEA) to determine whether clinical risk factors are associated with similar airway gene expression patterns indicative of a diminished immune response that we recently identified early in COVID-19 by nasal/oropharyngeal swab [25]. As we previously reported, the genes differentially expressed in association with SARS-CoV-2 infection compared to other viruses at diagnosis indicate a diminished innate and adaptive immune response that may allow for unabated viral infection and account for the long pre-symptomatic period associated with COVID-19 [25]. We hypothesized that clinical risk factors uniquely associated with COVID-19 severity (e.g., cardiovascular disease, hypertension) could predispose patients to develop more severe disease by contributing to this relative immunosuppression. We derived gene sets from our previously published RNA-seq data collected by nasal/oropharyngeal swab from patients at diagnosis of acute respiratory illness; 94 had COVID-19, 41 had other viral illness, and 103 had no virus identified by metagenomic sequencing analysis [25]. First, we generated gene sets derived from the 100 genes most up- and downregulated in association with infection type to use to determine if there were global similarities in gene expression changes across data sets. For pathway analyses, we then generated COVID-19-relevant gene sets specific to particular canonical pathways by inputting significantly differentially expressed genes (FDR < 0.05) between SARS-CoV-2 infection and other viral respiratory illness into the Ingenuity Pathway Analysis (IPA) canonical pathway function (Additional file 2: Table S3). GSEA was then performed using FGSEA [26] in which these gene sets were tested against gene lists ranked by their log fold change differential expression in association with comorbid clinical risk factors.
We found that the genes most downregulated in association with SARS-CoV-2 infection as compared to other viruses were significantly enriched amongst genes downregulated in association with obesity, hypertension, and cardiovascular disease in SPIROMICS (Fig. 3a–c). Findings for obesity were replicated in SARP and MAST and for hypertension in SARP (Additional file 3: Figure S6a-c, hypertension data not collected in MAST, cardiovascular disease data not collected in SARP or MAST). Conversely, genes upregulated in other viral infections (or conversely, downregulated by SARS-CoV-2) were upregulated in inflammatory airway conditions (current and former smokers, COPD) (Fig. 3d-f). Aging was associated with an enrichment in genes downregulated by SARS-CoV-2 infection only in MAST while genes upregulated with SARS-CoV-2 infection were enriched with increasing age across the data sets (Additional file 3: Figure S6d-f). Our results demonstrate a sharp contrast between SARS-CoV-2 and other viral infections, which often trigger airway disease exacerbations by potentiating the chronic airway inflammation associated with these diseases and smoking exposure. We found this same pattern in association with asthma in MAST but not when considering asthma overall in SARP, potentially due to heterogeneity of its asthma subjects. When considering just asthmatics with uncontrolled symptoms or those on inhaled compared to no steroids (a marker of severity), we did find this same enrichment of genes up and downregulated in association with non-COVID viral infections (pathway enrichment shown in Fig. 3g).
We used pathway gene set enrichment to determine the potential biological significance of these findings. We found across data sets that pathway gene sets derived from genes downregulated by SARS-CoV-2 infection as compared to other viruses were also enriched amongst genes downregulated in association with obesity, hypertension, cardiovascular disease, and aging (FDR < 0.05, Fig. 3g, Additional file 2: Table S4). Enriched downregulated pathways included those related to pro-inflammatory cytokines such as IL-6 and IL-17 as well as macrophage and granulocyte activation. Furthermore, pathways related to cardiovascular and metabolic disease signaling such as atherosclerosis and diabetes signaling were also enriched. We confirmed the enriched findings by separately performing IPA canonical pathway analyses on the genes differentially expressed (P < 0.05) in association with these comorbidities, finding similar results in these global/unsupervised analyses (Additional file 2: Table S5). Conversely, pro-inflammatory airway conditions such as smoking and COPD led to opposite effects. These findings suggest that obesity, hypertension, cardiovascular disease, and age are associated with a relative COVID-19-relevant immunosuppression at the airway epithelium, which, by stunting early anti-viral host responses, could contribute to increased susceptibility to SARS-CoV-2 infection and disease severity.
Host genetics has a biologically meaningful effect on the airway epithelial expression of many COVID-19-related genes
In order to map host genetic variants, we focused on 496 genes implicated in SARS-CoV-2 infection (Additional file 2: Table S6): ACE2 and TMPRSS2, key genes for viral entry [28]; CTSL, CTSB, and BSG, which may have a role as alternative routes for viral entry [28, 32]; host genes with protein-protein interactions with viral proteins [29]; differentially expressed genes as a response to the infection in cultured airway epithelial cells [30]; genes involved in autophagy that might counteract viral infection [31]; and other high interest genes from the COVID-19 Cell Atlas. Our cis-eQTL mapping in SPIROMICS (n = 144) identified significant (genome-wide FDR < 0.05) genetic regulatory variation for 108 (21.8%) of these COVID-19-related genes (Fig. 4a, Additional file 2: Table S7), with many genes also having significant eQTLs in other tissues in GTEx [14] (Additional file 2: Table S8).
Given the sample size, we have good power to discover the vast majority of eQTLs with > 2-fold effect on gene expression [14]. Many of the genes have a substantial genetic effect on gene expression: for example, the MERS receptor DPP4 [55] has a cis-regulatory variant rs6727102 where the alternative allele decreases expression by 3.3-fold (Fig. 4a). In 16 genes, the genetic regulatory effects were > 50% of the magnitude of the differential expression induced by SARS-CoV-2 infection [30] (Fig. 4b). While the key genes ACE2 or TMPRSS2 did not have eQTLs in bronchial epithelium (Additional file 3: Figure S7a-b), as previously reported [50], TMPRSS2 has an eQTL in GTEx lung tissue. This is consistent with the lack of phenome-wide association signals [56] or COVID-19 GWAS association at these loci (round 3 meta-analyses by COVID-19 Host Genetics Initiative [8]), suggesting that genetic regulation of these two genes is unlikely to contribute to potential host genetic effects on COVID-19. Many of the genes analyzed for eQTLs had variation in expression associated to clinical factors and comorbidities, with current smoking associated with the highest number of up-and downregulated genes in association with comorbidity (Additional file 3: Figure S8a-b). Compared to ACE2, the effect of current smoking on the expression of TMPRSS2 was modest (Additional file 3: Figure S7c), and as previously reported [10], expression levels of TMPRSS2 were higher in asthmatic than healthy controls, but not in COPD, and it decreased in association with steroid use (Additional file 3: Figure S7d).
Cis-eQTLs from bronchial epithelium replicated at a high rate in those tissues from the GTEx v8 data set [14] that have a large sample size or high epithelial cell abundance (Fig. 4c, Additional file 3: Figure S9a-b), reflecting similarity in cell type composition manifesting in similarity of regulatory variant activity [14]. However, relative to GTEx lung, our bronchial epithelium eQTLs included genes enriched for sensory perception of chemical stimulus and smell (Additional file 2: Table S9). In total, 143 genes with eQTLs in SPIROMICS were not tested in GTEx nor eQTLGen Consortium [42], since bronchial epithelium is not well represented in previous eQTL catalogs. In addition to standard cis-eQTL mapping, we mapped cell type interacting eQTLs [41] but none were discovered for the COVID-19-related genes.
Regulatory variants for COVID-19-related genes as host risk factors for COVID-19 susceptibility
To study the role of these regulatory variants in COVID-19 risk, we first analyzed eQTLs in the chromosome 3 locus with a significant association with hospitalization due to COVID-19 [8] (meta-analyses round 3) and severe COVID-19 with respiratory failure [5, 7]. We found no significant eQTLs in the bronchial epithelium for any of the six genes in this locus (Additional file 3: Figure S10a), suggesting that this genetic association may be driven by other tissues or cell types with a role in COVID-19. Moreover, these genes were rather lowly expressed in bronchial epithelium (Additional file 3: Figure S10b).
Next, given that COVID-19 GWAS still have limited power, we analyzed how regulatory variants for COVID-19-relevant genes associate to other immune- or respiratory-related phenotypes in large GWAS. Indication of these variants affecting (respiratory) infections would provide hypotheses of variants that might play a role in COVID-19 risk and its comorbidities (Fig. 5a). Thus, we performed a pheWAS analysis by Phenoscanner v2 [44, 45] for the 108 lead cis-eQTLs for COVID19-related genes and diverse set of phenotypes (Additional file 2: Table S10). Furthermore, we used the SPIROMICS phenotype data to study associations for 20 phenotypes (Additional file 2: Table S11). Of these loci, 44 were associated with at least one phenotype (P < 10−5), with expected patterns—best powered GWAS traits having most associations and shared signals for highly correlated traits (Additional file 3: Figure S11).
We further used colocalization analysis to extract loci where the eQTL and GWAS signals are likely to share a causal variant, as opposed to spurious overlap, focusing on 20 loci with associations for hematological and respiratory system traits of which 12 colocalized (PP4 > 0.5, Fig. 5b, Additional file 2: Table S12). In Fig. 5c, we highlight IFITM3 that is upregulated by SARS-CoV-2 infection [30] and has an eQTL associated with multiple blood cell traits of the immune system [58] and neutrophil count in SPIROMICS (P < 0.002). In addition, IFITM3 has a well-characterized role in the entry of multiple viruses, including coronaviruses [59]. Another interesting gene, ERMP1 (Fig. 5d), has an eQTL colocalizing with an asthma GWAS association in the UK Biobank. ERMP1 interacts with the SARS-CoV-2 protein Orf9c [29] and ranks highly in a genome-wide CRISPR screen for genes required for SARS-CoV-2 infection [60]. Also, severe asthma is a risk factor for COVID-19 hospitalization [5] and death [61]. An eQTL for the MEPCE gene that interacts with SARS-Cov-2 protein Nsp8 [29] is associated with platelet parameters [58] (Fig. 5e). Interestingly, platelets are hyperactivated in COVID-19 [62, 63], and platelet count could be used as a prognostic biomarker in COVID-19 patients [64–66].
Discussion
Using whole genome profiling data available from biologically relevant data sets, we have generated an archive of gene expression alterations that may contribute to COVID-19 susceptibility and severity. Although we include an extensive analysis of ACE2 gene expression in bronchial epithelium and isoform usage, our findings extend beyond this, providing insight into the contribution of genetics and specific clinical risk factors in the airways’ response to the SARS-CoV-2 virus.
We demonstrate replicable associations between current smoking, obesity, hypertension, and increased bronchial epithelial ACE2 expression, potentially facilitating SARS-CoV-2 entry into host cells. Obesity and hypertension have been strongly linked with COVID-19 susceptibility and severity [1–5]. We were not well-powered to study diabetes, but in a sputum gene expression study, we did find an association between diabetes and increased ACE2 expression [67]. Together, this work suggests that one mechanism by which diseases associated with the metabolic syndrome are uniquely susceptible to COVID-19 is through increased ACE2 expression. Although ACE2 interacts with angiotensin 2 [68], we did not find that renin-angiotensin system-modifying drugs increased ACE2 expression. In fact, although our sample size was small, our data suggests that angiotensin receptor blockers are associated with lower ACE2 expression levels in smokers.
Although we observed that the largest increases in ACE2 expression were amongst current smokers, active smoking has not been identified as one of the largest risk factors for COVID-19 [1–5]. Early reports suggested a lower prevalence of smoking amongst patients with COVID-19 as compared to the general population. However, these reports have been debunked as confounded and inappropriately designed based on the flawed assumption that individuals with symptomatic COVID-19 reflect the general population when they are actually older with more comorbidities [69]. Well-adjusted studies in COVID-19 have shown that current smoking is indeed associated with increased disease severity [70, 71]. Nonetheless, current smoking does not appear to be the biggest risk factor for developing severe COVID-19 disease in large clinical studies, and thus mechanisms beyond ACE2 receptor binding of the virus must be explored.
We find evidence that the truncated dACE2 transcript is present in the bronchial epithelium and correlated with the expression of known interferon stimulated genes (ISGs). However, it does not appear to account for the observed clinical associations with overall ACE2 expression. The functional role for dACE2 is not currently known although it does not appear to bind SARS-CoV-2 [23, 53]. However, others have speculated [23] that during viral infections when ISGs are stimulated, dACE2 may act as a dummy receptor for other ACE2 ligands (e.g., microRNA-200c-3p) that if bound to ACE2 would lead to internalization of the ACE2-ligand complex and functional depletion of ACE2. Thus, dACE2 may keep ACE2 levels high during infection. Our observations suggest that it is, however, the full length transcript and not this truncated isoform that is associated with clinical risk factors.
It is likely that much of the inter-individual variation in COVID-19 is driven by a more complex molecular response to the virus in the airway than expression of ACE2 alone. This supposition is supported by our results demonstrating that obesity, hypertension, and cardiovascular comorbidities, as well as aging, are associated with a downregulation of mucosal immune response pathways similar to that seen in early SARS-CoV-2 infection in comparison to other viral infections. Together with clinical data and Mendelian randomization analyses of the causal role of smoking and BMI on severe COVID-19 [72], our result suggest that these important comorbidities increase COVID-19 susceptibility and severity by creating an airway microenvironment in which SARS-CoV-2 can gain a foothold before an effective host response is mounted. In SARS-CoV, a delayed innate immune response in tandem with early robust viral replication has been shown to lead to an enhanced late pro-inflammatory state and more severe lung injury [73]. Recent evidence suggests that SARS-CoV-2 may also impair early innate immune defenses through a host shutdown process [74]. Although diseases of the metabolic syndrome (e.g., cardiovascular conditions, obesity, and diabetes) are often associated with increased systemic inflammation, there is evidence of an associated delay in inflammatory cell recruitment to the lung during coronavirus infection in animal models [75, 76]. Further study of the lung-specific immune environment associated with these systemic diseases may be crucial to understanding susceptibility to severe SARS-CoV-2 infection.
In contrast to metabolic disorders, we find that inflammatory airway conditions increase gene expression indicative of increased innate and adaptive immune responses, potentially priming individuals for airway disease exacerbations in response to other viruses but not SARS-CoV-2. This is consistent with the large body of research showing that viruses trigger the majority of airway disease exacerbations [77]. SARS-CoV-2, however, appears to have a different immune profile and does not appear to be a major trigger for airway disease exacerbations in clinical studies [78, 79].
Furthermore, we show that host genetics has a biologically meaningful effect on the expression of many genes in the bronchial epithelium that may play an important role in COVID-19, including genes of interest as future drug targets that may not be covered by previous large eQTL catalogs from other tissue types. While we did not observe significant genetic regulatory effects for ACE2 and TMPRSS2, the effect of regulatory variants on the expression of some COVID-19-related genes can be as strong as the expression changes induced by SARS-CoV-2 infection, highlighting the possible important role of host genetics in COVID-19. Most notably, 3p21.31 locus is robustly shown to be associated with COVID-19 severity [5, 7, 8], but the functional mechanisms are unclear. The six candidate genes—SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, and XCR1—were not highly expressed in bronchial epithelium, except for LZTFL1, and did not have eQTLs in our data set, suggesting that eQTL studies from other tissues and cell types could provide more evidence for the causative gene(s) of this genetic association.
Most severe cases of SARS-CoV-2 infection progress to acute respiratory distress syndrome and respiratory failure, thus regulatory variants for COVID-19-related genes that also affect respiratory infections or immune-related outcomes of a possible host response to a virus serve as candidates for host genetic factors for COVID-19, or its severity. We pinpoint multiple COVID-19-interacting genes for which genetic regulatory variants associate with immune- or respiratory-related outcomes, including the interferon-induced transmembrane protein 3 (IFITM3), endoplasmic reticulum metallopeptidase 1 (ERMP1), and methylphosphate capping enzyme (MEPCE), making them strong candidates for host genetic risk factors.
Conclusions
Altogether, our findings of genetic and non-genetic factors affecting the expression of COVID-19-related genes in bronchial epithelium provide essential insights for understanding inter-individual variation of COVID-19 and developing therapeutic targets for COVID-19.
Supplementary Information
Acknowledgements
The authors thank the SPIROMICS participants and participating physicians, investigators, and staff for making this research possible. More information about the study and how to access SPIROMICS data is available at www.spiromics.org. The authors would like to acknowledge the University of North Carolina at Chapel Hill BioSpecimen Processing Facility for sample processing, storage, and sample disbursements (http://bsp.web.unc.edu/).
We would like to acknowledge the following current and former investigators of the SPIROMICS sites and reading centers: Neil E Alexis, MD; Wayne H Anderson, PhD; Mehrdad Arjomandi, MD; Igor Barjaktarevic, MD, PhD; R Graham Barr, MD, DrPH; Patricia Basta, PhD; Lori A Bateman, MSc; Surya P Bhatt, MD; Eugene R Bleecker, MD; Richard C Boucher, MD; Russell P Bowler, MD, PhD; Stephanie A Christenson, MD; Alejandro P Comellas, MD; Christopher B Cooper, MD, PhD; David J Couper, PhD; Gerard J Criner, MD; Ronald G Crystal, MD; Jeffrey L Curtis, MD; Claire M Doerschuk, MD; Mark T Dransfield, MD; Brad Drummond, MD; Christine M Freeman, PhD; Craig Galban, PhD; MeiLan K Han, MD, MS; Nadia N Hansel, MD, MPH; Annette T Hastie, PhD; Eric A Hoffman, PhD; Yvonne Huang, MD; Robert J Kaner, MD; Richard E Kanner, MD; Eric C Kleerup, MD; Jerry A Krishnan, MD, PhD; Lisa M LaVange, PhD; Stephen C Lazarus, MD; Fernando J Martinez, MD, MS; Deborah A Meyers, PhD; Wendy C Moore, MD; John D Newell Jr, MD; Robert Paine, III, MD; Laura Paulin, MD, MHS; Stephen P Peters, MD, PhD; Cheryl Pirozzi, MD; Nirupama Putcha, MD, MHS; Elizabeth C Oelsner, MD, MPH; Wanda K O’Neal, PhD; Victor E Ortega, MD, PhD; Sanjeev Raman, MBBS, MD; Stephen I. Rennard, MD; Donald P Tashkin, MD; J Michael Wells, MD; Robert A Wise, MD; and Prescott G Woodruff, MD, MPH.
The project officers from the Lung Division of the National Heart, Lung, and Blood Institute were Lisa Postow, PhD, and Lisa Viviano, BSN; SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C), grants from the NIH/NHLBI (U01 HL137880 and U24 HL141762), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc.; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; Novartis Pharmaceuticals Corporation; Nycomed GmbH; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; and Theravance Biopharma and Mylan.
Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Genome Sequencing for “NHLBI TOPMed: SubPopulations and InteRmediate Outcome Measures In COPD Study” (phs001927) was performed at the Broad Institute Genomics Platform (HHSN268201600034I). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. A list of banner authors for the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium is provided in the Additional file 4.
Abbreviations
- SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
- COVID-19
Coronavirus disease 2019
- SPIROMICS
SubPopulations and InteRmediate Outcome Measures In COPD Study
- COPD
Chronic obstructive pulmonary disease
- SARP
Severe Asthma Research Program
- MAST
Mechanisms of ASThma Study
- eQTL
Expression quantitative trait locus
- pheWAS
Phenome-wide association study
- RNA-seq
RNA-sequencing
- WGS
Whole genome sequencing
- TOPMed
Trans-Omics for Precision Medicine
- GTEx
Genotype-Tissue Expression
- FEV1
Forced expiratory volume in 1 s
- ERS/ATS
European Respiratory Society/American Thoracic Society
- QC
Quality control
- dACE2
Truncated ACE2 transcript
- FDR
False discovery rate
- MAF
Minor allele frequency
- TSS
Transcription start site
- eGene
Gene with statistically significant eQTL
- aFC
Allelic fold change
- BMI
Body mass index
- GWAS
Genome-wide association study
- LD
Linkage disequilibrium
- PP4
Posterior support for colocalization in coloc, defined as posterior probability for observing an association with both traits driven by a shared causal variant (hypothesis four)
- FC
Fold change
- ARB
Angiotensin receptor blockers
- GSEA
Gene set enrichment analysis
- IPA
Ingenuity Pathway Analysis
- EFO
Experimental factor ontology
- ISG
Interferon stimulated genes
Authors’ contributions
S.K., T.L., S.A.C., P.G.W., and V.E.O. designed the study. S.A.C., S.G., J.N., and S.N. performed the differential expression and pathway analyses. S.K., M.M., A.P., and K.L.B. performed the eQTL analyses. V.E.O. and E.A. performed the phenome-wide association analysis. I.Z.B., R.G.B., E.R.B., R.P.B., A.P.C., C.B.C., D.J.C., G.J.C., J.L.C., M.T.D., M.K.H., N.N.H., E.A.H., R.J.K., J.A.K., F.J.M., D.A.M., R.P., S.P.P., M.C., L.C.D., S.C.E., J.V.F., E.I., N.N.J., B.D.L., X.L., W.C.M., S.E.W., J.Z., and C.L. were involved in the acquisition and processing of data. T.L. and S.A.C. supervised the work. S.K., T.L., and S.A.C. wrote the first draft. J.L.C., R.P.B., R.P., S.E.W., R.J.K., A.P.C., M.M., and K.L.B. contributed to the editing of the manuscript. All authors approved the final version of the manuscript.
Funding
This work was funded by the following funding sources: R01HL142992 (V.E.O.), R01HL137880 (V.E.O.), F30HG011194 (M.M.), T32HL144442 (K.L.B., and R.G.B.), R01MH106842 (T.L.), R01HL142028 (T.L., R.G.B., and S.K.), R01GM122924 (T.L.), UM1HG008901 (T.L.), R01GM124486 (T.L.), K23HL123778 (S.A.C.), R01HL121774 (S.A.C.), and U01HL137880 (S.A.C.). The funders had no role in study design, collection, analysis, and interpretation of data, or writing of the manuscript.
Availability of data and materials
The RNA-seq data for SPIROMICS and SARP are deposited to dbGaP at accessions phs001119.v1.p1 and phs001446, respectively. While awaiting data release via dbGaP, investigators may contact the corresponding authors or the SPIROMICS and SARP studies at https://www.spiromics.org/spiromics/ and http://www.severeasthma.org/home.html to discuss gaining access to the data via the ancillary study mechanism for these studies. MAST RNA-seq data are available at Gene Expression Omnibus (GEO) (accession number GSE67472 [80]). TOPMed WGS freeze 9 data for the SPIROMICS cohort will be available at dbGaP under accession number phs001927.
Full eQTL summary statistics for the 496 COVID-19-related genes generated during the current study can be downloaded from the GitHub repository at https://github.com/LappalainenLab/spiromics-covid19-eqtl/tree/master/eqtl/summary_stats [81]. eQTL mapping analyses code has been deposited to the GitHub repository at https://github.com/LappalainenLab/spiromics-covid19-eqtl [82].
Declarations
Ethics approval and consent to participate
The SPIROMICS study was approved by the Institutional Review Boards at each cooperating institutions: Columbia University IRB 2 (AAAE9315), University of Iowa IRB-01 (201308719), Johns Hopkins IRB-5 (NA_00035701), UCLA Medical IRB 1 (MIRB1) (10001740/18-000403), University of Michigan IRBMED B1 Board (HUM00036346/ HUM00141222), National Jewish Health IRB (HS2678), UCSF IRB Parnassus Panel (10-03169), Temple University IRB A2 (21416), U of Alabama at Birmingham IRB #2 (120906004), University of Illinois IRB #3 (2013-0939), University of Utah IRB Panel Review Board 5 (00027298/ 00108836), Wake Forest University IRB #5 (00012805/00048727), UNC Non-Biomedical IRB (10-0048), UCLA Medical IRB 1 (MIRB1) (18-000458), and University of Iowa IRB-01 (201003733). All participants provided written informed consent. The SARP and MAST studies were approved by the appropriate institutional review board at the participating sites and all participants provided written informed consent. The research conformed to the principles of the Helsinki Declaration.
Consent for publication
Not applicable
Competing interests
S.A.C. advises for AstraZeneca, GlaxoSmithKline, Glenmark Pharmaceuticals, and Amgen, gave invited lectures to Sonovion and Genentech, and writes for UpToDate. T.L. advises and has equity in Variant Bio and is a member of the scientific advisory board of Goldfinch Bio. V.E.O. has served and currently serves on Independent Data and Monitoring Committee for Regeneron and Sanofi for COVID-19 therapeutic clinical trials unrelated to the current manuscript. The remaining authors declare that they have no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Tuuli Lappalainen and Stephanie A. Christenson contributed equally to this work.
Contributor Information
Silva Kasela, Email: skasela@nygenome.org.
Tuuli Lappalainen, Email: tlappalainen@nygenome.org.
Stephanie A. Christenson, Email: stephanie.christenson@ucsf.edu
NHLBI SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS):
Neil E. Alexis, Wayne H. Anderson, Mehrdad Arjomandi, Igor Barjaktarevic, R. Graham Barr, Patricia Basta, Lori A. Bateman, Surya P. Bhatt, Eugene R. Bleecker, Richard C. Boucher, Russell P. Bowler, Stephanie A. Christenson, Alejandro P. Comellas, Christopher B. Cooper, David J. Couper, Gerard J. Criner, Ronald G. Crystal, Jeffrey L. Curtis, Claire M. Doerschuk, Mark T. Dransfield, Brad Drummond, Christine M. Freeman, Craig Galban, Mei Lan K. Han, Nadia N. Hansel, Annette T. Hastie, Eric A. Hoffman, Yvonne Huang, Robert J. Kaner, Richard E. Kanner, Eric C. Kleerup, Jerry A. Krishnan, Lisa M. LaVange, Stephen C. Lazarus, Fernando J. Martinez, Deborah A. Meyers, Wendy C. Moore, John D. Newell, Jr, Robert Paine, III, Laura Paulin, Stephen P. Peters, Cheryl Pirozzi, Nirupama Putcha, Elizabeth C. Oelsner, Wanda K. O’Neal, Victor E. Ortega, Sanjeev Raman, Stephen I. Rennard, Donald P. Tashkin, J. Michael Wells, Robert A. Wise, and Prescott G. Woodruff
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium:
Namiko Abe, Gonçalo Abecasis, Francois Aguet, Christine Albert, Laura Almasy, Alvaro Alonso, Seth Ament, Peter Anderson, Pramod Anugu, Deborah Applebaum-Bowden, Kristin Ardlie, Dan Arking, Donna K. Arnett, Allison Ashley-Koch, Stella Aslibekyan, Tim Assimes, Paul Auer, Dimitrios Avramopoulos, John Barnard, Kathleen Barnes, R. Graham Barr, Emily Barron-Casella, Lucas Barwick, Terri Beaty, Gerald Beck, Diane Becker, Lewis Becker, Rebecca Beer, Amber Beitelshees, Emelia Benjamin, Takis Benos, Marcos Bezerra, Larry Bielak, Joshua Bis, Thomas Blackwell, John Blangero, Eric Boerwinkle, Donald W. Bowden, Russell Bowler, Jennifer Brody, Ulrich Broeckel, Jai Broome, Karen Bunting, Esteban Burchard, Carlos Bustamante, Erin Buth, Brian Cade, Jonathan Cardwell, Vincent Carey, Cara Carty, Richard Casaburi, James Casella, Peter Castaldi, Mark Chaffin, Christy Chang, Yi-Cheng Chang, Daniel Chasman, Sameer Chavan, Bo-Juen Chen, Wei-Min Chen, Yii-Der Ida Chen, Michael Cho, Seung Hoan Choi, Lee-Ming Chuang, Mina Chung, Ren-Hua Chung, Clary Clish, Suzy Comhair, Matthew Conomos, Elaine Cornell, Adolfo Correa, Carolyn Crandall, James Crapo, L. Adrienne Cupples, Joanne Curran, Jeffrey Curtis, Brian Custer, Coleen Damcott, Dawood Darbar, Sayantan Das, Sean David, Colleen Davis, Michelle Daya, Mariza de Andrade, Lisa de las Fuentes, Michael DeBaun, Ranjan Deka, Dawn DeMeo, Scott Devine, Qing Duan, Ravi Duggirala, Jon Peter Durda, Susan Dutcher, Charles Eaton, Lynette Ekunwe, Adel El Boueiz, Patrick Ellinor, Leslie Emery, Serpil Erzurum, Charles Farber, Tasha Fingerlin, Matthew Flickinger, Myriam Fornage, Nora Franceschini, Chris Frazar, Mao Fu, Stephanie M. Fullerton, Lucinda Fulton, Stacey Gabriel, Weiniu Gan, Shanshan Gao, Yan Gao, Margery Gass, Bruce Gelb, Xiaoqi ( Priscilla) Geng, Mark Geraci, Soren Germer, Robert Gerszten, Auyon Ghosh, Richard Gibbs, Chris Gignoux, Mark Gladwin, David Glahn, Stephanie Gogarten, Da-Wei Gong, Harald Goring, Sharon Graw, Daniel Grine, C. Charles Gu, Yue Guan, Xiuqing Guo, Namrata Gupta, Jeff Haessler, Michael Hall, Daniel Harris, Nicola L. Hawley, Jiang He, Ben Heavner, Susan Heckbert, Ryan Hernandez, David Herrington, Craig Hersh, Bertha Hidalgo, James Hixson, Brian Hobbs, John Hokanson, Elliott Hong, Karin Hoth, Chao ( Agnes) Hsiung, Yi-Jen Hung, Haley Huston, Chii Min Hwu, Marguerite Ryan Irvin, Rebecca Jackson, Deepti Jain, Cashell Jaquish, Min A. Jhun, Jill Johnsen, Andrew Johnson, Craig Johnson, Rich Johnston, Kimberly Jones, Hyun Min Kang, Robert Kaplan, Sharon Kardia, Sekar Kathiresan, Shannon Kelly, Eimear Kenny, Michael Kessler, Alyna Khan, Wonji Kim, Greg Kinney, Barbara Konkle, Charles Kooperberg, Holly Kramer, Christoph Lange, Ethan Lange, Leslie Lange, Cathy Laurie, Cecelia Laurie, Meryl LeBoff, Jiwon Lee, Seunggeun Shawn Lee, Wen-Jane Lee, Jonathon LeFaive, David Levine, Dan Levy, Joshua Lewis, Xiaohui Li, Yun Li, Henry Lin, Honghuang Lin, Keng Han Lin, Xihong Lin, Simin Liu, Yongmei Liu, Yu Liu, Ruth J. F. Loos, Steven Lubitz, Kathryn Lunetta, James Luo, Michael Mahaney, Barry Make, Ani Manichaikul, Jo Ann Manson, Lauren Margolin, Lisa Martin, Susan Mathai, Rasika Mathias, Susanne May, Patrick McArdle, Merry-Lynn McDonald, Sean McFarland, Stephen McGarvey, Daniel McGoldrick, Caitlin McHugh, Hao Mei, Luisa Mestroni, Deborah A. Meyers, Julie Mikulla, Nancy Min, Mollie Minear, Ryan L. Minster, Braxton D. Mitchell, Matt Moll, May E. Montasser, Courtney Montgomery, Arden Moscati, Solomon Musani, Stanford Mwasongwe, Josyf C. Mychaleckyj, Girish Nadkarni, Rakhi Naik, Take Naseri, Pradeep Natarajan, Sergei Nekhai, Sarah C. Nelson, Bonnie Neltner, Deborah Nickerson, Kari North, Jeff O’Connell, Tim O’Connor, Heather Ochs-Balcom, David Paik, Nicholette Palmer, James Pankow, George Papanicolaou, Afshin Parsa, Juan Manuel Peralta, Marco Perez, James Perry, Ulrike Peters, Patricia Peyser, Lawrence S. Phillips, Toni Pollin, Wendy Post, Julia Powers Becker, Meher Preethi Boorgula, Michael Preuss, Bruce Psaty, Pankaj Qasba, Dandi Qiao, Zhaohui Qin, Nicholas Rafaels, Laura Raffield, Vasan S. Ramachandran, D. C. Rao, Laura Rasmussen-Torvik, Aakrosh Ratan, Susan Redline, Robert Reed, Elizabeth Regan, Alex Reiner, Muagututi‘a Sefuiva Reupena, Ken Rice, Stephen Rich, Dan Roden, Carolina Roselli, Jerome Rotter, Ingo Ruczinski, Pamela Russell, Sarah Ruuska, Kathleen Ryan, Ester Cerdeira Sabino, Danish Saleheen, Shabnam Salimi, Steven Salzberg, Kevin Sandow, Vijay G. Sankaran, Christopher Scheller, Ellen Schmidt, Karen Schwander, David Schwartz, Frank Sciurba, Christine Seidman, Jonathan Seidman, Vivien Sheehan, Stephanie L. Sherman, Amol Shetty, Aniket Shetty, Wayne Hui-Heng Sheu, M. Benjamin Shoemaker, Brian Silver, Edwin Silverman, Jennifer Smith, Josh Smith, Nicholas Smith, Tanja Smith, Sylvia Smoller, Beverly Snively, Michael Snyder, Tamar Sofer, Nona Sotoodehnia, Adrienne M. Stilp, Garrett Storm, Elizabeth Streeten, Jessica Lasky Su, Yun Ju Sung, Jody Sylvia, Adam Szpiro, Carole Sztalryd, Daniel Taliun, Hua Tang, Margaret Taub, Kent D. Taylor, Matthew Taylor, Simeon Taylor, Marilyn Telen, Timothy A. Thornton, Machiko Threlkeld, Lesley Tinker, David Tirschwell, Sarah Tishkoff, Hemant Tiwari, Catherine Tong, Russell Tracy, Michael Tsai, Dhananjay Vaidya, David Van Den Berg, Peter VandeHaar, Scott Vrieze, Tarik Walker, Robert Wallace, Avram Walts, Fei Fei Wang, Heming Wang, Karol Watson, Daniel E. Weeks, Bruce Weir, Scott Weiss, Lu Chen Weng, Jennifer Wessel, Cristen Willer, Kayleen Williams, L. Keoki Williams, Carla Wilson, James Wilson, Quenna Wong, Joseph Wu, Huichun Xu, Lisa Yanek, Ivana Yang, Rongze Yang, Norann Zaghloul, Maryam Zekavat, Yingze Zhang, Snow Xueyan Zhao, Wei Zhao, Degui Zhi, Xiang Zhou, Xiaofeng Zhu, Michael Zody, and Sebastian Zoellner
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The RNA-seq data for SPIROMICS and SARP are deposited to dbGaP at accessions phs001119.v1.p1 and phs001446, respectively. While awaiting data release via dbGaP, investigators may contact the corresponding authors or the SPIROMICS and SARP studies at https://www.spiromics.org/spiromics/ and http://www.severeasthma.org/home.html to discuss gaining access to the data via the ancillary study mechanism for these studies. MAST RNA-seq data are available at Gene Expression Omnibus (GEO) (accession number GSE67472 [80]). TOPMed WGS freeze 9 data for the SPIROMICS cohort will be available at dbGaP under accession number phs001927.
Full eQTL summary statistics for the 496 COVID-19-related genes generated during the current study can be downloaded from the GitHub repository at https://github.com/LappalainenLab/spiromics-covid19-eqtl/tree/master/eqtl/summary_stats [81]. eQTL mapping analyses code has been deposited to the GitHub repository at https://github.com/LappalainenLab/spiromics-covid19-eqtl [82].