Integrative genomics identifies new genes associated with severe COPD and emphysema

Chronic obstructive pulmonary disease (COPD) is characterized by irreversible airflow obstruction and is strongly influenced by genetic factors [1, 2]. Genome-wide association studies of COPD and related traits (e.g., emphysema) have revealed multiple genetic loci associated with disease risk [3‐5]. Most loci identified by genome-wide association studies (GWAS) are regulatory, and do not directly alter the amino acid sequence.

Gene expression is arguably the most impactful and well-studied effect of regulatory genetic variation. GWAS loci are enriched for expression quantitative trait loci (eQTL), rendering it a potential link between genetic variant and biology of disease [6, 7]. The efforts of large cohort studies and consortia such as the Genotype-Tissue Expression Project have discovered thousands of genetic variants associated with gene expression in multiple tissues. While most GWAS studies do not concomitantly measure gene expression, the strong relationship of genetic variation to gene expression allows one to use gene expression reference datasets to predict gene expression given a set of genotypes, and subsequently identify gene expression differences for a given phenotype. This approach has been implemented in software called S-PrediXcan and TWAS [8‐10]. Aggregating information from variant level to infer gene-level associations increases the power to discover more genes at loci not previously implicated by GWAS and gives mechanistic insight regarding genes being regulated via disease-associated genetic variants [7, 11].

Despite the convention of naming a discovered locus for the nearest gene (e.g., HHIP), further study is needed to identify the specific gene(s) and variant(s) responsible for disease risk [9, 11, 12]. In identified COPD susceptibility loci, most loci contain multiple genes, and variants in these genes are correlated (in linkage disequilibrium). More than one gene in a locus may also play a role in disease pathogenesis, as seen in other complex diseases [13, 14]. With recently developed methods and a growing amount of gene expression data made publicly available, integrating GWAS with known functional annotations of each variant (e.g., associated with gene expression) could highlight novel and biologically relevant genes for further evaluation.

We hypothesized that application of these integrative methods to specific phenotypes of COPD (severe disease and quantitative emphysema) would facilitate discovery of new gene-disease associations and elucidate the mechanism of gene in existing susceptibility loci. Specifically, we sought to identify genes and pathways genetically up- or down-regulated by phenotype-associated variants in tissue-specific reference datasets using S-PrediXcan and TWAS [3, 5], and to assess the potential causality of individual genes using multi-variant colocalization.¹

Genome-wide association studies have arguably become the mainstay of identifying genetic risk factors for complex disease. However, these studies cannot identify which gene(s) in the region is responsible for the association, and testing all variants individually and independently is likely suboptimal. Here, we used an integrative method that combines the genetic component of gene expression with genetic association analysis in severe COPD and quantitative emphysema to predict differentially expressed genes. Importantly, this method focuses on the association of genetic component of gene expression, not gene expression as a whole, as is typical in most gene expression studies. We also provided additional support of our results by examining results in a second gene expression dataset, and performing colocalization analysis that attempts to identify whether association signals for gene expression and a phenotype of interest appear to be driven by the same causal variant(s). We implicated genes that are genetically regulated in known COPD-susceptibility loci, such as FAM13A, and also found genes in regions that were not previously reported: WNT3 for severe COPD, and DCBLD1 and LILRA3 for quantitative emphysema.

We found a novel association of WNT3 in lung tissue with severe COPD in two gene expression datasets. Although variants surrounding this gene in the 17q21 locus were not genome-wide significant in our COPD analysis GWAS (Fig. 3), the top signal (rs9912530) is in strong LD with variants previously reported in GWAS of FEV₁ [18, 19], interstitial lung disease [20], and idiopathic pulmonary fibrosis [21] (r² with these previously described variants, 0.55–0.72). WNT3 (Wnt family member 3) encodes Wnt3, a critical component of the Wnt-beta-catenin-TCF signaling pathway [22] and a required signal for the apical ectodermal ridge in limb patterning [23]. Deficient WNT3 is associated with tetra-amelia syndrome, a Mendelian disease characterized by an absence of all limbs. The top signal is also in strong LD with variants associated with various complex diseases such as Parkinson’s disease and celiac disease (r² 0.72–0.79) [24, 25]. Previous expression studies of small airway epithelium found that this gene, along with its Wnt signaling companions, was down-regulated in smokers compared with nonsmokers [26]. Of interest, FAM13A, a well-supported COPD susceptibility gene, has been involved in the Beta-catenin/Wnt signaling pathway by protein degradation [27]. While there is substantial interest in Wnt signaling in lung disease [28], the contribution of WNT3 to the pathogenesis of COPD requires further investigation. To address whether these findings were specific for severe COPD, we repeated the analysis including moderate disease (GOLD 2). All of our genes were at least nominally significant, though overall the significance of our findings was attenuated (Additional file 1: Table S4).

For emphysema, we identified novel associations of LILRA3 and DCBLD1 using whole blood and lung tissue, respectively, and validated these findings in additional gene expression datasets. LILRA3 (leukocyte immunoglobulin like receptor A3) is a gene encoding a soluble receptor for class I major histocompatibility complex (MHC) antigens expressed in monocytes and B cells, which is located in the 19q13 locus. Our top hit from GWAS in this locus, was not genome-wide significant (rs384116 with P = 1.88 × 10^− 5; Additional file 1: Figure S1), and 13-Mb away from the previously reported locus [16] that contains EGLN2 and RAB4B (rs7937; r² 0.002). It is in modest LD with variants suggestively associated with FEV₁/FVC [18] (r² 0.44), in strong LD with variants genome-wide significantly associated with HDL-C level [29] and prostate cancer [30] (r² 0.92–0.99). Blood may be the most relevant tissue for this gene, as it is preferentially expressed [31] with a high estimate of heritability of gene expression in whole blood [32]. However, it may also have an effect in other tissues, given its broad eQTL effects identified by multi-tissue eQTL analysis [33]. This was supported by the suggestive signals of this gene using lung tissue in S-PrediXcan analysis (P = 7.71 × 10^− 5 in GTEx-Lung and 1.38 × 10^− 4 in the Lung-eQTL Consortium with the same direction of effect). Nonetheless, its functional role in COPD has not been described previously. Our other novel association identified in lung tissue, DCBLD1 (discoidin, CUB and LCCL domain containing 1), located in the 6q22 locus, is an integral component of cell membranes and binds to oligosaccharides [34]. GWAS signals in this locus are also sub-genome wide significant (Additional file 1: Figure S2). Our top GWAS variant at this locus was in LD with variants associated with lung cancer [35] (r² 0.54).

In addition to novel associations, our study also provides insight into disease-associated genes in known COPD susceptibility loci. We identified six genes (FAM13A, GPRIN3, HYKK, PSMA4, EGLN2, and RAB4B) in three known COPD-susceptibility loci for which their genetic component of gene expression in blood or in lung tissue is associated with severe COPD. Five of these six genes are not the most proximal to the top associated SNP, a phenomenon previously observed in other genetic association studies [36, 37]. These findings underscore the complexity of genetic regulation in tissues and also identify multiple potential effector genes in the same locus. For example, in 15q25, PSMA4, and not CHRNA3 (the nearest gene to the top GWAS hit) was highlighted in S-PrediXcan and colocalization analysis. Although a role for IREB2 has been clearly demonstrated [38], our study suggested that other genes in the locus, particularly PSMA4 – a gene encoded for subunit of proteasome complex that acts in the proteolytic pathway [39], may also be of biologic importance.

At the 4q22 locus, an association for FAM13A identified using DGN-Blood was not validated in the GTEx-blood dataset. However, a significant but directionally opposite association was identified in the Lung-eQTL consortium dataset. To further explore this phenomenon, we examined individual SNP eQTL data from the Framingham Heart Study (FHS) blood, and the lung tissue from the Lung eQTL consortium (Additional file 1: Supplementary Methods). We confirmed that SNPs have opposite directions of effect in lung and blood (Additional file 1: Figure S3 and S4). This finding is consistent with prior reports describing significant and opposite tissue specific effects of eQTLs [33, 40, 41]. The interpretation of this phenomenon is not clear, but may be a result of pleiotropic effects of FAM13A [42, 43]. Of note, a recent analysis of emphysema-related gene expression in blood and lung tissue [44] found that the expression of genes in two tissues are often opposite; together, our findings highlight the tissue-specific genetic regulation of genes in COPD susceptibility loci. At the 19q13 locus, while both EGLN2 and RAB4B were successfully validated, only GWAS and eQTL signals for EGLN2 colocalized. This genetic locus was associated with COPD [16] and smoking behavior [45]. Although the causal gene(s) in this region is unclear, methylation and expression studies support the role of EGLN2 in this region [46]. EGLN2 (egl-9 family hypoxia inducible factor 2) encodes an enzyme that regulate the degradation of alpha subunit of hypoxia inducible factor (HIF) [47]. Gene and protein expression of HIF-1α is reduced in lung tissue samples from COPD patients [48].

Although ATF6B (activating transcription factor 6 beta) and ITGA1 (integrin subunit alpha 1) were not successfully validated, we cannot rule out the possibility of false negatives due to differences between the transcriptome datasets used for validation, and they are potentially interesting candidates for COPD. ATF6B was implicated in the unfolded protein response (UPR) pathway during endoplasmic reticulum (ER) stress following cigarette smoke, and may contribute to lung inflammation in patients with COPD [49], while integrins were found to be involved in COPD through the mitogen-activated protein kinase (MAPK) pathway [50, 51]. This region also harbors variants associated with FEV₁/FVC [52]. Decreased expression of ITGA1 was observed in the small airways of patients with low FEV₁ [53].

Our analysis assesses only the genetic component of gene expression. We also investigated whether these genes were differentially expressed in COPD patients, in 464 blood samples from the COPDGene study [54], and 151 lung tissue samples [55] (Additional file 1: Supplementary Methods and Table S5-S8). These genes were not differentially expressed, with the exception of LILRA3, which was nominally significant with %LAA-950 (P = 0.03). Given that the genetic component of gene expression was replicated, we believe that the genetic findings are robust, and speculate that these null findings could be due to non-genetic (i.e. environmental) perturbations that may occur downstream, or as a result of the genetic effects. In fact, in several cases measurements of mRNA or protein are actually opposite those predicted by genetic risk. For example, SERPINA1 risk alleles result in decreased levels and increased risk for COPD, yet average, alpha-1 levels in patients with COPD are actually elevated. Similarly, genetic variants in AGER and DSP affect transcript or protein levels opposite than what is measured in disease [4, 56, 57]. The mechanisms underlying our genetic findings, as well as AGER and DSP, that result in null or opposite direction effects requires further experimental investigation.

In addition to examination of individual loci, we applied pathway enrichment analysis to nominally significant differentially expressed genes in severe COPD and quantitative emphysema both in whole blood and lung tissue. This analysis identified enrichment of the T cell receptor signaling pathway in emphysema. This finding is consistent with reports that found antigen-specific T cell differentiation in lungs of patients with severe emphysema [58]. Our analysis using gProfileR does not assess of direction of effect, and the relative up- or down-regulation of specific genes in this pathway makes determination of direction difficult. To attempt to infer direction, we used Gene Set Enrichment Analysis (GSEA; [59]). In these results, the TCR signaling pathway and downstream TCR response were up-regulated, though these results were not statistically significant (Additional file 1: Table S9). Further study will be needed to determine the combined effects of COPD genetic susceptibility variants on T cell function and whether these explain some immune dysfunction seen in COPD [60, 61]. The finding of the enrichment of genes in the proteasome core complex further suggested a role of proteasome in COPD as described previously. Somewhat surprisingly, we observed enrichment of the asthma pathway in KEGG using genes identified in quantitative emphysema. This finding complements the description of substantial genetic correlation of COPD and asthma [4], and the presence of quantitative emphysema (or lung hyperinflation) in asthmatic patients [62].

Our study did not identify associations of genetically regulated differential expression of genes at some previously reported GWAS loci. Moreover, some of our identified associations in our discovery dataset were not successfully validated in a second transcriptome dataset. These findings indicate some of the limitations of our approach. First, as S-PrediXcan uses cis genetic variants as predictors for gene expression, variants that have lesser or no effect on transcript abundance or act in trans would not be detected by this approach [63]. Second, although most genetic variants implicated by GWAS are likely regulatory, only a minority of genetic loci are explained by existing eQTLs [64]. This may be due to lack of data in the appropriate tissue, cell type, or biologic conditions; or the heterogeneity of gene expression studies of bulk tissue. We may overcome these issues as more gene expression datasets and newer techniques such as single-cell gene expression profiling [65] become widely available. Moreover, issues such as cell type composition, sample collection methods, disease status, and differences in analytic methods also made the overlapping analysis challenging. Third, the number of genes available for an analysis depends on the power and sample size of the expression data used in constructing a gene expression prediction model [8, 9]. Given the noisy and condition-specific nature of gene expression datasets, variants with small effects on gene expression may be undetectable at the sample sizes available. Additionally, the difference in sample size among transcriptome databases decreases our power to validate or discover more genes.

However, despite technical and population differences, most cis-eQTLs appear to be consistent between studies [66]. Therefore, despite in some cases a modest value of overall coefficient of correlation between predicted and measured gene expression, associations of the genetic component of gene expression as inferred by imputed gene expression have been successfully in identifying disease-associated genes that complement existing methods.

In conclusion, we found that genetic determinants of gene expression were associated with severe COPD and quantitative emphysema phenotypes, identifying genes at known loci, and identifying novel COPD-associated genes. These findings were obtained by integrating GWAS results with gene expression data, performing colocalization analysis, and validating key results in independent gene expression datasets. These findings may provide mechanistic insights into the genetics of COPD.