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. Author manuscript; available in PMC: 2023 Jun 5.
Published in final edited form as: Eur Respir J. 2017 Nov 30;50(5):1700657. doi: 10.1183/13993003.00657-2017

Surfactant Protein D is a Causal Risk Factor for Chronic Obstructive Pulmonary Disease: Results of Mendelian Randomization

Ma’en Obeidat 1, Xuan Li 1, Stephen Burgess 2, Guohai Zhou 1, Nick Fishbane 1, Nadia N Hansel 3, Yohan Bossé 4,5, Philippe Joubert 4, Ke Hao 6, David C Nickle 7, Maarten van den Berge 8, Wim Timens 9, Michael H Cho 10,11, Brian D Hobbs 10,11, Kim de Jong 12, Marike Boezen 12, Rayjean Hung 13, Nicholas Rafaels 14, Rasika Mathias 15, Ingo Ruczinski 16, Terri H Beaty 17, Kathleen C Barnes 14, Peter D Paré 1, Don D Sin, International COPD Genetics Consortium, Lung eQTL Consortium, and Lung Health Study1,✉
PMCID: PMC7614619  EMSID: EMS176558  PMID: 29191953

Abstract

Background

Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death worldwide. Surfactant protein D (SP-D) is a protein produced primarily in the lung and is involved in regulating pulmonary surfactants, maintaining lipid homeostasis in the lung and promoting innate immunity to protect the lungs from microbial and chemical insults. COPD is associated with reduced levels of SP-D in bronchial and bronchoalveolar lavage (BAL) fluid, as well as increased serum SP-D levels. It remains uncertain whether SP-D is a biomarker of COPD or is part of the causal pathway leading to COPD.

Methods

The Lung Health Study (LHS) is a longitudinal multicenter study of mild-to-moderate COPD subjects, who were followed over 11 years. In year five, SP-D serum levels were measured in 4,061 individuals who also had whole genome genotypes. To identify genetic variants that act as protein quantitative trait loci (pQTLs) for serum SP-D levels, a genome-wide association study (GWAS) for SP-D levels in 4,041 individuals from LHS was undertaken using 1000 genome imputed genotypes and adjusting for age, sex, body mass index (BMI), and genetic principal components. The identified pQTLs were additionally tested for association with variation in lung tissue expression of the SP-D encoding gene; SFTPD in 1,037 individuals and genes co-expression patterns were also utilized to uncover genes whose expression strongly correlate with SFTPD. Finally, a Mendelian randomization (MR) framework was applied whereby SP-D pQTLs were tested for association with COPD risk in large GWAS meta-analysis of COPD (11,157 cases and 36,699 controls) from the International COPD Genetic Consortium (ICGC) and with longitudinal decline of FEV1 over 11 years in individuals from LHS.

Results

Three regions on chromosomes 6, 10 and 16 achieved genome-wide significance (P< 5x10-8) as pQTLs for serum SP-D levels. The most significantly associated pQTL was rs61860415 in the SFTPD gene on chromosome 10 (P=6.34E-92). The sentinel SNP in the second associated locus was rs116688442 in the HLA region on chromosome 6 (P=2.24E-54). The third locus harboured rs62048789 in the ATP2C2 gene on chromosome 16 (P=2.61E-17). Conditional analysis uncovered an additional three independent SNPs exceeding genome-wide significance on chromosome 10. Variants in the SFTPD locus were also associated with lung tissue expression of SFTPD and the SNPs’ effect on serum protein levels was significantly related to their effect on mRNA expression in lung. The MR analysis revealed that SNPs associated with increased serum SP-D levels decreased the risk of COPD (estimate = -0.13, P=4.06 x 10-5) in the ICGC COPD GWAS results. In the LHS, MR analysis revealed those SNPs that increased serum SP-D levels also slowed the FEV1 decline over 11 years (estimate = 0.0018, P=5.38 x 10-3).

Interpretation

The integration of genetic variation effect on serum levels, lung tissue expression and disease phenotypes provided novel insights into SP-D biology and established a novel causal link between increased SP-D levels and protection against COPD risk and accelerated FEV1 decline. SP-D represents a very promising biomarker and therapeutic target for COPD which warrants further study.

Funding

Funding for the SP-D measurements was provided by the Canadian Institutes of Health Research (CIHR). Genotyping for the LHS samples was provided by the Gene, Environment Association Studies grant U01HG004738. The gene expression and genotyping for the lung eQTL study was funded by Merck.

Introduction

Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death worldwide 1. The disease is characterized by airway obstruction that is not fully reversible, and is caused by a complex interaction between genetic and environmental risk factors (e.g. smoking)2. Although one third of variability in lung function can be attributed to genetic factors, genes directly controlling risk to COPD remain largely unknown3.

A critical class of molecules involved in lung growth and homeostasis are surfactants. One surfactant that has been associated with COPD, even in the absence of cigarette smoke, is surfactant protein D (SP-D)4. SP-D is a large multimeric collagenous glycoprotein weighing ~43 kDa and is part of the collectin family of proteins5. SP-D is produced primarily by type II alveolar cells, as well as club cells6,7. SP-D plays an important role in regulating pulmonary surfactants, maintaining lipid homeostasis in the lung8 and promoting innate immunity 9 to protect the lungs from microbial and chemical insults 10. SP-D deficiency in mice leads to alveolar macrophage activation, increased oxidant stress in the airways and emphysematous changes in lung parenchyma by 3 weeks of age.11 In humans, single nucleotide polymorphisms (SNPs) in the surfactant protein D (SFTPD) gene on chromosome 10 have been associated with COPD in several different cohorts12. The most well studied SFTPD variant (rs721917) is a coding SNP, which results in the substitution of methionine for threonine at amino acid 11 (Met11Thr)13. This SNP is associated with SP-D serum levels and emphysema14. COPD is associated with reduced levels of SP-D in bronchial and bronchoalveolar lavage (BAL) fluid15, as well as increased serum SP-D levels16. Despite these associations it remains uncertain whether SP-D is a biomarker of COPD or is part of the causal pathway leading to COPD.

One way of evaluating causality in biomarker-disease associations is to use Mendelian randomization (MR), which utilizes genetic variation to ascertain causal associations17–19. MR exploits two unique attributes of genotypes enabling robust causal interrogation: 1) the random allocation of parental alleles to zygotes at meiosis, independent of environmental exposures later in life, and 2) the unidirectional flow of biological information from genes to transcript to protein, thus avoiding reverse causation20. Within the MR framework, if the relationship between SP-D and COPD is truly causal, the genetic variant(s) influencing serum SP-D levels should also be significantly associated with COPD risk and/or FEV1 decline over time.

In the present study, we unravelled the genetic architecture of SP-D serum levels in a large cohort of COPD patients to determine SP-D protein and mRNA expression quantitative trait loci (pQTL and eQTL) and used MR to demonstrate a causal relationship between serum SP-D and two important phenotypes: COPD risk and FEV1 decline over time.

Methods

Overall study design

The overall study design is depicted in Figure 1. Firstly, we identified protein quantitative trait loci (pQTLs) for SP-D using a genome wide association study (GWAS) of serum SP-D levels in COPD patients, who participated in the Lung Health Study (LHS) 21. Secondly, using data from the Lung eQTL Consortium22, we determined which of these pQTLs were also associated with changes in gene expression (mRNA) and potentially represented expression trait quantitative loci (eQTLs) in lung tissue. We also used gene expression data from lung tissues to identify networks of genes strongly related to SFTPD. Thirdly, we determined which of the SP-D pQTLs were associated with risk to COPD, cross sectional lung function and COPD progression defined by the rate of decline in FEV1 over 11 years. Finally, we applied a MR framework to determine whether there was a causal relationship between serum SP-D levels and COPD risk or progression.

Figure 1. Study design.

Figure 1

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Cohorts

Lung Health Study (LHS)

The details of the LHS have been published previously21, 23. Briefly, LHS was a multicenter clinical study that evaluated the effects of ipratropium bromide, a short acting antimuscarinic agent, and smoking cessation on lung function decline in current smokers with mild to moderate COPD. For the first 5 years, the lung function of participants was measured annually, followed by another lung function measurement at year 11. At each visit the subjects’ smoking status was determined using a questionnaire, which was validated by salivary cotinine and exhaled carbon monoxide levels as previously described 21. Based on these data, the subjects were categorized as “sustained quitters” if they were non-smokers at all of the follow-up visits, “continuous smokers” if they were smoking at all of the follow-up visits and “intermittent quitters” if their smoking status varied during the follow-up period. In year 5 of LHS, venipuncture was carried out on 5,413 LHS participantswho were alive and eligible for venipuncture at this visit. The blood samples were taken when participants were stable and free of exacerbations for at least 4 weeks and were separated into buffy coat and serum24.

Genotyping

From the buffy coat samples of 4,251 European Americans in LHS, SNP genotyping was performed. The details of genotyping and quality control have been previously described25. Briefly, samples were genotyped using the Illumina Human660WQuad v.1_A BeadChip. Overall, 98.4 % of samples (n = 4,181) passed initial quality control standards and genotypes were available for 559,766 SNPs. An additional 133 samples were removed because they failed quality control, which resulted in a final sample of n = 4,048 for the present analysis. Imputation was undertaken with the software IMPUTE2 26 using the ‘all ancestries’ 1000 Genomes Project (1000G) reference panel, March 2012 release27. Variants were excluded if the imputation r2 was < 0.3.

Serum SP-D measurements in LHS

Using one aliquot of serum samples, SP-D levels were measured in duplicate in 4,754 LHS participants using a commercially available solid phase sandwich enzyme-linked immunosorbent assay according to manufacturer’s instructions (ELISA; BioVendor Laboratory Medicine, Modrice, Czech Republic) by study personnel, who were blinded to the subject’s clinical or genotyping information. The lower limit of detection for the assay was 0.2 ng/mL, and the coefficient of variation was 3.9%28.

Protein quantitative trait loci (pQTL) of serum SP-D: GWAS of SP-D serum levels

We performed a genome-wide association study for log serum SP-D levels using SNPTEST29 assuming an additive genetic model and adjusting for age, sex, body mass index (BMI), smoking status (sustained quitters versus intermittent quitters versus continuous smokers) and the first 5 genetic principal components (PCs). The final GWAS dataset included 4,041 subjects. SP-D pQTLs were defined as the sentinel SNPs meeting genome-wide significance (P<5x10-8).

For each of the genome-wide significant pQTL loci identified, we performed conditional analysis to reveal independent SNPs in these regions associated with SP-D levels. For each locus, we regressed log SP-D levels against the most significant SNP along with age, sex, BMI, and smoking status to obtain regression residuals. With these residuals, a second genetic association was performed, adjusting for the first 5 genetic PCs. The residual genetic association was repeated while conditioning on the sentinel SNP yielding genome-wide significance until all significant SNPs for SP-D levels (defined by P<5x10-8) were exhausted at each locus. Using this approach, we identified 3 additional independent SNPs on the chr10 locus. The other regions (chr6 and 16) did not harbour any additional loci that were independently associated with SP-D levels.

A recent large GWAS meta-analysis evaluating SNPs associated with COPD 30 in 15,256 COPD cases and 47,936 controls identified a coding SNP in the SFTPD gene, rs721917 (Met11Thr), which was significantly associated with risk of COPD (P=2.5E-08 in the overall meta-analysis which included an additional 9,498 COPD cases and 9,748 controls from the UK BiLEVE study31. This SNP effect on serum SP-D was investigated in our GWAS results and it was also included it all subsequent analyses.

Association of SP-D pQTLs with lung tissue expression in the Lung eQTL study

Because lung tissue samples were not collected in LHS participants, we used data from the lung eQTL study to identify potential lung eQTLs for SFTPD. The study details and the subjects’ characteristics have been previously described22. Briefly, lung eQTLs were derived from a meta-analysis of genotyping and RNA expression data performed in non-tumor lung tissue samples from 1,111 patients who underwent lung resection surgery at three participating sites: University of British Columbia (UBC; n=339), Laval University (n=409) and the University of Groningen (n=363). Gene expression profiling was performed using an Affymetrix custom array (GPL10379) testing 51,627 non-control probe sets where normalization was performed using robust multi-array average (RMA)32. The expression data are available at NCBI Gene Expression Omnibus repository (GEO, http://www.ncbi.nlm.nih.gov/geo) through accession number GSE23546.

Genotyping was performed on DNA extracted from blood or lung tissue using the Illumina Human1M-Duo BeadChip array, and imputed with MaCH/Minimac software33 using the 1000G reference panel, March 2012 release. The eQTL analysis was adjusted for age, sex and smoking status. The resulting eQTLs were categorized into cis-acting (if the signal was less than 1Mb away from transcription start site) or trans eQTLs (if the signal was further than 1Mb away or on a different chromosome). Following standard microarray and genotyping quality control steps, association testing for each variant with mRNA expression in either cis (SNP within 1Mb of transcript) or trans (more than 1Mb away or on a different chromosome) position was undertaken separately for each centre and then pooled together using inverse variance weighting meta-analysis.

All genome-wide significant pQTLs for SP-D levels were tested for association with lung tissue mRNA levels (i.e. lung eQTLs). Scatter plots were used to visualize the SNPs effect on both SP-D serum levels and lung mRNA.

Association of SP-D pQTLs with COPD risk and progression

The relationship of SNPs identified as pQTLs for serum SP-D with COPD risk was investigated in the International COPD Genetics Consortium (ICGC) Study. ICGC is the largest COPD GWAS to date, and included a total of 15,256 COPD cases and 47,936 controls30. The look-up in ICGC was performed in GWAS results for individuals with European ancestry with a sample size of 11,157 cases and 36,699 controls.

SP-D pQTLs were also tested for association with FEV1 decline over the 11 years of follow-up in the LHS using a multiple linear mixed effects model which takes into account multiple measurements of FEV1 over time within the same individual. These pQTLs were additionally tested for association with cross sectional FEV1 and FEV1/FVC in the LHS cohort using linear regression. The analysis of FEV1 decline in LHS used SNP dosage as a predictor and the (time x genotype) interaction as the response variable, while the analysis of cross sectional measures used FEV1 and FEV1/FVC as the response variables. All analyses in LHS were adjusted for age, sex, BMI, and smoking status as noted previously.

Mendelian randomization (MR) analysis

The independent pQTLs for SP-D identified in this study (including the coding SNP) were used as genetic instruments in MR analyses. The instrumental variables linear regression analysis of the SP-D pQTLs effect on outcome (COPD risk and lung function phenotypes) versus the SNPs’ allelic effect on observed SP-D serum levels was weighted by the inverse variance of the outcome effect estimate, and constrained (forced to pass through the origin) 34.

Weighted gene co-expression analysis (WGCNA)

The WGCNA R package 35 was used to cluster modules of co-expressed genes into a co-expression network. The expression values from 1,037 lung tissue samples were used in this analysis. A weighted gene co-expression network reconstruction algorithm was used to create a consensus co-expression network among the 18,403 genes that were common to all three centres 36. WGCNA first creates a matrix of Pearson correlations across all genes, which is then transformed into an adjacency matrix through ‘soft thresholding’ by raising these correlations to some power β. In this study β =10 was selected to enable the matrix to approximate scale-free topology, thus creating a topological overlap matrix (TOM) 37. Average linkage hierarchical clustering was used to group genes based on their topological overlap of connectivity, followed by a dynamic cut-tree algorithm to cluster dendrogram branches into distinct gene modules 38. Modules were defined as groups of highly interconnected genes. For each gene, Module Membership (MM) was calculated with values ranging between 0 and 1 by correlating the gene’s expression with the module eigengene, which was determined by the first principal component of the gene expression profile in that module. We deemed “hub” genes as those with the highest MM. The generated TOM values in this WGCNA estimate the strength of connection between the different genes in a network39 40, and were used to identify genes strongly correlated to SFTPD.

Statistical analysis software

All analyses were performed with R version 3.2.1

Role of the funding source

The funding sources have no roles in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Results

Descriptive demographics of LHS Participants

The demographics of LHS participants across the quintiles of serum SP-D levels are shown in Table 1. Serum SP-D levels were significantly related to increasing age, decreasing BMI and FEV1, and smoking status (with continuous smokers having higher average serum SP-D levels than sustained quitters).

Table 1. Demographic and clinical characteristics of subjects with genotypes across quintiles of serum surfactant protein D (SP-D) in the Lung Health Study.

Quint 1 (n=786) Quint 2 (n=792) Quint 3 (n=812) Quint 4 (n=822) Quint 5 (n=829) P value
SP-D, ng/ml ≤59.7 59.7 to 81.3 81.3 to 105.4 105.4 to 139.5 >139.5 --
Age, years 52.1±6.8 52.8±6.8 54.0±6.6 53.8±6.6 55.1±6.3 <.0001
Men, % 63.6% 65.5% 61.6% 62.2% 63.1% 0.524
BMI, kg/m2 27.9±4.3 27.4±4.4 26.8±4.3 26.4±4.2 26.0±4.2 <.0001
Smokers, %† 70.9% 78.7% 82.9% 87.2% 92.5% <.0001
FEV1, % Predicted 78.5±11.6 75.7±12.0 74.9±12.0 74.6±11.9 73.3±12.2 <.0001
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†

Either continuous or intermittent smokers, defined as smokers (verified objectively using salivary cotinine or exhaled carbon monoxide) at the time of at least in one annual follow-up visit. All values are for the year 5 values because this was the visit during which blood samples were collected. For age, BMI, FEV1, the values are mean±SD. P value is generated using the analysis of variance (ANOVA) test for continuous variables and a chi square test for categorical variables.

Genome-wide association study of serum SP-D levels

Variants with effective sample sizes (N effective, computed as the product of sample size and imputation quality summed across studies) < 70% were filtered out, leaving 9,355,308 variants in this GWAS analysis. Quantile–quantile (QQ) plots are presented in Supplementary Figure 1 and showed a deviation from the expected distribution only for low p-values indicating strong association signals. The genomic inflation factor (lambda) was 1.02, suggesting no systematic deviation in the association statistics due to factors such as population structure.

A total of 1,933 SNPs representing three regions on chromosomes 6, 10 and 16 achieved genome-wide significance (P< 5x10-8) as described previously in Kim et al. 41. Manhattan plot is shown in Figure 2 and the region plots are shown in Figure 3. The most significantly associated pQTL was an intronic SNP (rs61860415) in the SFTPD gene (P=6.34E-92). The sentinel SNP in the second associated locus was an intergenic SNP (rs116688442) in the HLA region on chromosome 6 (P=2.24E-54). The third locus harboured the intronic SNP (rs62048789) in the ATP2C2 gene on chromosome 16 (P=2.61E-17).

Figure 2. SP-D GWAS Manhattan plot.

Figure 2

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Manhattan plot shows the P values (-log10 scale) on the Y axis and the SNP positions across 22 autosomal chromosomes on the X axis. The horizontal red line represents the genome-wide cut-off of 5x10-08.

Figure 3. Region plots of the SP-D pQTLs loci.

Figure 3

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The Y axis represent the P values in the (–log10 scale) and the X axis is the genomic position. Gene names and their corresponding coordinates are shown below. The sentinel SNP is shown as a purple diamond and the color coding of SNPs reflects the degree of linkage disequilibrium (LD) with the sentinel SNP using 1000G reference.

To identify additional independent signals, association testing was repeated using residuals from the sentinel SNP in the GWAS model. The analysis of these residuals on chromosome 10 identified an additional 3 independent signals exceeding genome-wide significance (Supplementary Figure 2). The regions on chromosomes 6 and 16 did not harbour any additional independent loci significant at the P<5x10-8 threshold. All pQTL sentinel SNPs meeting the genome-wide association threshold for serum SP-D in these three regions are presented in Table 2. SNP rs721917; recently associated with COPD in the ICGC study30 was a pQTL for serum SP-D (P=7.5x10-12). This SNP had a modest linkage disequilibrium (LD) with the other sentinel SNPs on chr 10 region ranging from r2=0.16 with SNP rs80271335 to r2=0.53 with SNP rs12358676. The association of this SNP with SP-D serum levels is reported in Table 2 and it is also included in all the subsequent analyses.

Table 2. The protein expression trait loci (pQTL) for serum surfactant protein D (SP-D).

SNP Chr Position (hg19) Coded allele/refe rence allele MAF Imputati on r2 beta SE P value % Variance
rs116688442 6 31347214 A / G 0.055 0.998 0.709 0.046 2.24E-54 6.013
rs61860415 10 81730579 C / T 0.176 0.992 -0.570 0.028 6.34E-92 9.056
rs80271335 10 81723739 C / T 0.100 0.976 -0.691 0.036 3.26E-84 8.330
rs144420336 10 81716293 A / G 0.063 0.918 0.571 0.045 2.37E-37 3.677
rs12358676 10 81755644 G / T 0.464 0.987 0.185 0.021 3.87E-19 1.787
rs721917 10 81706324 G / A 0.422 1 -0.143 0.021 7.50E-12 1.071
rs62048789 16 84406620 A / G 0.135 0.967 -0.259 0.031 2.61E-17 1.680
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Chr: chromosome. Coded allele: the allele used to derive the statistics. MAF: minor allele frequency. Imputation r2: imputation quality control metrics. SE: standard error. % variance is the variance explained by that particular SNP. Beta, SE and P value for Chr10 pQTLs are from the SP-D pQTL GWAS.

The effect of SP-D pQTLs on lung mRNA expression (eQTLs)

Given that SP-D is predominantly synthesized in the lung, the three loci harbouring pQTLs for serum SP-D were evaluated in lung tissue to determine whether these regions also acted as an expression quantitative trait loci (eQTLs) for lung tissue mRNA22. The top eQTL results (based on the lowest eQTL P-value per gene) for each of the loci are shown in Table 3. The pQTL SNPs in the SFTPD locus on chr10 were also lung eQTLs for SFTPD. and the SNPs’ effect on serum protein levels was significantly related to their effect on mRNA expression in lung tissue (Figure 4). Together, these data suggest SNPs in the SFTPD gene region affect serum SP-D by altering the levels of gene expression in lung tissue. In contrast, the pQTL SNPs in chr6 and chr16 were not related to SFTPD mRNA levels in the lung, indicating they affect serum levels through other mechanisms (Supplementary Figure 3).

Table 3. Lung tissue eQTL results for serum SP-D pQTLs.

Chr SNP Position (b37) Alleles Z.Laval Z.Groningen Z.UBC Lung eQTL value Gene Symbol SP-D GWAS P value
6 rs9380221 31150873 T C -15.8 -17.8 -13.7 3.87E-163 PSORS1C3 1.28E-08
10 rs12267802 81749610 A G 2.9 1.7 3.4 3.75E-06 SFTPD 2.22E-31
16 rs12445532 84404091 G A -9.3 -4.2 -3.7 4.53E-27 ATP2C2 4.30E-11
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Chr: chromosome. Alleles: the SNP alleles, the first letter is the coded/effective allele. Lung eQTL P value: is the eQTL meta-analysis p value from UBC, Groningen, and Laval studies. Z.Laval: the eQTL Z score in the in Laval cohort. Z.UBC: the Z score in the in UBC cohort. Z.Groningen: the Z score in the in Groningen cohort. Gene Symbol: The gene that is regulated by the eQTL. PSORS1C3: psoriasis susceptibility 1 candidate 3, SFTPD: surfactant protein D, ATP2C2: ATPase, Ca + + transporting, type 2C, member 2.

Figure 4. SP-D serum pQTLs on chr10 as lung eQTLs.

Figure 4

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Plotted are all the genome-wide significant SNPs for serum SP-D levels. The Y axis is the effect estimates of the SNPs on serum SP-D levels. The X axis is their effect estimates on lung tissue mRNA levels of SFTPD. SNPs in red are ones that have a significant effect on mRNA levels using a nominal P value of P<0.05. The graph shows SNPs associated with increased serum levels in the GWAS are also associated with increases the lung mRNA expression and vice versa.

Association of SP-D pQTLs with COPD and lung function

The 7 pQTL SNPs for serum SP-D levels (including the coding SNP), were tested for association with COPD risk and progression (defined by observed FEV1 decline over 11 years). The association results with COPD risk were extracted from the ICGC meta-analysis. Thesethe LHS. The association results of SP-D pQTLs were additionally tested for association with FEV1 decline over 11 years in participants in the LHS.

Additional testing was also performed for cross sectional FEV1 and FEV1/FVC measures in pQTL SNPs with COPD phenotypes and lung function are summarized in Table 4. Interestingly, the coding SNP (rs721917) in the SFTPD gene was significantly associated with all phenotypes studied (P<0.05) and showed the lowest P-values of all tested SNPs.

Table 4. Association SP-D pQTLs with COPD and related phenotypes.

Phenotype rs116688442 (chr6) estimate (P value) (alleles) rs12358676 (chr10) estimate (P value) (alleles) rs144420336 (chr10) estimate (P value) (alleles) rs61860415 (chr10) estimate (P value) (alleles) rs62048789 (chr16) estimate (P value) (alleles) rs721917 (chr10) estimate (P value) (alleles) rs80271335 (chr10) estimate (P value) (alleles)
LHS FEV1 decline N = ~3,800 -0.00203 (p = 0.0678) (G_A) 0.000852 (p = 0.0863) (T_G) -0.00274 (p = 0.0102) (G_A) 0.000572 (p = 0.396) (T_C) 0.000633 (p = 0.387) (G_A) 0.00117 (p = 0.0187) (A_G) 0.000765 (p = 0.37) (T_C)
LHS FEV1 Y5 N= ~3,800 0.00395 (p = 0.836) (G_A) 0.0164 (p = 0.0567) (T_G) -0.0312 (p = 0.09) (G_A) 0.0114 (p = 0.327) (T_C) -0.0153 (p = 0.23) (G_A) 0.0216 (p = 0.0127) (A_G) 0.0216 (p = 0.144) (T_C)
LHS FEV1FVC Y5 N= ~3,800 0.331 (p = 0.381) (G_A) 0.331 (p = 0.0528) (T_G) -0.462 (p = 0.205) (G_A) 0.366 (p = 0.113) (T_C) -0.00906 (p = 0.971) (G_A) 0.484 (p = 0.00495) (A_G) 0.617 (p = 0.0349) (T_C)
ICGC COPD 11,157 cases / 36,699 controls* NA -0.0326 (p = 0.128) (T_G) -0.0709 (p = 0.172) (A_G) -0.0713 (p = 0.012) (T_C) 0.039 (p = 0.229) (A_G) -0.0828 (p = 8.87e-05) (A_G) -0.0879 (p = 0.0161) (T_C)
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LHS: Lung Health Study. ICGC: International COPD Genetics Consortium. FEV1 decline was estimated using Linear mixed effect. For each study, the alleles shown are the coded_noncoded allele. Estimates and P values are computed for the coded allele. P values <0.05 is are shown in bold.

*

COPD results are reported for meta-analysis across the 17 ICGC European ancestry cohorts with genome-wide data available.

Mendelian randomization (MR) analyses

The combined SNPs allelic effects on serum SP-D levels and risk of COPD and FEV1 decline were evaluated using MR. In the ICGC cohort, MR analysis revealed that SNPs associated with increased serum SP-D levels decreased the risk of COPD in a dose-dependent manner (estimate = -0.13 change in COPD log odds ratio for every unit increase in log SP-D, P=4.06 x 10-5; Figure 5A). In the LHS, MR analysis revealed those SNPs that increased serum SP-D levels also increased FEV1/FVC ratio (estimate=0.46 increase in FEV1/FVC per unit increase in log SP-D, P=0.037; Supplementary Figure 4) and slowed the FEV1 decline over 11 years (estimate = 0.0018 less decline in FEV1 slope per unit increase in log SP-D, P=5.38 x 10-3; Figure 5B), all in a dose-dependent manner (i.e. a greater SNP effect on observed serum SP-D level was associated with a greater protection against COPD and less FEV1 decline over time).

Figure 5. Association of individual SNPs with SP-D serum levels and COPD risk and progression.

Figure 5

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A) Estimates of SP-D on COPD risk, and B) estimates of SP-D on FEV1 decline. Estimates are derived from serum GWAS for SP-D and from ICGC and LHS GWAS for COPD risk and FEV1 decline, respectively. Error bars represent 95% CIs. The SNPs rs numbers are shown. The slope of the line is the instrumental variable regression estimate of the effect of SP-D on COPD risk and FEV1 decline.

WGCNA and SFTPD co-expression in lung tissue

To further understand the biology of SP-D, we searched for genes that were strongly correlated with SFTPD gene expression in lung tissue. Using the consensus network approach, we identified 38 distinct modules in the expression data on lung tissue, which consisted of strongly co-expressed genes. The module containing the SFTPD gene included 364 different genes which were enriched for pathways related to lipid, cholesterol and fatty acids metabolic and biosynthesis processes (Supplementary Table 1). This network construction additionally allowed us to identify ‘hub’ genes, which, by definition, had the highest module membership value for a given module. The hub gene in the module containing SFTPD was surfactant associated 3 (SFTA3). The top 50 genes with the strongest correlation with SFTPD are shown in Supplementary Figure 5. Genes strongly co-expressed with SFPD included CRTAC1, CACNA2D2, and S100.

Discussion

Despite its high prevalence and impact, there are currently no biomarkers or disease modifying therapies for COPD. Establishing causality for selected proteins and pathways is one promising step toward their development as both biomarkers and therapeutic targets.

In this study, we determined the effect of genetic variation on SP-D protein levels in serum, mRNA levels in lung tissue and disease phenotypes to identify a potential causal role for SP-D in the etiology of COPD. We identified 6 independent loci that were significantly associated with serum SP-D levels, including four independent SNPs at the SFTPD locus on chr10, plus two distal regions of the genome (in the HLA locus on chr6 and the ATP2C2 gene region on chr16). Importantly, using a lung tissue-specific eQTL dataset, we showed the effects of the SNPs in the SFTPD gene on serum SP-D is likely by modulating SFTPD mRNA levels in the lung. The genetic signal on chr16, on the other hand, appears to be related to expression of the ATP2C2 gene and thus its effect on serum SP-D is likely indirect. Most importantly, using MR, we showed SNPs associated with higher serum SP-D levels seem to impart protection against COPD and FEV1 decline over time. Together these data suggest a causal role of SP-D in the pathogenesis of COPD.

A previous study by Kim et al. identified the three loci that were pQTLs for SP-D on chr6, 10 and 1641. A more recent study by Sun et al. additionally identified SNP rs2146192 in the SFTPD gene region as associated with SP-D blood levels in COPD subjects42. This SNP is in modest LD with the top SP-D pQTL in our study (r2=0.53). Using conditional analyses, Sun et al. additionally identified two independent pQTL SNPs; one on chr10 and the other in the HLA region on chr6. In the current study, we confirm and extend the findings of Kim et al. and Sun et al. We replicated the same 3 genes from Kim et al. and identified several additional SNPs in the SFTPD, HLA and ATP2C2 regions act as pQTLs for SP-D levels. In total, the 3 genes explained 30% of the variation in serum SP-D levels. The strongest pQTLs for SP-D in this study were in the SFTPD gene encoding the SP-D protein, suggesting serum levels are driven in part by SP-D production in lung tissue. Consistent with this notion, we observed SNPs’ estimated effect on SFTPD mRNA levels in lung tissue appears significantly related to their effect on serum SP-D levels. In addition, there were no distal genetic determinants of SFTPD expression in lung tissue suggesting the SP-D serum pQTLs on chr10 captured most of the regulatory effects on SFTPD production in the lung.

The identification of distal pQTLs may provide important insights into SP-D biology. The chr16 locus contains the ATPase secretory pathway Ca2+ transporting 2 (ATP2C2) gene, which is expressed in many tissues (including the lungs) and functions as a transporter of Ca2+ ions43. SNPs in the ATP2C2 region had no effect on SFTPD mRNA levels in lungs, however, mRNA levels of ATP2C2 were positively correlated with SFTPD mRNA expression (r=0.2, P= 6.9X10-11). Together, these data suggest ATP2C2 may regulate serum SP-D through an indirect mechanism such as post translational modification, transport, and/or clearance, among others or through a shared functional pathway.

In lung tissue, SFTPD was mapped to a module of 364 strongly co-expressed genes which were enriched for pathways related to lipid and cholesterol biosynthesis. Surfactant associated 3 (SFTA3) was the ‘hub’ gene and its expression was positively related to SFTPD. SFTA3 is a recently characterised immunoregulatory protein in the lung, and may play an important role in inflammation and immune defence44. Other genes that were strongly co-expressed with SFTPD included cartilage acidic protein 1 (CRTAC1), the calcium channel, voltage-dependent, alpha 2/delta subunit 2 (CACNA2D2), and S100 calcium binding protein A14 (S100A14) genes. CRTAC1 is up-regulated (>10 fold) in type 2 epithelial cells as they differentiate in culture45. Previous studies have shown binding of SP-D to phosphatidylinositol46, TLR2 and TLR447, and carbohydrate structures on the surface of bacteria, viruses, and fungi is calcium dependent48–53. Calcium may also cause SP-D oligomers to self-aggregate, as has been shown previously with SPA54. Further work is needed to unravel the mechanisms underlying calcium related gene associations with SP-D gene expression and serum levels.

In the LHS study, there was a modest negative correlation between serum SP-D levels and FEV1 (r=-0.063, P<1x10-4), consistent with several other studies49, 50. Superficially these relationships appear to be paradoxical to the main findings of the present study (opposite direction of association). However, cross-sectional relationships between lung function and serum SP-D have to be interpreted cautiously because of the possibility of reverse causation (i.e. pathological processes which contribute to COPD causing increased leakage of SP-D from lungs into the systemic circulation) and confounding (e.g. smoking). For instance, we and others have previously shown serum SP-D is affected not only by lung synthesis of SP-D but also by permeability of the alveolar-capillary interface, which is perturbed in COPD and also increased by active smoking and lung inflammation55, 56. Genetic analyses avoid most of these pitfalls since the causal pathway between genetic variants and protein and disease is unidirectional. In the present study, MR results supported a significant protective role for SP-D on both risk of COPD and FEV1 decline.

One of the important considerations in MR studies is the selection of appropriate genetic variants to infer causality of biomarker-disease associations. We used a hypothesis-free GWAS to identify local and distal pQTLs for serum SP-D levels, and in addition included the coding SNP rs721917 given its association with COPD risk in the ICGC study. The functional consequences of this protein coding variants are not clear, but it was associated with serum SP-D levels in the present study. The SNP may also affect the multimeric structure of SP-D, thereby disrupting the protein’s host defence and immune functions by reducing its the binding affinity to pathogen ligands or the antioxidant properties.

There is a huge unmet clinical need to identify novel biomarkers and therapeutic targets for COPD. SP-D represents an excellent candidate since: 1) it is a lung specific protein that can be reliably assayed in blood, BAL and lung tissue; 2) it tracks well with disease severity and exacerbations57; 3) it is responsive to steroid treatment 57 ; and 4) it has inherent functional and biological attributes suggesting a role in COPD pathogenesis10. Moreover, this study is the first to our knowledge to report a novel causal link between genetically elevated SP-D serum levels and both decreased risk for COPD and FEV1 decline.

The current study has a number of limitations. First, LHS study participants were all smokers with mild or moderate COPD at the time of recruitment, and this may have affected the serum measures of SP-D as shown by the discordance between genetic and observational analyses. Second, the serum and lung mRNA expression were measured in different individuals, which clearly prevents direct comparisons between the two sites. However, the use of genetic variation provided a robust method to estimate and extrapolate the findings for mRNA and protein between lung tissue and blood. Third, SP-D was detected in serum using ELISA, which may not differentiate between intact (dodecameric) and monomeric or trimeric forms of SP-D. Further quantitative studies of different multimeric SP-D protein structures are warranted.

In conclusion, the current study investigated the effect of genetic variation on serum SP-D levels, lung tissue gene expression and COPD risk and progression. The findings provided novel insights into SP-D biology and establish a novel causal link between increased SP-D levels and protection against COPD risk and accelerated FEV1 decline. SP-D represents a very promising biomarker and therapeutic target for COPD which warrants further study.

Supplementary Material

Supplementary material

Acknowledgements

Ma’en Obeidat is a Postdoctoral Fellow of the Michael Smith Foundation for Health Research (MSFHR) and the Canadian Institute for Health Research (CIHR) Integrated and Mentored Pulmonary and Cardiovascular Training program (IMPACT). He is also a recipient of British Columbia Lung Association Research Grant.

Ke Hao is partially supported by National Natural Science Foundation of China grant no. 21477087.

Michael Cho is recipient of NIH grant R01 HL113264, and he also received grant support from GlaxoSmithKline (GSK).

Footnotes

Authors’ Contributions

Conceived and designed the study: MO, PDP, DDS

Lung eQTL data collection and analysis: KH, DCN, MVB, WT, YB, PJ

International COPD Genetics Consortium (ICGC) GWAS data collection and analysis: MHC, BDD, KDJ, MB

Lung Health Study (LHS) data analyses: MO, XL, GZ, NF, NH, THB, NR, RM, IR, KCB,

Statistical support and advice on study conduct: RH, SB,

Wrote the manuscript: MO, PDP, DDS

Discussed results and implications and commented on the manuscript at all stages: all co-authors.

Competing financial interests

David C. Nickle is employed by Merck & Co. Inc.

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