Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Abstract

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10-10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10-392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.

Fig 1. Comparison of–log10 p values for pQTL SNPs in SPIROMICS and COPDGene cohorts.

The–log₁₀(P value) of each pQTL SNP is plotted on the x-axis for COPDGene and the y-axis for SPIROMICS. 164 of 182 significant pQTLs in COPDGene were replicated in SPIROMICS at a P < (0.05/182 to correct for multiple tests). 209 of 290 significant pQTLs in SPIROMICS were replicated in COPDGene at a P < (0.05/290 to correct for multiple tests). See also S3 Table.

Fig 2. Genome wide associations between single nucleotide polymorphisms (SNPs) and blood biomarkers.

Combined Manhattan plots show pQTL SNPs by chromosomal location for 38 biomarkers with at least one SNP significant at genome wide significance after adjustment for multiple testing (red line). The -10logP values are shown using results from a meta-analysis of both SPIROMICS and COPDGene SNPs. The abbreviation for the biomarker associated with the pQTL SNP can be found in S1 Table.

Fig 3. SNPs in ABO are the strongly associated with many blood biomarker levels as well as other non-blood analyte measurements.

Trans pQTLs in the ABO region (shown in schematic form below the plots) were common in this study (top panel) and in published studies (GWAS Catalog). The rs687289 or rs507666 SNPs in the ABO blood group locus on chromosome 9, which encodes alpha 1-3-N-acetylgalactosaminyltransferase, are a major determinant of ABO blood type. In this study, these SNPs were the strongest pQTLs for 6 blood biomarkers, all distant (trans) from their biomarker genes. Other biologic features (such as clotting time), metabolites (proteins, lipids, hormones), and urinary features have been noted to have strong association with this locus (see [, –73]).

Fig 4. Consequences of pQTL SNPs.

We examined the most significant SNP for each biomarker (top pQTL) and all 590 significant pQTL SNPs (all pQTLs), compared to all 664,913 SNPs (all SNPs) used for testing with the Ensemble Variant Effect Predictor (release 78). Upstream refers to within 5 kb and downstream refers to more than 5kb distant. All pQTL SNPs were enriched for missense, synonymous, upstream and 3′ UTR variants compared to all SNPs tested on the genotyping platform, while pQTL SNPs occurred less frequently in introns and intergenic regions (binomial test p-value < 0.05 starred in blue). Most of these variant classes showed additional enrichment or reduction for the top pQTL SNPs.

Fig 5. Blood biomarker variance explained by top two pQTLs SNPs and clinical covariates.

The percent variation for 39 blood biomarkers explained by clinical (green) top pQTL SNP (red), second top independent pQTL SNP (peach), other unknown factors (grey). Clinical factors include age, gender, body mass index, smoking status, and principal components of ancestral genetic markers as described in the methods. The analysis includes subjects from SPIROMICS (S) and COPDGene (C) cohorts. TNRF (TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL-R3)); PCAM (Platelet endothelial cell adhesion molecule (PECAM-1)); SRP1 (Alpha-1-Antitrypsin (alpha-1 (AAT)); NRC (Neuronal Cell Adhesion Molecule (Nr-CAM)); SPK (Pancreatic secretory trypsin inhibitor (TATI)); SRT1 (Sortilin); other abbreviations are listed in Table 1 and S2 Table.

Fig 6. Examples of biomarker pQTL SNPs.

Plasma levels of IL6R (A) and E-selectin (B) are strongly influenced by pQTL SNPs (P = 10⁻¹⁹³ and P = 4 X 10⁻¹⁰⁴). The pQTL SNP for IL6R is on chromosome 4, which is local (cis) to IL6R, the gene coding for its protein. The pQTL SNP for E-selectin protein is on chromosome 9, which is distant (trans) from SELE (chromosome 1), the gene coding for its protein. This pQTL SNP is in the ABO locus, which encodes alpha 1-3-N-acetylgalactosaminyltransferase.

Fig 7. Circos plot showing distant (trans) pQTLs and their relationships to eQTL SNPs.

The arrows in the inner circle represent pQTL SNPs significantly associated (beginning of arrow) with biomarker (end of arrow). Biomarker abbreviations (see text for full list) are shown on the outer ring. Local (cis) pQTL SNPs are shown as hash marks adjacent to biomarker gene location. The color of represents significance of association. Red lines are associations between genes. The thinnest, darkest red lines signify associations with significance of P = 10⁻¹², and the lines become slightly thicker and darker at the significance levels of P = 10⁻¹⁰ and P = 10⁻⁸. The green lines signify eQTL associations. The cutoffs for line thickness and darkness for the green lines are P = 10⁻⁷ and P = 10⁻⁶. The only eQTL association with significance P < 10⁻⁸ were local, near the HP and PECAM1 genes.

Fig 8. Clinical and biologic significance of pQTL SNPs.

(A) Biomarker pQTL SNPs were tested for association with four different COPD disease phenotypes: emphysema, airflow limitation (FEV₁%), chronic bronchitis, and exacerbations using four different statistical regression models to infer the causal relations of causal, reactive, independent, complete or collide. A complete listing of pQTL SNPs disease association p-values for both cohorts can be found in S8 Table. Note that testing b₂ = 0 and b₄ = 0 are equivalent because in both cases, we are testing whether the disease and biomarker is conditionally dependent given SNP. Therefore, we only examined b2 in our analysis. No significant results were obtained for chronic bronchitis or exacerbations and so these two phenotypes are not shown. (B) The T allele of rs2070600 is associated with lower plasma levels of sRAGE and (C) lower plasma levels of sRAGE (shown by sRAGE quartile) are associated with more emphysema on quantitative CT scan (model 0); (D) the T allele is not clearly associated with emphysema when considering only the SNP-disease association (model 1); however, (E) the T allele is associated with less emphysema within each biomarker quartile (model 2), and the SNP fits the collide model.