Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

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 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

BNP and obesity in acute decompensated heart failure with preserved vs. reduced ejection fraction: The atherosclerosis risk in communities surveillance study

Background: Levels of B-type natriuretic peptide (BNP), a prognostic marker in patients with heart failure (HF), are lower among HF patients with obesity or preserved Left Ventricular Ejection Fraction (LVEF). We examined the distribution and prognostic value of BNP across BMI categories in acute decompensated heart failure (ADHF) patients with preserved vs. reduced LVEF. Methods: We analyzed data from the Atherosclerosis Risk in Communities (ARIC) HF surveillance study which sampled and adjudicated ADHF hospitalizations in patients aged ≥55years from 4 US communities (2005-2009). We examined 5 BMI categories: underweight (\u3c18.5kg/m2), normal weight (18.5-\u3c25), overweight (25-\u3c30), obese (30-\u3c40) and morbidly obese (≥40) in HF with preserved LVEF (HFpEF) and reduced LVEF (HFrEF). The outcome was 1-year mortality from admission. We used ANCOVA to model log BNP and logistic regression for 1-year mortality, both adjusted for demographics and clinical characteristics. Results: The cohort included 9820 weighted ADHF hospitalizations (58% HFrEF; 42% HFpEF). BNP levels were lower in HFpEF compared to HFrEF (p\u3c0.001) and decreased as BMI increased within the LVEF groups (p\u3c0.001). After adjustment for covariates, log10 BNP independently predicted 1-year mortality (adjusted OR 1.62 (95% CI 1.17-2.24)) with no significant interaction by BMI or LVEF groups. Conclusions: BNP levels correlated inversely with BMI, and were higher in HFrEF compared to HFpEF. Obese patients with HFpEF and ADHF had a significant proportion with BNP levels below clinically accepted thresholds. Nevertheless, BNP was a predictor of mortality in ADHF across groups of BMI in HFpEF and HFrE

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

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 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

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD.

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

Summary of pQTLs by measured biomarker.

<p>Summary of pQTLs by measured biomarker.</p

Clinical and biologic significance of pQTL SNPs.

<p>(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 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006011#pgen.1006011.s008" target="_blank">S8 Table</a>. 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.</p

Consequences of pQTL SNPs.

<p>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.</p

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

<p>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 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006011#pgen.1006011.t001" target="_blank">Table 1</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006011#pgen.1006011.s002" target="_blank">S2 Table</a>.</p

SNPs in <i>ABO</i> are the strongly associated with many blood biomarker levels as well as other non-blood analyte measurements.

<p>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 <i>ABO</i> 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 (<i>trans</i>) 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 [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006011#pgen.1006011.ref010" target="_blank">10</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006011#pgen.1006011.ref035" target="_blank">35</a>–<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006011#pgen.1006011.ref073" target="_blank">73</a>]).</p

Demographics of participants by cohort at study entry^*.

<p>Demographics of participants by cohort at study entry<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006011#t001fn001" target="_blank">^*</a>.</p