Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

SPIROMICS Research Group, COPDGene Investigators

Research output: Contribution to journalArticle

20 Citations (Scopus)

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.

Original languageEnglish
Article numbere1006011
JournalPLoS Genetics
Volume12
Issue number8
DOIs
Publication statusPublished - 1 Aug 2016

Fingerprint

Genetic Polymorphisms
Chronic Obstructive Pulmonary Disease
single nucleotide polymorphism
Single Nucleotide Polymorphism
biomarker
biomarkers
polymorphism
Biomarkers
blood
genetic polymorphism
Emphysema
protein
quantitative trait loci
blood groups
Blood Group Antigens
air flow
proteins
airflow
phenotype
Proteins

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics
  • Genetics(clinical)
  • Cancer Research

Cite this

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD. / SPIROMICS Research Group; COPDGene Investigators.

In: PLoS Genetics, Vol. 12, No. 8, e1006011, 01.08.2016.

Research output: Contribution to journalArticle

SPIROMICS Research Group & COPDGene Investigators 2016, 'Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD', PLoS Genetics, vol. 12, no. 8, e1006011. https://doi.org/10.1371/journal.pgen.1006011
SPIROMICS Research Group ; COPDGene Investigators. / Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD. In: PLoS Genetics. 2016 ; Vol. 12, No. 8.
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title = "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.",
author = "{SPIROMICS Research Group} and {COPDGene Investigators} and Wei Sun and Katerina Kechris and Sean Jacobson and Drummond, {M. Bradley} and Hawkins, {Gregory A.} and Jenny Yang and Chen, {Ting Huei} and Quibrera, {Pedro Miguel} and Wayne Anderson and Barr, {R. Graham} and Basta, {Patricia V.} and Bleecker, {Eugene R.} and Terri Beaty and Richard Casaburi and Peter Castaldi and Cho, {Michael H.} and Alejandro Comellas and Crapo, {James D.} and Gerard Criner and Dawn Demeo and Christenson, {Stephanie A.} and Couper, {David J.} and Curtis, {Jeffrey L.} and Doerschuk, {Claire M.} and Freeman, {Christine M.} and Gouskova, {Natalia A.} and Han, {Mei Lan K.} and Hanania, {Nicola A.} and Hansel, {Nadia N.} and Hersh, {Craig P.} and Hoffman, {Eric A.} and Kaner, {Robert J.} and Kanner, {Richard E.} and Kleerup, {Eric C.} and Sharon Lutz and Martinez, {Fernando J.} and Meyers, {Deborah A.} and Peters, {Stephen P.} and Regan, {Elizabeth A.} and Rennard, {Stephen I.} and Scholand, {Mary Beth} and Silverman, {Edwin K.} and Woodruff, {Prescott G.} and O’Neal, {Wanda K.} and Bowler, {Russell P.} and Alexis, {Neil E.} and Boucher, {Richard C.} and Carretta, {Elizabeth E.} and Comellas, {Alejandro P.} and Ronald Crystal",
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T1 - Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

AU - SPIROMICS Research Group

AU - COPDGene Investigators

AU - Sun, Wei

AU - Kechris, Katerina

AU - Jacobson, Sean

AU - Drummond, M. Bradley

AU - Hawkins, Gregory A.

AU - Yang, Jenny

AU - Chen, Ting Huei

AU - Quibrera, Pedro Miguel

AU - Anderson, Wayne

AU - Barr, R. Graham

AU - Basta, Patricia V.

AU - Bleecker, Eugene R.

AU - Beaty, Terri

AU - Casaburi, Richard

AU - Castaldi, Peter

AU - Cho, Michael H.

AU - Comellas, Alejandro

AU - Crapo, James D.

AU - Criner, Gerard

AU - Demeo, Dawn

AU - Christenson, Stephanie A.

AU - Couper, David J.

AU - Curtis, Jeffrey L.

AU - Doerschuk, Claire M.

AU - Freeman, Christine M.

AU - Gouskova, Natalia A.

AU - Han, Mei Lan K.

AU - Hanania, Nicola A.

AU - Hansel, Nadia N.

AU - Hersh, Craig P.

AU - Hoffman, Eric A.

AU - Kaner, Robert J.

AU - Kanner, Richard E.

AU - Kleerup, Eric C.

AU - Lutz, Sharon

AU - Martinez, Fernando J.

AU - Meyers, Deborah A.

AU - Peters, Stephen P.

AU - Regan, Elizabeth A.

AU - Rennard, Stephen I.

AU - Scholand, Mary Beth

AU - Silverman, Edwin K.

AU - Woodruff, Prescott G.

AU - O’Neal, Wanda K.

AU - Bowler, Russell P.

AU - Alexis, Neil E.

AU - Boucher, Richard C.

AU - Carretta, Elizabeth E.

AU - Comellas, Alejandro P.

AU - Crystal, Ronald

PY - 2016/8/1

Y1 - 2016/8/1

N2 - 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.

AB - 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.

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