TY - JOUR
T1 - Chronic obstructive pulmonary disease and related phenotypes
T2 - polygenic risk scores in population-based and case-control cohorts
AU - Moll, Matthew
AU - Sakornsakolpat, Phuwanat
AU - Shrine, Nick
AU - Hobbs, Brian D.
AU - DeMeo, Dawn L.
AU - John, Catherine
AU - Guyatt, Anna L.
AU - McGeachie, Michael J.
AU - Gharib, Sina A.
AU - Obeidat, Ma'en
AU - Lahousse, Lies
AU - Wijnant, Sara R.A.
AU - Brusselle, Guy
AU - Meyers, Deborah A.
AU - Bleecker, Eugene R.
AU - Li, Xingnan
AU - Tal-Singer, Ruth
AU - Manichaikul, Ani
AU - Rich, Stephen S.
AU - Won, Sungho
AU - Kim, Woo Jin
AU - Do, Ah Ra
AU - Washko, George R.
AU - Barr, R. Graham
AU - Psaty, Bruce M.
AU - Bartz, Traci M.
AU - Hansel, Nadia N.
AU - Barnes, Kathleen
AU - Hokanson, John E.
AU - Crapo, James D.
AU - Lynch, David
AU - Bakke, Per
AU - Gulsvik, Amund
AU - Hall, Ian P.
AU - Wain, Louise
AU - Soler Artigas, María
AU - Jackson, Victoria E.
AU - Strachan, David P.
AU - Hui, Jennie
AU - James, Alan L.
AU - Kerr, Shona M.
AU - Polasek, Ozren
AU - Vitart, Veronique
AU - Marten, Jonathan
AU - Rudan, Igor
AU - Kähönen, Mika
AU - Surakka, Ida
AU - Gieger, Christian
AU - Karrasch, Stefan
AU - Rawal, Rajesh
AU - Schulz, Holger
AU - Deary, Ian J.
AU - Harris, Sarah E.
AU - Enroth, Stefan
AU - Gyllensten, Ulf
AU - Imboden, Medea
AU - Probst-Hensch, Nicole M.
AU - Lehtimäki, Terho
AU - Raitakari, Olli T.
AU - Langenberg, Claudia
AU - Luan, Jian'an
AU - Wareham, Nick
AU - Zhao, Jing Hua
AU - Hayward, Caroline
AU - Murray, Alison
AU - Porteous, David J.
AU - Smith, Blair H.
AU - Jarvelin, Marjo Riitta
AU - Wielscher, Matthias
AU - Joshi, Peter K.
AU - Kentistou, Katherine A.
AU - Timmers, Paul RHJ
AU - Wilson, James F.
AU - Cook, James P.
AU - Lind, Lars
AU - Mahajan, Anubha
AU - Morris, Andrew P.
AU - Ewert, Ralf
AU - Homuth, Georg
AU - Stubbe, Beate
AU - Weiss, Stefan
AU - Zeggini, Eleftheria
AU - Weiss, Scott T.
AU - Silverman, Edwin K.
AU - Dudbridge, Frank
AU - Tobin, Martin D.
AU - Cho, Michael H.
N1 - Funding Information:
DLD received grant support from Bayer and Novartis. SRAW reports grants from GlaxoSmithKline (GSK), outside of the submitted work. ERB has undertaken clinical trials through his employer, Wake Forest School of Medicine and University of Arizona, for AstraZeneca, MedImmune, Boehringer Ingelheim, Genentech, Novartis, Regeneron, and Sanofi Genzyme; and has served as a paid consultant for ALK-Abello, AstraZeneca, MedImmune, GSK, Novartis, Regeneron, Sanofi Genzyme, and Teva, outside of the submitted work. RT-S was an employee of GSK during this study and is a current shareholder of GSK. GRW reports that their spouse works for Biogen. BMP serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. NNH reports grants from the COPD Foundation, National Institutes of Health, and Boehringer Ingelheim, grants and personal fees from AstraZeneca and GSK, and personal fees from Mylan, outside of the submitted work. DL reports grants from the National Heart, Lung, and Blood Institute, and personal fees from Parexel, Veracyte, Boehringer Ingelheim, Genentech, and Acceleron, outside of the submitted work; and has a patent pending for “Systems and methods for classifying severity of COPD”. PB reports grants from GSK during the conduct of the study; and personal fees from GSK, Boehringer-Ingelheim, AstraZeneca, and Chiesi, outside of the submitted work. IPH reports grants from the National Institute of Health Research, during the conduct of the study; and grants from GSK and Boehringer Ingelheim, outside of the submitted work. EKS has received grant and travel support from GSK. MHC has received grant support from GSK, consulting fees from Genentech and AstraZeneca, and speaking fees from Illumina. All other authors declare no competing interests.
Funding Information:
MM is supported by NIH T32HL007427. BDH is supported by K08HL136928 and the Parker B Francis Research Opportunity Award. DLD is supported by PO1 HL 114501 and PO1 132825. ALG is funded by internal fellowships at the University of Leicester from the Wellcome Trust Institutional Strategic Support Fund (204801/Z/16/Z) and the BHF Accelerator Award (AA/18/3/34220). CJ holds a Medical Research Council Clinical Research Training Fellowship (MR/P00167X/1). MJM is supported by R01 HL139634. SRAW was funded by the Funds for Scientific Research Flanders (Fonds voor Wetenschappelijk Onderzoek Vlaanderen; grant number 3G037618). AM is supported by R01 HL131565. LW holds a GSK?British Lung Foundation Chair in Respiratory Research. EKS is supported by P01 HL114501, R01 HL133135, R01 HL137927, and R01 HL147148. MDT is supported by a Wellcome Trust Investigator Award (WT202849/Z/16/Z) and has been supported by the Medical Research Council (MRC; MR/N011317/1). MHC is supported by R01HL113264, R01HL137927, and R01HL135142. This research was conducted with use of the UK Biobank resource under application 20915. The UK Biobank genetic data were partially generated by Medical Research Council (MRC) strategic award to MDT, IPH, and LW (MC_PC_12010). The research was partially supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre. The views expressed in this Article are those of the authors and not necessarily those of any of the funding bodies. Additional funding information, including funding for individual studies, can be found in the appendix (pp 5?6).
Funding Information:
MM is supported by NIH T32HL007427. BDH is supported by K08HL136928 and the Parker B Francis Research Opportunity Award. DLD is supported by PO1 HL 114501 and PO1 132825. ALG is funded by internal fellowships at the University of Leicester from the Wellcome Trust Institutional Strategic Support Fund (204801/Z/16/Z) and the BHF Accelerator Award (AA/18/3/34220). CJ holds a Medical Research Council Clinical Research Training Fellowship (MR/P00167X/1). MJM is supported by R01 HL139634. SRAW was funded by the Funds for Scientific Research Flanders (Fonds voor Wetenschappelijk Onderzoek Vlaanderen; grant number 3G037618). AM is supported by R01 HL131565. LW holds a GSK–British Lung Foundation Chair in Respiratory Research. EKS is supported by P01 HL114501, R01 HL133135, R01 HL137927, and R01 HL147148. MDT is supported by a Wellcome Trust Investigator Award (WT202849/Z/16/Z) and has been supported by the Medical Research Council (MRC; MR/N011317/1). MHC is supported by R01HL113264, R01HL137927, and R01HL135142. This research was conducted with use of the UK Biobank resource under application 20915. The UK Biobank genetic data were partially generated by Medical Research Council (MRC) strategic award to MDT, IPH, and LW (MC_PC_12010). The research was partially supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre. The views expressed in this Article are those of the authors and not necessarily those of any of the funding bodies. Additional funding information, including funding for individual studies, can be found in the appendix (pp 5–6) .
Publisher Copyright:
© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2020/7/7
Y1 - 2020/7/7
N2 - Background: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV1 and FEV1/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV1/FVC <0·7 and FEV1 <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. Findings: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern. Interpretation: A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth. Funding: US National Institutes of Health, Wellcome Trust.
AB - Background: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV1 and FEV1/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV1/FVC <0·7 and FEV1 <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. Findings: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern. Interpretation: A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth. Funding: US National Institutes of Health, Wellcome Trust.
UR - http://www.scopus.com/inward/record.url?scp=85087427951&partnerID=8YFLogxK
U2 - 10.1016/S2213-2600(20)30101-6
DO - 10.1016/S2213-2600(20)30101-6
M3 - Article
C2 - 32649918
AN - SCOPUS:85087427951
SN - 2213-2600
VL - 8
SP - 696
EP - 708
JO - The Lancet Respiratory Medicine
JF - The Lancet Respiratory Medicine
IS - 7
ER -