Matthew Moll1,2, Adel Boueiz1,2, Auyon J Ghosh1,2, Aabida Saferali1, Sool Lee1,3, Zhonghui Xu1, Jeong H Yun1,2, Brian D Hobbs1,2, Craig P Hersh1,2, Don D Sin4,5, Ruth Tal-Singer6, Edwin K Silverman1,2, Michael H Cho1,2, Peter J Castaldi1,7. 1. Channing Division of Network Medicine. 2. Division of Pulmonary and Critical Care Medicine, and. 3. Department of Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina. 4. Centre for Heart Lung Innovation, St. Paul's Hospital, Vancouver, British Columbia, Canada. 5. Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; and. 6. COPD Foundation, Washington, DC. 7. Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Abstract
Rationale: The ability of peripheral blood biomarkers to assess chronic obstructive pulmonary disease (COPD) risk and progression is unknown. Genetics and gene expression may capture important aspects of COPD-related biology that predict disease activity. Objectives: Develop a transcriptional risk score (TRS) for COPD and assess the contribution of the TRS and a polygenic risk score (PRS) for disease susceptibility and progression. Methods: We randomly split 2,569 COPDGene (Genetic Epidemiology of COPD) participants with whole-blood RNA sequencing into training (n = 1,945) and testing (n = 624) samples and used 468 ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points) COPD cases with microarray data for replication. We developed a TRS using penalized regression (least absolute shrinkage and selection operator) to model FEV1/FVC and studied the predictive value of TRS for COPD (Global Initiative for Chronic Obstructive Lung Disease 2-4), prospective FEV1 change (ml/yr), and additional COPD-related traits. We adjusted for potential confounders, including age and smoking. We evaluated the predictive performance of the TRS in the context of a previously derived PRS and clinical factors. Measurements and Main Results: The TRS included 147 transcripts and was associated with COPD (odds ratio, 3.3; 95% confidence interval [CI], 2.4-4.5; P < 0.001), FEV1 change (β, -17 ml/yr; 95% CI, -28 to -6.6; P = 0.002), and other COPD-related traits. In ECLIPSE cases, we replicated the association with FEV1 change (β, -8.2; 95% CI, -15 to -1; P = 0.025) and the majority of other COPD-related traits. Models including PRS, TRS, and clinical factors were more predictive of COPD (area under the receiver operator characteristic curve, 0.84) and annualized FEV1 change compared with models with one risk score or clinical factors alone. Conclusions: Blood transcriptomics can improve prediction of COPD and lung function decline when added to a PRS and clinical risk factors.
Rationale: The ability of peripheral blood biomarkers to assess chronic obstructive pulmonary disease (COPD) risk and progression is unknown. Genetics and gene expression may capture important aspects of COPD-related biology that predict disease activity. Objectives: Develop a transcriptional risk score (TRS) for COPD and assess the contribution of the TRS and a polygenic risk score (PRS) for disease susceptibility and progression. Methods: We randomly split 2,569 COPDGene (Genetic Epidemiology of COPD) participants with whole-blood RNA sequencing into training (n = 1,945) and testing (n = 624) samples and used 468 ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points) COPD cases with microarray data for replication. We developed a TRS using penalized regression (least absolute shrinkage and selection operator) to model FEV1/FVC and studied the predictive value of TRS for COPD (Global Initiative for Chronic Obstructive Lung Disease 2-4), prospective FEV1 change (ml/yr), and additional COPD-related traits. We adjusted for potential confounders, including age and smoking. We evaluated the predictive performance of the TRS in the context of a previously derived PRS and clinical factors. Measurements and Main Results: The TRS included 147 transcripts and was associated with COPD (odds ratio, 3.3; 95% confidence interval [CI], 2.4-4.5; P < 0.001), FEV1 change (β, -17 ml/yr; 95% CI, -28 to -6.6; P = 0.002), and other COPD-related traits. In ECLIPSE cases, we replicated the association with FEV1 change (β, -8.2; 95% CI, -15 to -1; P = 0.025) and the majority of other COPD-related traits. Models including PRS, TRS, and clinical factors were more predictive of COPD (area under the receiver operator characteristic curve, 0.84) and annualized FEV1 change compared with models with one risk score or clinical factors alone. Conclusions: Blood transcriptomics can improve prediction of COPD and lung function decline when added to a PRS and clinical risk factors.
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Authors: Matthew Moll; Phuwanat Sakornsakolpat; Nick Shrine; Brian D Hobbs; Dawn L DeMeo; Catherine John; Anna L Guyatt; Michael J McGeachie; Sina A Gharib; Ma'en Obeidat; Lies Lahousse; Sara R A Wijnant; Guy Brusselle; Deborah A Meyers; Eugene R Bleecker; Xingnan Li; Ruth Tal-Singer; Ani Manichaikul; Stephen S Rich; Sungho Won; Woo Jin Kim; Ah Ra Do; George R Washko; R Graham Barr; Bruce M Psaty; Traci M Bartz; Nadia N Hansel; Kathleen Barnes; John E Hokanson; James D Crapo; David Lynch; Per Bakke; Amund Gulsvik; Ian P Hall; Louise Wain; Scott T Weiss; Edwin K Silverman; Frank Dudbridge; Martin D Tobin; Michael H Cho Journal: Lancet Respir Med Date: 2020-07 Impact factor: 102.642