Relative contributions of family history and a polygenic risk score on COPD and related outcomes: COPDGene and ECLIPSE studies

Matthew Moll, Sharon M Lutz, Auyon J Ghosh, Phuwanat Sakornsakolpat, Craig P Hersh, Terri H Beaty, Frank Dudbridge, Martin D Tobin, Murray A Mittleman, Edwin K Silverman, Brian D Hobbs, Michael H Cho, Matthew Moll, Sharon M Lutz, Auyon J Ghosh, Phuwanat Sakornsakolpat, Craig P Hersh, Terri H Beaty, Frank Dudbridge, Martin D Tobin, Murray A Mittleman, Edwin K Silverman, Brian D Hobbs, Michael H Cho

Abstract

Introduction: Family history is a risk factor for chronic obstructive pulmonary disease (COPD). We previously developed a COPD risk score from genome-wide genetic markers (Polygenic Risk Score, PRS). Whether the PRS and family history provide complementary or redundant information for predicting COPD and related outcomes is unknown.

Methods: We assessed the predictive capacity of family history and PRS on COPD and COPD-related outcomes in non-Hispanic white (NHW) and African American (AA) subjects from COPDGene and ECLIPSE studies. We also performed interaction and mediation analyses.

Results: In COPDGene, family history and PRS were significantly associated with COPD in a single model (PFamHx <0.0001; PPRS<0.0001). Similar trends were seen in ECLIPSE. The area under the receiver operator characteristic curve for a model containing family history and PRS was significantly higher than a model with PRS (p=0.00035) in NHWs and a model with family history (p<0.0001) alone in NHWs and AAs. Both family history and PRS were significantly associated with measures of quantitative emphysema and airway thickness. There was a weakly positive interaction between family history and the PRS under the additive, but not multiplicative scale in NHWs (relative excess risk due to interaction=0.48, p=0.04). Mediation analyses found that a significant proportion of the effect of family history on COPD was mediated through PRS in NHWs (16.5%, 95% CI 9.4% to 24.3%), but not AAs.

Conclusion: Family history and the PRS provide complementary information for predicting COPD and related outcomes. Future studies can address the impact of obtaining both measures in clinical practice.

Trial registration: ClinicalTrials.gov NCT00292552 NCT00608764.

Keywords: COPD epidemiology; emphysema; tobacco and the lung.

Conflict of interest statement

Competing interests: EKS received grant support from GlaxoSmithKline and Bayer. MHC has received grant support from GlaxoSmithKline and Bayer, consulting fees from Genentech and AstraZeneca, and speaking fees from Illumina. CPH reports grant support from Boehringer-Ingelheim, Novartis, Bayer and Vertex, outside of this study. MDT receives grant support from GlaxoSmithKline and Orion.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.

Figures

Figure 1
Figure 1
Schematic of study design. COPD, chronic obstructive pulmonary disease; PRS, Polygenic Risk Score. FamHx = family history of COPD. PRS = polygenic risk score. C = covariates.
Figure 2
Figure 2
(A) AUC analysis in COPDGene NHWs: Predictive performance (AUC) of three logistic regression models for the discrimination of outcomes shown on the x-axis. The PRS was analysed as a continuous variable. For each outcome, three models were trained: model 1 (outcome ~ family history + age + sex + pack-years), model 2 (outcome ~ PRS + age + sex + pack-years + principal components of genetic ancestry) and model 3 (outcome ~ family history + PRS + age + sex + pack-years + principal components of genetic ancestry). B) AUC analysis in COPDGene AAs. Abbreviations are listed in the caption for table 3. P values comparing model AUCs are shown in online supplemental table S8 and were considered significant if less than Bonferroni-corrected level of significance (p

Figure 3

Meta-analyses of binary outcomes with…

Figure 3

Meta-analyses of binary outcomes with PRS treated as a continuous variable. COPDGene and…

Figure 3
Meta-analyses of binary outcomes with PRS treated as a continuous variable. COPDGene and ECLIPSE studies were meta-analysed, and fixed effects odds ratios with 95% CIs are shown for family history and PRS for each outcome. ORs for the PRS indicate the odds ratio for the listed outcome for every standard deviation increase in the PRS. The Bonferroni-corrected level of significance is 0.05/11 = 0.0045 (includes seven continuous outcomes shown in supplement). Abbreviations are listed in the caption of table 3.
Figure 3
Figure 3
Meta-analyses of binary outcomes with PRS treated as a continuous variable. COPDGene and ECLIPSE studies were meta-analysed, and fixed effects odds ratios with 95% CIs are shown for family history and PRS for each outcome. ORs for the PRS indicate the odds ratio for the listed outcome for every standard deviation increase in the PRS. The Bonferroni-corrected level of significance is 0.05/11 = 0.0045 (includes seven continuous outcomes shown in supplement). Abbreviations are listed in the caption of table 3.

References

    1. Vogelmeier CF, Criner GJ, Martinez FJ, et al. . Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report. gold executive summary. Am J Respir Crit Care Med 2017;195:557–82. 10.1164/rccm.201701-0218PP
    1. Fletcher C, Peto R, Tinker CSF. The natural history of chronic bronchitis and emphysema: an eight-year study of early chronic obstructive lung disease in working men in London. Oxford University Press, Oxford, 1976.
    1. Rennard SI, Vestbo J. COPD: the dangerous underestimate of 15%. Lancet 2006;367:1216–9. 10.1016/S0140-6736(06)68516-4
    1. Palmer LJ, Knuiman MW, Divitini ML, et al. . Familial aggregation and heritability of adult lung function: results from the Busselton health study. Eur Respir J 2001;17:696–702. 10.1183/09031936.01.17406960
    1. Wilk JB, Djousse L, Arnett DK, et al. . Evidence for major genes influencing pulmonary function in the NHLBI family heart study. Genet Epidemiol 2000;19:81–94. 10.1002/1098-2272(200007)19:1<81::AID-GEPI6>;2-8
    1. Zhou JJ, Cho MH, Castaldi PJ, et al. . Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers. Am J Respir Crit Care Med 2013;188:941–7. 10.1164/rccm.201302-0263OC
    1. Lambert SA, Abraham G, Inouye M. Towards clinical utility of polygenic risk scores. Hum Mol Genet 2019;28:R133–42. 10.1093/hmg/ddz187
    1. Hersh CP, Hokanson JE, Lynch DA, et al. . Family history is a risk factor for COPD. Chest 2011;140:343–50. 10.1378/chest.10-2761
    1. Yang J, Visscher PM, Wray NR. Sporadic cases are the norm for complex disease. Eur J Hum Genet 2010;18:1039–43. 10.1038/ejhg.2009.177
    1. Wain LV, Shrine N, Artigas MS, et al. . Genome-Wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets. Nat Genet 2017;49:416–25. 10.1038/ng.3787
    1. Sakornsakolpat P, Prokopenko D, Lamontagne M, et al. . Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nat Genet 2019;51:494–505. 10.1038/s41588-018-0342-2
    1. Hobbs BD, de Jong K, Lamontagne M, et al. . Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis. Nat Genet 2017;49:426–32. 10.1038/ng.3752
    1. Shrine N, Guyatt AL, Erzurumluoglu AM, et al. . New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat Genet 2019;51:481–93. 10.1038/s41588-018-0321-7
    1. Busch R, Hobbs BD, Zhou J, et al. . Genetic association and risk scores in a chronic obstructive pulmonary disease meta-analysis of 16,707 subjects. Am J Respir Cell Mol Biol 2017;57:35–46. 10.1165/rcmb.2016-0331OC
    1. Moll M, Sakornsakolpat P, Shrine N, et al. . Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts. Lancet Respir Med 2020;8:696–708. 10.1016/S2213-2600(20)30101-6
    1. Tada H, Melander O, Louie JZ, et al. . Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur Heart J 2016;37:561–7. 10.1093/eurheartj/ehv462
    1. Abraham G, Havulinna AS, Bhalala OG, et al. . Genomic prediction of coronary heart disease. Eur Heart J 2016;37:3267–78. 10.1093/eurheartj/ehw450
    1. Agerbo E, Sullivan PF, Vilhjálmsson BJ, et al. . Polygenic risk score, parental socioeconomic status, family history of psychiatric disorders, and the risk for schizophrenia: a Danish population-based study and meta-analysis. JAMA Psychiatry 2015;72:635–41. 10.1001/jamapsychiatry.2015.0346
    1. Regan EA, Hokanson JE, Murphy JR, et al. . Genetic epidemiology of COPD (COPDGene) study design. COPD 2011;7:32–43. 10.3109/15412550903499522
    1. Vestbo J, Anderson W, Coxson HO, et al. . Evaluation of COPD longitudinally to identify predictive surrogate end-points (eclipse). Eur Respir J 2008;31:869–73. 10.1183/09031936.00111707
    1. Celli BR, Cote CG, Marin JM, et al. . The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004;350:1005–12. 10.1056/NEJMoa021322
    1. Han MK, Kazerooni EA, Lynch DA, et al. . Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes. Radiology 2011;261:274–82. 10.1148/radiol.11110173
    1. Parr DG, Stoel BC, Stolk J, et al. . Validation of computed tomographic lung densitometry for monitoring emphysema in alpha1-antitrypsin deficiency. Thorax 2006;61:485–90. 10.1136/thx.2005.054890
    1. Van Tho N, Ogawa E, Trang LTH, et al. . A mixed phenotype of airway wall thickening and emphysema is associated with dyspnea and hospitalization for chronic obstructive pulmonary disease. Ann Am Thorac Soc 2015;12:988–96. 10.1513/AnnalsATS.201411-501OC
    1. Park J, Hobbs BD, Crapo JD, et al. . Subtyping COPD by using visual and quantitative CT imaging features. Chest. 2020;157:47-60. July. 10.1016/j.chest.2019.06.015
    1. Polycor R. Package. Available: [Accessed 4 Feb 2020].
    1. Rothman KJ. Sander Greenland TLL. modern epidemiology. Lippincott Williams & Wilkins, 2008.
    1. Steyerberg EW, Vickers AJ, Cook NR, et al. . Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010;21:128–38. 10.1097/EDE.0b013e3181c30fb2
    1. Leisman DE, Harhay MO, Lederer DJ, et al. . Development and reporting of prediction models: guidance for authors from editors of respiratory, sleep, and critical care journals. Crit Care Med 2020;48:623–33. 10.1097/CCM.0000000000004246
    1. Robin X, Turck N, Hainard A, et al. . pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77. 10.1186/1471-2105-12-77
    1. Schwarzer G. Meta: an R package for meta-analysis. R News 2007;7:40–5.
    1. VanderWeele TJ, Knol MJ. A tutorial on interaction. Epidemiol Method 2014;3:33–72. 10.1515/em-2013-0005
    1. Mathur MB, VanderWeele TJ. R function for additive interaction measures. Epidemiology 2018;29:e5–6. 10.1097/EDE.0000000000000752
    1. Vansteelandt S, Bekaert M, Lange T. Imputation strategies for the estimation of natural direct and indirect effects. Epidemiol Method 2012;1 10.1515/2161-962X.1014
    1. Lange T, Vansteelandt S, Bekaert M. A simple unified approach for estimating natural direct and indirect effects. Am J Epidemiol 2012;176:190–5. 10.1093/aje/kwr525
    1. Loeys T, Moerkerke B, De Smet O, et al. . Flexible mediation analysis in the presence of nonlinear relations: beyond the mediation formula. Multivariate Behav Res 2013;48:871–94. 10.1080/00273171.2013.832132
    1. Steen J, Loeys T, Moerkerke B, et al. . medflex : An R Package for Flexible Mediation Analysis using Natural Effect Models. J Stat Softw 2017;76:1–46. 10.18637/jss.v076.i11
    1. Zöller B, Li X, Sundquist J, et al. . Familial transmission of chronic obstructive pulmonary disease in adoptees: a Swedish nationwide family study. BMJ Open 2015;5:e007310. 10.1136/bmjopen-2014-007310
    1. Nihlén U, Nyberg P, Montnémery P, et al. . Influence of family history and smoking habits on the incidence of self-reported physician's diagnosis of COPD. Respir Med 2004;98:263–70. 10.1016/j.rmed.2003.10.006
    1. Do CB, Hinds DA, Francke U, et al. . Comparison of family history and SNPs for predicting risk of complex disease. PLoS Genet 2012;8:e1002973. 10.1371/journal.pgen.1002973

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe