TY - JOUR
T1 - Studying the Utility of Using Genetics to Predict Smoking-Related Outcomes in a Population-Based Study and a Selected Cohort
AU - Bray, Michael J.
AU - Chen, Li Shiun
AU - Fox, Louis
AU - Ma, Yinjiao
AU - Grucza, Richard A.
AU - Hartz, Sarah M.
AU - Culverhouse, Robert C.
AU - Saccone, Nancy L.
AU - Hancock, Dana B.
AU - Johnson, Eric O.
AU - McKay, James D.
AU - Baker, Timothy B.
AU - Bierut, Laura J.
N1 - Funding Information:
The study was funded by the National Institutes of Health (NIH) training grant 5T32MH014677 to Michael J. Bray. This study was also funded by the National Institute on Drug Abuse grant R01DA036583 to Laura J. Bierut, R01DA042090 to Dana B. Hancock, and R01DA042195 to Richard A. Grucza. In addition, Laura J. Bierut was also supported by National Institute on Aging grant R56AG058726 and National Cancer Institute grants U19CA203654 and P30CA091842. Research reported in this publication was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH. The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under contract numbers HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I. The authors thank the staff and participants of the ARIC study for their important contributions. Funding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle).
Publisher Copyright:
© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Introduction: The purpose of this study is to examine the predictive utility of polygenic risk scores (PRSs) for smoking behaviors. Aims and Methods: Using summary statistics from the Sequencing Consortium of Alcohol and Nicotine use consortium, we generated PRSs of ever smoking, age of smoking initiation, cigarettes smoked per day, and smoking cessation for participants in the population-based Atherosclerosis Risk in Communities (ARIC) study (N = 8638), and the Collaborative Genetic Study of Nicotine Dependence (COGEND) (N = 1935). The outcomes were ever smoking, age of smoking initiation, heaviness of smoking, and smoking cessation. Results: In the European ancestry cohorts, each PRS was significantly associated with the corresponding smoking behavior outcome. In the ARIC cohort, the PRS z-score for ever smoking predicted smoking (odds ratio [OR]: 1.37; 95% confidence interval [CI]: 1.31, 1.43); the PRS z-score for age of smoking initiation was associated with age of smoking initiation (OR: 0.87; 95% CI: 0.82, 0.92); the PRS z-score for cigarettes per day was associated with heavier smoking (OR: 1.17; 95% CI: 1.11, 1.25); and the PRS z-score for smoking cessation predicted successful cessation (OR: 1.24; 95% CI: 1.17, 1.32). In the African ancestry cohort, the PRSs did not predict smoking behaviors. Conclusions: Smoking-related PRSs were associated with smoking-related behaviors in European ancestry populations.This improvement in prediction is greatest in the lowest and highest genetic risk categories.The lack of prediction in African ancestry populations highlights the urgent need to increase diversity in research so that scientific advances can be applied to populations other than those of European ancestry. Implications: This study shows that including both genetic ancestry and PRSs in a single model increases the ability to predict smoking behaviors compared with the model including only demographic characteristics.This finding is observed for every smoking-related outcome. Even though adding genetics is more predictive, the demographics alone confer substantial and meaningful predictive power. However, with increasing work in PRSs, the predictive ability will continue to improve.
AB - Introduction: The purpose of this study is to examine the predictive utility of polygenic risk scores (PRSs) for smoking behaviors. Aims and Methods: Using summary statistics from the Sequencing Consortium of Alcohol and Nicotine use consortium, we generated PRSs of ever smoking, age of smoking initiation, cigarettes smoked per day, and smoking cessation for participants in the population-based Atherosclerosis Risk in Communities (ARIC) study (N = 8638), and the Collaborative Genetic Study of Nicotine Dependence (COGEND) (N = 1935). The outcomes were ever smoking, age of smoking initiation, heaviness of smoking, and smoking cessation. Results: In the European ancestry cohorts, each PRS was significantly associated with the corresponding smoking behavior outcome. In the ARIC cohort, the PRS z-score for ever smoking predicted smoking (odds ratio [OR]: 1.37; 95% confidence interval [CI]: 1.31, 1.43); the PRS z-score for age of smoking initiation was associated with age of smoking initiation (OR: 0.87; 95% CI: 0.82, 0.92); the PRS z-score for cigarettes per day was associated with heavier smoking (OR: 1.17; 95% CI: 1.11, 1.25); and the PRS z-score for smoking cessation predicted successful cessation (OR: 1.24; 95% CI: 1.17, 1.32). In the African ancestry cohort, the PRSs did not predict smoking behaviors. Conclusions: Smoking-related PRSs were associated with smoking-related behaviors in European ancestry populations.This improvement in prediction is greatest in the lowest and highest genetic risk categories.The lack of prediction in African ancestry populations highlights the urgent need to increase diversity in research so that scientific advances can be applied to populations other than those of European ancestry. Implications: This study shows that including both genetic ancestry and PRSs in a single model increases the ability to predict smoking behaviors compared with the model including only demographic characteristics.This finding is observed for every smoking-related outcome. Even though adding genetics is more predictive, the demographics alone confer substantial and meaningful predictive power. However, with increasing work in PRSs, the predictive ability will continue to improve.
UR - http://www.scopus.com/inward/record.url?scp=85119336861&partnerID=8YFLogxK
U2 - 10.1093/ntr/ntab100
DO - 10.1093/ntr/ntab100
M3 - Article
C2 - 33991188
AN - SCOPUS:85119336861
SN - 1462-2203
VL - 23
SP - 2110
EP - 2116
JO - Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
JF - Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
IS - 12
ER -