title: [Genome-wide association study based risk prediction model in predicting lung cancer risk in Chinese].
abstract: OBJECTIVE
  To evaluate the predictive power of risk model by combining traditional epidemiological factors and genetic factors.
  
  
  METHODS
  Our previous GWAS data of lung cancer in Chinese were used in training set (Nanjing and Shanghai: 1473 cases vs. 1962 control) and testing set (Beijing and Wuhan: 858 cases vs. 1 115 control). All the single nucleotide polymorphisms (SNPs) associated with lung cancer risk were systematically selected and stepwise logistic regression analysis was used to select independent factors in the training set. The wGRS (weighted genetic score) was further used to calculate genetic risk score. To evaluate the contribution of the genetic factors, 3 risk models were established by using the training set, i.e. smoking model (based on smoking status) , genetic risk model (based on genetic risk score) and combined model (based on smoke and genetic risk score). The predictability of the models were evaluated by the areas under the receiver operating characteristic (ROC) curves, area under curve (AUC), net reclassification improvement (NRI) and integrated discrimination index (IDI). Besides, the results were further verified in the testing set.
  
  
  RESULTS
  In the training set, it was found that the AUC of the smoking, genetic risk and combined models were 0.65 (0.63-0.66), 0.60 (0.59-0.62) and 0.69 (0.67-0.71), respectively. Compared with combined model, the predictive power of other two models significantly declined, the difference was statistically significant (P<0.001). Furthermore, compared with the smoking model, the NRI of the combined model increased by 4.57% (2.23%-6.91%) and IDI increased by 3.11% (2.52%-3.69%) in the training set, the difference was statistically significant (P<0.001). Similarly, in the testing set NRI increased by 2.77%, the difference was not statistically significant (P=0.069) , and IDI increased by 3.16%, the difference was statistically significant (P<0.001).
  
  
  CONCLUSIONS
  This study showed that combining 14 genetic variants with traditional epidemiological factors could improve the predictive power of risk model for lung cancer. The model could be used in the screening of high-risk population of lung cancer in Chinese and provide evidence for the early diagnosis and treatment of lung cancer.
authors.affiliation: Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Meng
authors.initials: M
authors.lastname: Zhu
authors.affiliation: Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Juncheng
authors.initials: J
authors.lastname: Dai
authors.affiliation: Medical Systems Biology Research Center, Tsinghua University School of Medicine.
authors.forename: Lan
authors.initials: L
authors.lastname: Xie
authors.affiliation: Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Guangfu
authors.initials: G
authors.lastname: Jin
authors.affiliation: Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Hongxia
authors.initials: H
authors.lastname: Ma
authors.affiliation: Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Hu
authors.affiliation: Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University.
authors.forename: Yongyong
authors.initials: Y
authors.lastname: Shi
authors.affiliation: State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College.
authors.forename: Dongxin
authors.initials: D
authors.lastname: Lin
authors.affiliation: Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Email: hbshen@njmu.edu.cn.
authors.forename: Hongbing
authors.initials: H
authors.lastname: Shen
mesh_terms: D019540:Area Under Curve; D044466:Asian People; D000068476:Beijing; D016022:Case-Control Studies; D002681:China; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D012307:Risk Factors