title: Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model.
abstract: BACKGROUND
  Lung cancer is a complex polygenic disease. Although recent genome-wide association (GWA) studies have identified multiple susceptibility loci for lung cancer, most of these variants have not been validated in a Chinese population. In this study, we investigated whether a genetic risk score combining multiple.
  
  
  METHODS
  Five single-nucleotide polymorphisms (SNPs) identified in previous GWA or large cohort studies were genotyped in 5068 Chinese case-control subjects. The genetic risk score (GRS) based on these SNPs was estimated by two approaches: a simple risk alleles count (cGRS) and a weighted (wGRS) method. The area under the receiver operating characteristic (ROC) curve (AUC) in combination with the bootstrap resampling method was used to assess the predictive performance of the genetic risk score for lung cancer.
  
  
  RESULTS
  Four independent SNPs (rs2736100, rs402710, rs4488809 and rs4083914), were found to be associated with a risk of lung cancer. The wGRS based on these four SNPs was a better predictor than cGRS. Using a liability threshold model, we estimated that these four SNPs accounted for only 4.02% of genetic variance in lung cancer. Smoking history contributed significantly to lung cancer (P < 0.001) risk [AUC = 0.619 (0.603-0.634)], and incorporated with wGRS gave an AUC value of 0.639 (0.621-0.652) after adjustment for over-fitting. This model shows promise for assessing lung cancer risk in a Chinese population.
  
  
  CONCLUSIONS
  Our results indicate that although genetic variants related to lung cancer only added moderate discriminatory accuracy, it still improved the predictive ability of the assessment model in Chinese population.
authors.affiliation: State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Institute of Genetics, School of Life Sciences, Fudan University, Handan Rd, Shanghai 200433, China.
authors.forename: Huan
authors.initials: H
authors.lastname: Li
authors.forename: Lixin
authors.initials: L
authors.lastname: Yang
authors.forename: Xueying
authors.initials: X
authors.lastname: Zhao
authors.forename: Jiucun
authors.initials: J
authors.lastname: Wang
authors.forename: Ji
authors.initials: J
authors.lastname: Qian
authors.forename: Hongyan
authors.initials: H
authors.lastname: Chen
authors.forename: Weiwei
authors.initials: W
authors.lastname: Fan
authors.forename: Hongcheng
authors.initials: H
authors.lastname: Liu
authors.forename: Li
authors.initials: L
authors.lastname: Jin
authors.forename: Weimin
authors.initials: W
authors.lastname: Wang
authors.forename: Daru
authors.initials: D
authors.lastname: Lu
mesh_terms: D016022:Case-Control Studies; D002681:China; D005260:Female; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D008297:Male; D008957:Models, Genetic; D020412:Multifactorial Inheritance; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors