Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model

BMC Medical Genetics

Daru Lu²  Weimin Wang³  Li Jin²  Hongcheng Liu²  Weiwei Fan²  Hongyan Chen²  Ji Qian²  Jiucun Wang²  Xueying Zhao²  Lixin Yang¹  Huan Li² 

[1] Department of Cardiothoracic Surgery, Changhai Hospital of Shanghai, Second Military Medical University, Shanghai, China;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;Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, China Jiliang University, Hangzhou, 310018, China

关键词: Risk assessment;    Lung cancer;    Genetic risk score;    Cumulative risk;    Chinese;   

Others  :  1177761
DOI  :  10.1186/1471-2350-13-118

 received in 2012-03-21, accepted in 2012-11-06,  发布年份 2012

【 摘 要 】

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.

Conclusion

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.

【 授权许可】

   
2012 Li et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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