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Open Access Research article

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

Huan Li1, Lixin Yang2, Xueying Zhao1, Jiucun Wang1, Ji Qian1, Hongyan Chen1, Weiwei Fan1, Hongcheng Liu1, Li Jin1, Weimin Wang34* and Daru Lu1*

Author affiliations

1 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

2 Department of Cardiothoracic Surgery, Changhai Hospital of Shanghai, Second Military Medical University, Shanghai, China

3 Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine Technique, College of Life Sciences, China Jiliang University, Hangzhou, China

4 Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, China Jiliang University, Hangzhou, 310018, China

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Citation and License

BMC Medical Genetics 2012, 13:118  doi:10.1186/1471-2350-13-118

Published: 10 December 2012

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.

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.

Keywords:
Chinese; Cumulative risk; Genetic risk score; Lung cancer; Risk assessment