Article

Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene-environment interactions

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Abstract

We conducted a genome-wide association study (GWAS) and a genome-wide gene-environment interaction analysis of esophageal squamous-cell carcinoma (ESCC) in 2,031 affected individuals (cases) and 2,044 controls with independent validation in 8,092 cases and 8,620 controls. We identified six new ESCC susceptibility loci, of which four, at chromosomes 4q23, 16q12.1, 22q12 and 3q27 had a significant marginal effect (P = 1.78 × 10−39 to P = 2.49 × 10−11) and two of which, at 2q22 and 13q33, had a significant association only in the gene–alcohol drinking interaction (gene-environment interaction P (PG × E) = 4.39 × 10−11 and PG × E = 4.80 × 10−8, respectively). Variants at the 4q23 locus, which includes the ADH cluster, each had a significant interaction with alcohol drinking in their association with ESCC risk (PG × E = 2.54 × 10−7 to PG × E = 3.23 × 10−2). We confirmed the known association of the ALDH2 locus on 12q24 to ESCC, and a joint analysis showed that drinkers with both of the ADH1B and ALDH2 risk alleles had a fourfold increased risk for ESCC compared to drinkers without these risk alleles. Our results underscore the direct genetic contribution to ESCC risk, as well as the genetic contribution to ESCC through interaction with alcohol consumption.

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Change history

  • Corrected online 27 August 2014

    In the version of this article initially published, in Table 1 and its associated text, there was a calculation error in which the relative sizes of the case and control populations were set to be equal; because the size of the case population (15,767) was nearly double that of the control population (8,329), this resulted in erroneously inflated penetrance estimates. A simple definition of penetrance is used that is often applied in medical genetics—namely, the proportion of observed mutation carriers that are affected—to provide a metric that would be useful to clinical geneticists in a setting in which disease is heavily enriched, for example, in diagnosing children with developmental delay. That formulation is biased upwards with respect to population-level penetrance. Thus, in this corrigendum, an estimate more appropriate for population-level inference is provided assuming a general disease prevalence of 5.3% (Am. J. Hum. Genet. 42, 677–693, 1988) along with the more familiar odds ratio (OR) estimate. Importantly, all of these measures of penetrance are intrinsically limited by sampling error and imprecision in defining disease prevalence. We note that the mutation carrier counts, P values and other results in the original version of Table 1 are correct, and the key results and conclusions of the paper are unaffected. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

This work was funded by the National High-Tech Research and Development Program of China (2009AA022706 to D.L.), the National Basic Research Program of China (2011CB504303 to D.L. and W.T.), the National Natural Science Foundation of China (30721001 to D.L., Q.Z. and Z.L.) and the Intramural Research Program of the US National Institutes of Health, NCI and the Division of Cancer Epidemiology and Genetics.

Author information

Author notes

    • Chen Wu
    • , Peter Kraft
    • , Kan Zhai
    •  & Jiang Chang

    These authors contributed equally to this work.

Affiliations

  1. State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

    • Chen Wu
    • , Kan Zhai
    • , Jiang Chang
    • , Zhihua Liu
    • , Qimin Zhan
    • , Yu Liu
    • , Yan Qiao
    • , Yuling Zhou
    • , Dianke Yu
    • , Wen Tan
    •  & Dongxin Lin
  2. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.

    • Chen Wu
    • , Peter Kraft
    •  & Liming Liang
  3. Division of Cancer Epidemiology and Genetics, National Cancer Institute (NCI), US National Institutes of Health, Bethesda, Maryland, USA.

    • Zhaoming Wang
    • , Christian C Abnet
    • , Nan Hu
    • , Neal D Freedman
    • , Alisa M Goldstein
    • , Stephen J Chanock
    •  & Philip R Taylor
  4. Core Genotyping Facility, NCI-Frederick, SAIC-Frederick, Frederick, Maryland, USA.

    • Zhaoming Wang
    •  & Stephen J Chanock
  5. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.

    • Yun Li
  6. Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, Jiangsu, China.

    • Zhibin Hu
    • , Guangfu Jin
    •  & Hongbing Shen
  7. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University School of Oncology, Beijing Cancer Hospital and Institute, Beijing, China.

    • Zhonghu He
    • , Chuanhai Guo
    •  & Yang Ke
  8. State Key Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China.

    • Weihua Jia
    • , Jianhua Fu
    •  & Yixin Zeng
  9. Key Laboratory for Environment and Health (Ministry of Education), School of Public Health, Huazhong University of Sciences and Technology, Wuhan, Hubei, China.

    • Xiaoping Miao
    •  & Tangchun Wu
  10. Laboratory of Cancer Molecular Genetics, Medical College of Soochow University, Suzhou, Jiangsu, China.

    • Yifeng Zhou
  11. Anyang Cancer Hospital, Anyang, Henan, China.

    • Changdong Lu
    •  & Haijun Yang
  12. Shanxi Cancer Hospital, Taiyuan, Shanxi, China.

    • Ti Ding

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Contributions

D.L. was the overall principle investigator of the study who conceived the study and obtained financial support, was responsible for study design, oversaw the entire study, interpreted the results and wrote parts of and synthesized the paper. C.W. performed overall project management, oversaw laboratory analyses, performed statistical analyses and drafted the initial manuscript. P.K. oversaw statistical analyses, interpreted the results and reviewed the manuscript. Y. Li and L.L. performed the imputation analysis and reviewed the manuscript. K.Z., J.C., Y.Q., Yuling Zhou and Y. Liu performed laboratory analyses. Z. Hu, G.J. and H.S. were responsible for subject recruitment and sample preparation of Nanjing samples. Z. He, C.G., C.L., H.Y. and Y.K. were responsible for subject recruitment and sample preparation of Henan samples. W.J., J.F. and Y. Zeng were responsible for subject recruitment and sample preparation of Guangzhou samples. X.M. and T.W. provided some of the control samples. Yifeng Zhou was responsible for subject recruitment of the additional validation cohorts. D.Y. and W.T. performed subject recruitment and sample preparation of Beijing samples. Q.Z. and Z.L. provided some of the financial support and reviewed the manuscript. Z.W., C.C.A., N.H., N.D.F., T.D., A.M.G., S.J.C. and P.R.T. performed subject recruitment, sample preparation, laboratory analysis and statistical analysis of Shanxi samples and reviewed the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Dongxin Lin.

Supplementary information

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    Supplementary Text and Figures

    Supplementary Tables 1–7 and Supplementary Figure 1.