Joint analysis of three genome-wide association studies of esophageal squamous cell carcinoma in Chinese populations

Abstract

We conducted a joint (pooled) analysis of three genome-wide association studies (GWAS)1,2,3 of esophageal squamous cell carcinoma (ESCC) in individuals of Chinese ancestry (5,337 ESCC cases and 5,787 controls) with 9,654 ESCC cases and 10,058 controls for follow-up. In a logistic regression model adjusted for age, sex, study and two eigenvectors, two new loci achieved genome-wide significance, marked by rs7447927 at 5q31.2 (per-allele odds ratio (OR) = 0.85, 95% confidence interval (CI) = 0.82–0.88; P = 7.72 × 10−20) and rs1642764 at 17p13.1 (per-allele OR = 0.88, 95% CI = 0.85–0.91; P = 3.10 × 10−13). rs7447927 is a synonymous SNP in TMEM173, and rs1642764 is an intronic SNP in ATP1B2, near TP53. Furthermore, a locus in the HLA class II region at 6p21.32 (rs35597309) achieved genome-wide significance in the two populations at highest risk for ESSC (OR = 1.33, 95% CI = 1.22–1.46; P = 1.99 × 10−10). Our joint analysis identifies new ESCC susceptibility loci overall as well as a new locus unique to the population in the Taihang Mountain region at high risk of ESCC.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Association results, recombination and LD plots for the 5q31.2 and 17p13.1 regions identified in stage 1 (joint analysis of three independent GWAS), for the two replication sets (Beijing and Henan) and for the joint estimate from all data.

References

  1. 1

    Abnet, C.C. et al. A shared susceptibility locus in PLCE1 at 10q23 for gastric adenocarcinoma and esophageal squamous cell carcinoma. Nat. Genet. 42, 764–767 (2010).

    CAS  Article  Google Scholar 

  2. 2

    Wang, L.D. et al. Genome-wide association study of esophageal squamous cell carcinoma in Chinese subjects identifies susceptibility loci at PLCE1 and C20orf54. Nat. Genet. 42, 759–763 (2010).

    CAS  Article  Google Scholar 

  3. 3

    Wu, C. et al. Genome-wide association study identifies three new susceptibility loci for esophageal squamous-cell carcinoma in Chinese populations. Nat. Genet. 43, 679–684 (2011).

    CAS  Article  Google Scholar 

  4. 4

    Wu, C. et al. Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene-environment interactions. Nat. Genet. 44, 1090–1097 (2012).

    CAS  Article  Google Scholar 

  5. 5

    Abnet, C.C. et al. Genotypic variants at 2q33 and risk of esophageal squamous cell carcinoma in China: a meta-analysis of genome-wide association studies. Hum. Mol. Genet. 21, 2132–2141 (2012).

    CAS  Article  Google Scholar 

  6. 6

    Yang, C.S. Research on esophageal cancer in China: a review. Cancer Res. 40, 2633–2644 (1980).

    CAS  PubMed  Google Scholar 

  7. 7

    Kamangar, F., Chow, W.H., Abnet, C.C. & Dawsey, S.M. Environmental causes of esophageal cancer. Gastroenterol. Clin. North Am. 38, 27–57 (2009).

    Article  Google Scholar 

  8. 8

    Tran, G.D. et al. Prospective study of risk factors for esophageal and gastric cancers in the Linxian general population trial cohort in China. Int. J. Cancer 113, 456–463 (2005).

    CAS  Article  Google Scholar 

  9. 9

    Bishop, D.T. et al. Genome-wide association study identifies three loci associated with melanoma risk. Nat. Genet. 41, 920–925 (2009).

    CAS  Article  Google Scholar 

  10. 10

    Sherborne, A.L. et al. Variation in CDKN2A at 9p21.3 influences childhood acute lymphoblastic leukemia risk. Nat. Genet. 42, 492–494 (2010).

    CAS  Article  Google Scholar 

  11. 11

    Berndt, S.I. et al. Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia. Nat. Genet. 45, 868–876 (2013).

    CAS  Article  Google Scholar 

  12. 12

    Rajaraman, P. et al. Genome-wide association study of glioma and meta-analysis. Hum. Genet. 131, 1877–1888 (2012).

    Article  Google Scholar 

  13. 13

    Gu, F. et al. Common genetic variants in the 9p21 region and their associations with multiple tumours. Br. J. Cancer 108, 1378–1386 (2013).

    CAS  Article  Google Scholar 

  14. 14

    Ishikawa, H. & Barber, G.N. STING is an endoplasmic reticulum adaptor that facilitates innate immune signalling. Nature 455, 674–678 (2008).

    CAS  Article  Google Scholar 

  15. 15

    Kennedy, R.B. et al. Genome-wide analysis of polymorphisms associated with cytokine responses in smallpox vaccine recipients. Hum. Genet. 131, 1403–1421 (2012).

    CAS  Article  Google Scholar 

  16. 16

    Eck, P. et al. Genomic and functional analysis of the sodium-dependent vitamin C transporter SLC23A1-SVCT1. Genes Nutr. 2, 143–145 (2007).

    CAS  Article  Google Scholar 

  17. 17

    Mirvish, S.S. Role of N-nitroso compounds (NOC) and N-nitrosation in etiology of gastric, esophageal, nasopharyngeal and bladder cancer and contribution to cancer of known exposures to NOC. Cancer Lett. 93, 17–48 (1995).

    CAS  Article  Google Scholar 

  18. 18

    Enciso-Mora, V. et al. Low penetrance susceptibility to glioma is caused by the TP53 variant rs78378222. Br. J. Cancer 108, 2178–2185 (2013).

    CAS  Article  Google Scholar 

  19. 19

    Hu, N. et al. Frequent inactivation of the TP53 gene in esophageal squamous cell carcinoma from a high-risk population in China. Clin. Cancer Res. 7, 883–891 (2001).

    CAS  PubMed  Google Scholar 

  20. 20

    Remeseiro, S. & Losada, A. Cohesin, a chromatin engagement ring. Curr. Opin. Cell Biol. 25, 63–71 (2013).

    CAS  Article  Google Scholar 

  21. 21

    Coviello, A.D. et al. A genome-wide association meta-analysis of circulating sex hormone–binding globulin reveals multiple loci implicated in sex steroid hormone regulation. PLoS Genet. 8, e1002805 (2012).

    CAS  Article  Google Scholar 

  22. 22

    Freedman, N.D. et al. The association of menstrual and reproductive factors with upper gastrointestinal tract cancers in the NIH-AARP cohort. Cancer 116, 1572–1581 (2010).

    Article  Google Scholar 

  23. 23

    Wang, Q.M., Qi, Y.J., Jiang, Q., Ma, Y.F. & Wang, L.D. Relevance of serum estradiol and estrogen receptor β expression from a high-incidence area for esophageal squamous cell carcinoma in China. Med. Oncol. 28, 188–193 (2011).

    CAS  Article  Google Scholar 

  24. 24

    Bei, J.X. et al. A genome-wide association study of nasopharyngeal carcinoma identifies three new susceptibility loci. Nat. Genet. 42, 599–603 (2010).

    CAS  Article  Google Scholar 

  25. 25

    Li, S. et al. GWAS identifies novel susceptibility loci on 6p21.32 and 21q21.3 for hepatocellular carcinoma in chronic hepatitis B virus carriers. PLoS Genet. 8, e1002791 (2012).

    CAS  Article  Google Scholar 

  26. 26

    Lan, Q. et al. Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia. Nat. Genet. 44, 1330–1335 (2012).

    CAS  Article  Google Scholar 

  27. 27

    Slager, S.L. et al. Genome-wide association study identifies a novel susceptibility locus at 6p21.3 among familial CLL. Blood 117, 1911–1916 (2011).

    CAS  Article  Google Scholar 

  28. 28

    Okada, Y. et al. HLA-Cw*1202-B*5201-DRB1*1502 haplotype increases risk for ulcerative colitis but reduces risk for Crohn's disease. Gastroenterology 141, 864–871 (2011).

    CAS  Article  Google Scholar 

  29. 29

    Han, J.W. et al. Genome-wide association study in a Chinese Han population identifies nine new susceptibility loci for systemic lupus erythematosus. Nat. Genet. 41, 1234–1237 (2009).

    CAS  Article  Google Scholar 

  30. 30

    Su, Z. et al. Common variants at the MHC locus and at chromosome 16q24.1 predispose to Barrett's esophagus. Nat. Genet. 44, 1131–1136 (2012).

    CAS  Article  Google Scholar 

  31. 31

    Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    CAS  Article  Google Scholar 

  32. 32

    Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    CAS  Article  Google Scholar 

Download references

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.) and the National Natural Science Foundation of China (30721001 to D.L., Q.Z. and Z.L.).

We thank all the subjects and their family members whose contributions made this work possible; medical students from Zhengzhou University, Xinxiang Medical University, Zhengzhou Medical School and the Henan University of Science and Technology for sample and data collections; Q.C. Kan (The First Affiliated Hospital of Zhengzhou University) and Y. Xing (Xinxiang Medical University) for organizing field work for sample collection and finding financial support for the study; the Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, China, for genotyping; and W. Huang (Health Department of Henan Province) for field work organization.

This work was supported by the Invitation Team of the Ministry of Education (2008IRTSTHN010), the National Natural Science Foundation of China (81071783), 863 High-Tech Key Projects (2012AA02A209, 2012AA02A503 and 2012AA02A201), Innovation Scientists and Technicians Troop Construction Projects of Henan Province (3047), the Key Disciplines Revitalization Plan of Zhengzhou University (20132016) and the Collaborative Innovation Center for Esophageal Cancer Research of Henan Province (20132016).

The Shanghai Men's Health Study (SMHS) was supported by NCI extramural research grant R01 CA82729. The Shanghai Women's Health Study (SWHS) was supported by NCI extramural research grant R37 CA70837 and, in part, for biological sample collection, by NCI intramural research program contract NO2-CP-11010 with Vanderbilt University. The studies would not be possible without continuing support and devotion from the study participants and the staff of SMHS and SWHS.

The Singapore Chinese Health Study (SCHS) was supported by NCI extramural research grants R01 CA55069, R35 CA53890, R01 CA80205 and R01 CA144034. We are indebted to the contributions of M.C. Yu and H.-P. Lee in the establishment of this cohort. The study would not be possible without the assistance in identifying cancer cases through database linkage provided by the Ministry of Health in Singapore. We are indebted to the study subjects for their continuing participation and to the staff of SCHS for their support.

The Shanxi Upper Gastrointestinal Cancer Genetics Project was supported by NCI intramural research program contract NO2-SC-66211 with the Shanxi Cancer Hospital and Institute.

The Nutrition Intervention Trials (NIT) was supported by NCI intramural research program contracts NO1-SC-91030 and HHSN261200477001C with the Cancer Institute and Hospital of the Chinese Academy of Medical Sciences.

This research was supported in part by the intramural research program of the US NIH/NCI, the Division of Cancer Epidemiology and Genetics, and the Center for Cancer Research.

This project was funded in part with federal funds from the US NIH/NCI under contract HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the US Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US government.

Author information

Affiliations

Authors

Contributions

C.C.A., S.J.C., N.D.F., A.M.G., N.H., D.L., X.S., P.R.T., L.-D.W., Zhaoming Wang and C. Wu organized and designed the study. L.B., S.J.C., A.H., D.L., X.S., P.R.T., L.-D.W., Zhaoming Wang, C. Wu, J.Y. and M.Y. conducted and supervised the genotyping of samples. C.C.A., S.J.C., N.D.F., A.M.G., M.G., D.L., N.H., P.R.T., L.-D.W., Zhaoming Wang and K.Y. contributed to the design and execution of statistical analysis. C.C.A., S.J.C., N.D.F., X.-S.F., A.M.G., J.H., N.H., D.L., X.S., P.R.T., W.T., L.-D.W., Zhaoming Wang, C. Wu and X.-B.Z. wrote the first draft of the manuscript. C. Wu, Zhaoming Wang, X.S., X.-S.F., C.C.A., J.H., N.H., X.-B.Z., W.T., Q.Z., Z. Hu, Z. He, W.J., Y. Zhou, K.Y., X.-O.S., J.-M.Y., W. Zheng, X.-K.Z., S.-G.G., Z.-Q.Y., F.-Y.Z., Z.-M.F., J.-L.C., H.-L.L., X.-N.H., B. Li, X.C., S.M.D., L.L., M.P.L., T.D., Y.-L.Q., Z.L., Yu Liu, D.Y., J.C., L. Wei, Y.-T.G., W.-P.K., Y.-B.X., Z.-Z.T., J.-H.F., J.-J.H., S.-L.Z., P.Z., D.-Y.Z., Y.Y., Y.H., C. Liu, K.Z., Y.Q., G.J., C. Guo, J.F., X.M., C. Lu, H.Y., C. Wang, W.A.W., M.G., M.Y., J.Y., E.-T.G., A.-L.L., W. Zhang, Xue-Min Li, L.-D.S., B.-G.M., Yan Li, S.T., X.-Q.P., J.L., A.H., K.J., C. Giffen, L.B., J.F.F., H.S., Y.K., Y. Zeng, T.W., P.K., C.C.C., M.A.T., Z.-C.H., Y.-L.L., Y.-L.H., Yu Liu, L.W., G.Y., L.-S.C., X.L., T.M., H.M., L.S., Xin-Min Li, Xiu-Min Li, J.-W.K., Y.-F.Z., L.-Q.Y., Zhou Wang, Yin Li, Q.Q., W.-J.Y., G.-Y.L., L.-Q.C., E.-M.L., L.Y., W.-B.Y., R.W., L.-W.W., X.-P.F., F.-H.Z., W.-X.Z., Y.-M.M., M.Z., G.-L.X., J.-L.L., M.H., J.-L.R., B. Liu, S.-W.R., Q.-P.K., F.L., I.S., W.W., Y.-R.Z., C.-W.F., J.W., Y.-H.Y., H.-Z.H., Q.-D.B., B.-C.L., A.-Q.W., D.X., W.-C.Y., L. Wang, X.-H.Z., S.-Q.C., J.-Y.H., X.-J.Z., N.D.F., A.M.G., D.L., P.R.T., L.-D.W. and S.J.C. contributed to the epidemiological studies or contributed samples to the GWAS or follow-up genotyping. C. Wu, Zhaoming Wang, X.S., X.-S.F., C.C.A., J.H., N.H., X.-B.Z., W.T., Q.Z., Z. Hu, Z. He, W.J., Y. Zhou, K.Y., X.-O.S., J.-M.Y., W. Zheng, X.-K.Z., S.-G.G., Z.-Q.Y., F.-Y.Z., Z.-M.F., J.-L.C., H.-L.L., X.-N.H., B. Li, X.C., S.M.D., L.L., M.P.L., T.D., Y.-L.Q., Z.L., Yu Liu, D.Y., J.C., L. Wei, Y.-T.G., W.-P.K., Y.-B.X., Z.-Z.T., J.-H.F., J.-J.H., S.-L.Z., P.Z., D.-Y.Z., Y.Y., Y.H., C. Liu, K.Z., Y.Q., G.J., C. Guo, J.F., X.M., C. Lu, H.Y., C. Wang, W.A.W., M.G., M.Y., J.Y., E.-T.G., A.-L.L., W. Zhang, Xue-Min Li, L.-D.S., B.-G.M., Yan Li, S.T., X.-Q.P., J.L., A.H., K.J., C. Giffen, L.B., J.F.F., H.S., Y.K., Y. Zeng, T.W., P.K., C.C.C., M.A.T., Z.-C.H., Y.-L.L., Y.-L.H., Yu Liu, L.W., G.Y., L.-S.C., X.L., T.M., H.M., L.S., Xin-Min Li, Xiu-Min Li, J.-W.K., Y.-F.Z., L.-Q.Y., Zhou Wang, Yin Li, Q.Q., W.-J.Y., G.-Y.L., L.-Q.C., E.-M.L., L.Y., W.-B.Y., R.W., L.-W.W., X.-P.F., F.-H.Z., W.-X.Z., Y.-M.M., M.Z., G.-L.X., J.-L.L., M.H., J.-L.R., B. Liu, S.-W.R., Q.-P.K., F.L., I.S., W.W., Y.-R.Z., C.-W.F., J.W., Y.-H.Y., H.-Z.H., Q.-D.B., B.-C.L., A.-Q.W., D.X., W.-C.Y., L. Wang, X.-H.Z., S.-Q.C., J.-Y.H., X.-J.Z., N.D.F., A.M.G., D.L., P.R.T., L.-D.W. and S.J.C. contributed to the writing of the manuscript.

Corresponding authors

Correspondence to Dongxin Lin or Philip R Taylor or Li-Dong Wang.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Eigenvector plots for the three GWAS including subpopulations from the NCI scan.

The NCI subpopulations are as follows: NITC, Nutrition Intervention Trial Cohort; SHNX, Shanxi Cancer Genetics Study; SING, Singapore Chinese Cohort; SWHS, Shanghai Men’s Health Study; SWHS, Shanghai Women’s Health Study. Beijing refers to the study by Wu et al., and Henan refers to the study by Wang et al. The plot on the left uses the first and second eigenvectors, whereas the plot on the right uses the second and third eigenvectors. The plots show that the first eigenvector partially separates the subpopulations.

Supplementary Figure 2 QQ plots of observed versus expected P values for the joint genome-wide stage 1 analysis from a logistic regression model adjusted for age, sex, study and two eigenvectors from a joint model to adjust for population stratification.

The red dots represent a plot using all SNPs, whereas the green dots represent a plot excluding SNPs (and SNPs within 500 kb of SNPs) previously reported to be associated with ESCC risk. The inflation factor λ for the plot using all SNPs was 1.01.

Supplementary Figure 3 QQ plots of observed versus expected P values for the Beijing study based on a logistic regression model adjusted for age, sex and three study-specific eigenvectors (first, fifth and seventh) to adjust for population stratification.

The red dots represent a plot using all SNPs, whereas the green dots represent a plot excluding SNPs (and SNPs within 500 kb of SNPs) previously reported to be associated with ESCC risk. The inflation factor lfor the plot using all SNPs was 1.00.

Supplementary Figure 4 QQ plots of observed versus expected P values for the Henan study from a logistic regression model adjusted for age and sex.

This study did not require eigenvector adjustment for population stratification because the ESCC risk base model did not reveal statistically significant eigenvectors. The red dots represent a plot using all SNPs, whereas the green dots represent a plot excluding SNPs (and SNPs within 500 kb of SNPs) previously reported to be associated with ESCC risk. The inflation factor λ for the plot using all SNPs was 1.02.

Supplementary Figure 5 QQ plots of observed versus expected P values for the NCI study based on a logistic regression model adjusted for age, sex and the first study-specific eigenvector to adjust for population stratification.

The red dots represent a plot using all SNPs, whereas the green dots represent a plot excluding SNPs (and SNPs within 500 kb of SNPs) previously reported to be associated with ESCC risk. The inflation factor λ for the plot using all SNPs was 1.01.

Supplementary Figure 6 Association results, recombination and linkage disequilibrium plot for the region at 6p21.32 for the NCI and Henan scans combined (meta), the Henan replication set and the combined estimate from both stages.

The region at 6p21.32: 32,508,399–32,689,013 for the HLA class II locus was plotted. Association results from a trend test in –log10 P values (y axis, left; gray diamonds, stage 1 results including the NCI and Henan scans; purple diamonds, Henan replication results; red diamonds, combined results) of the SNPs are shown according to their chromosomal positions (x axis). Linkage disequilibrium structure based on 1000 Genomes Project CHB data (n = 91) was visualized with snp.plotter software. Owing to the hypervariability of the locus, the number of SNPs was pruned so that no two sites were within the same 500-bp window. The line graph shows the likelihood ratio statistics (y axis, right) for recombination hotspot as determined by SequenceLDhot software on the basis of the background recombination rates inferred by PHASE v2.1 using 100 randomly sampled controls from the NCI scan (red line) and Henan scan (blue line) data from stage 1. Physical locations are based on NCBI human genome Build 37. Gene annotation was based on the NCBI RefSeq genes from the UCSC Genome Browser.

Supplementary Figure 7 QQ plots of observed versus expected P values for the joint analysis from a logistic regression model adjusted for age and sex.

The green dots represent results from a model without adjustment for population stratification, whereas the red dots represent results from a model including two eigenvectors to adjust for population stratification. The inflation factor λ was 1.17 before adjustment and 1.01 after adjustment.

Supplementary Figure 8 QQ plots of observed versus expected P values for the Beijing study from a logistic regression model adjusted for age and sex.

The green dots represent results from a model without adjustment for population stratification, whereas the red dots represent results from a model including three study-specific eigenvectors (first, fifth and seventh) to adjust for population stratification. The inflation factor λ was 1.38 before adjustment and 1.00 after adjustment. This adjustment accounts for differences in estimates for SNPs rs6503659, rs17761864 and rs2847281, which had P values of 1.04 × 10–6, 8.06 × 10–7 and 4.40 × 10–6 before adjustment and 8.49 × 10–3, 1.12 × 10–1 and 2.47 × 10–1 after adjustment.

Supplementary Figure 9 QQ plots of observed versus expected P values for the NCI study from a logistic regression model adjusted for age and sex.

The green dots represent results from a model without adjustment for population stratification, whereas the red dots represent results from a model including one study-specific eigenvector to adjust for population stratification. The inflation factor λ was 1.013 before adjustment and 1.01 after adjustment.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Tables 1–9 (PDF 2589 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, C., Wang, Z., Song, X. et al. Joint analysis of three genome-wide association studies of esophageal squamous cell carcinoma in Chinese populations. Nat Genet 46, 1001–1006 (2014). https://doi.org/10.1038/ng.3064

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing