A genome-wide association study identifies two new cervical cancer ...

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A genome-wide association study identifies two new cervical cancer susceptibility loci at 4q12 and 17q12 Yongyong Shi1–4,42, Li Li5,42, Zhibin Hu6,7,42, Shuang Li1,42, Shixuan Wang1,42, Jihong Liu8,42, Chen Wu9,42, Lin He2–4, Jianfeng Zhou10, Zhiqiang Li2–4, Ting Hu1, Yile Chen11, Yao Jia1, Shaoshuai Wang1, Li Wu11, Xiaodong Cheng12, Zhijun Yang5, Ru Yang1, Xiong Li1, Kecheng Huang1, Qinghua Zhang1, Hang Zhou1, Fangxu Tang1, Zhilan Chen1, Jian Shen1, Jie Jiang6,7, Hu Ding10, Hui Xing13, Shulan Zhang14, Pengpeng Qu15, Xiaojie Song16, Zhongqiu Lin17, Dongrui Deng1, Ling Xi1, Weiguo Lv12, Xiaobing Han18, Guangshi Tao19, Lixing Yan20, Zhedong Han20, Zhuang Li5, Xiaoping Miao21, Shandong Pan6,7, Yuanming Shen12, Hui Wang1, Dan Liu1, Ee Gong1, Zheng Li8, Limei Zhou15, Xiaomei Luan14, Chuping Wang22, Qian Song23, Sufang Wu24, Hongbin Xu25, Jiawei Shen2–4, Fulin Qiang26, Gang Ma27, Li Liu26, Xiaojun Chen26, Jibin Liu26, Jiangping Wu28, Yan Shen28, Yang Wen6,7, Minjie Chu6,7, Jiang Yu29, Xiaoxia Hu30, Yujuan Fan31, Hongying He32, Yanming Jiang33, Zhiying Lei34, Cui Liu35, Jianhua Chen2,36, Yuan Zhang37, Cunjian Yi38, Shuangyun Chen39, Wenjin Li2, Daowen Wang10, Zehua Wang37, Wen Di40, Keng Shen41, Dongxin Lin9, Hongbing Shen6,7, Youji Feng24, Xing Xie12 & Ding Ma1 To identify new genetic risk factors for cervical cancer, we conducted a genome-wide association study in the Han Chinese population. The initial discovery set included 1,364 individuals with cervical cancer (cases) and 3,028 female controls, and we selected a ‘stringently matched samples’ subset (829 cases and 990 controls) from the discovery set on the basis of principal component analysis; the follow-up stages included two independent sample sets (1,824 cases and 3,808 controls for follow-up 1 and 2,343 cases and 3,388 controls for follow-up 2). We identified strong evidence of associations between cervical cancer and two new loci: 4q12 (rs13117307, Pcombined, stringently matched = 9.69 × 10−9, per-allele odds ratio (OR)stringently matched = 1.26) and 17q12 (rs8067378, Pcombined, stringently matched = 2.00 × 10−8, per-allele ORstringently matched = 1.18). We additionally replicated an association between HLA-DPB1 and HLA-DPB2 (HLA-DPB1/2) at 6p21.32 and cervical cancer (rs4282438, Pcombined, stringently matched = 4.52 × 10−27, per-allele ORstringently matched = 0.75). Our findings provide new insights into the genetic etiology of cervical cancer. Cervical cancer is the third leading cause of cancer-related mortality among women worldwide1, and approximately 80% of the diagnoses of cervical cancer have occurred in the developing world2. Abundant epidemiological and clinical evidence indicates that persistent infection with high-risk human papillomavirus (HPV) is the major risk factor and is a requirement for the development of cervical cancer3,4; HPV infection is detected in 99.7% of cervical cancer cases5. Cervical cancer

has therefore been traditionally recognized by the World Health Organization as entirely attributable to HPV infection5. Nevertheless, high-risk HPV infection alone has been found to be insufficient to induce tumor progression6. HPV infection is common enough that a majority of sexually active women have been infected more than once during their lifetimes7. However, most infections are transient and cleared spontaneously by the immune response; even persistent infections may clear, and premalignant lesions can also regress8. Moreover, the majority of infected women never develop cancer. Fewer than 4% of individuals infected with HPV develop persistent infections and premalignant lesions (cervical intraepithelial neoplasia (CIN)) and even fewer develop invasive cancer9,10, indicating a complex relationship between host genetics and the virus. Cervical cancer is caused by a combination of genetic heritability and definite external environmental contributions5,11. In Swedish studies, genetic heritability was shown to account for 27% of the effects of factors underlying cervical cancer development11, and the estimate of heritability for cervical cancer was substantially higher than those for colorectal and lung cancer. Therefore, efforts to identify the genetic risk factors of cervical cancer are of great importance, as they will contribute to an overall etiological understanding and provide general insight into host-virus interactions. Previously, common variants in the MHC region were identified as being associated with cervical cancer in Swedish populations, but there was not enough independent validation of these positive loci12. How genetic susceptibility is linked mechanistically to the progression of cervical cancer is still poorly understood. Therefore, further investigations are needed to understand the possible mechanisms of genetic susceptibility to cervical cancer.

A full list of author affiliations appears at the end of the paper. Received 9 April; accepted 7 June; published online 30 June 2013; doi:10.1038/ng.2687

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letters Table 1 The geographical details of samples in the discovery and follow-up stages Initial set

Follow-up 1 (1,824 cases, 3,808 controls) North (2,217)

Region

Province

Center

Hubei Hunan Jiangsu Zhejiang Othera Beijing Henan Shandong Shanxi Otherb Guangxi Guangdong

North

South

Follow-up 2 South (1,987)

Cases (1,364)

Controls (3,028)

Cases (600)

Controls (1,617)

Cases (460)

Controls (968)

Cases (764)

Controls (1,223)

Cases (2,343)

Controls (3,388)

544 175 374 164 23 26 35 3 7 13 – –

504 43 198 453 37 443 303 415 386 246 – –

– – – – – 445 3 9 2 141 – –

– – – – – 1,152 26 120 41 278 – –

307 111 – – 42 – – – – – – –

859 21 – – 88 – – – – – – –

– – – – – – – – – – 368 396

– – – – – – – – – – 1,028 195

452 – 1,070 821 – – – – – – – –

684 – 1,536 1,168 – – – – – – – –

Anhui, Sichuan and Jiangxi. bHebei, Tianjin, Jilin and Liaoning.

We conducted a genome-wide association study (GWAS) of cervical cancer in the Han Chinese population. The initial discovery set for the GWAS included 1,364 cases and 3,028 female controls, and from this initial set we selected a stringently matched samples subset (829 cases and 990 controls) on the basis of principal components analysis (PCA; Online Methods). The follow-up stages involved two independent sample sets (1,824 cases and 3,808 controls for follow-up 1 and 2,343 cases and 3,388 controls for follow-up 2). We also collected 729 samples from individuals with CIN. The characteristics of the samples are listed in Table 1, and additional details are provided in the Online Methods. In the discovery stage, we genotyped 1,374 cases and 3,135 controls using the Affymetrix Axiom Genome-Wide CHB1 Array. After standard quality control (Online Methods), we subjected a total of 563,339 SNPs in 1,364 cases and 3,028 controls to statistical analysis. We used PCA to evaluate the population structure (Supplementary Figs. 1 and 2a). To minimize the potential for population stratification bias, we matched the samples on the basis of the PCA (Online Methods). We generated the stringently matched sample set according to a strict criterion (Online Methods and Supplementary Fig. 2b). The quantile-quantile plots showed some evidence for inflation due to population stratification (genomic inflation factor (λ) = 1.066 and λ standardized to a sample set of 1,000 (λ1,000) = 1.035 for the initial discovery sample set; λ = 1.023 for the stringently matched samples; Online Methods and Supplementary Fig. 3)13,14. We performed a GWAS analysis using logistic regression with PCA-based correction for both the initial discovery sample set and the stringently matched samples (Online Methods); we selected SNPs that were consistently significant in both sample sets (P ≤ 5 × 10−5 in the initial discovery set and P ≤ 10−4 in the matched samples; Online Methods) for follow-up 1 (Fig. 1 and Supplementary Figs. 4 and 5). 7 6 –log10 P

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aShanghai,

Center (1,428)

In total, 41 SNPs showed consistently significant associations in the analysis of the discovery-stage sets (Online Methods). Among these SNPs, we selected 22 representative SNPs for follow-up 1 and ignored the other 19 because of high linkage disequilibrium (LD; r2 ≥ 0.8) with at least 1 of the 22 representative SNPs (Online Methods and Supplementary Table 1). In follow-up 1, 13 of the 22 SNPs were nominally statistically significant in 1,824 cases and 3,808 controls, and we then performed a meta-analysis to combine the results from the northern Han, central Han and southern Han Chinese data sets (Pmeta-analysis, follow-up 1 < 5.0 × 10−2; Online Methods and Supplementary Table 2). Thus, we genotyped these 13 SNPs in follow-up 2 with another 2,343 cases and 3,388 controls (Supplementary Table 3). By combining the results from all three stages, we achieved genome-wide significant associations (P < 5.0 × 10−8) for 11 SNPs, including 1 SNP at 4q12, 1 SNP at 17q12 and 9 SNPs at 6p21.32 (Supplementary Tables 4 and 5). The association of rs13117307 (Pcombined, stringently matched = 9.69 × 10 −9, per-allele OR stringently matched = 1.26) in 4q12 was in an intronic region of EXOC1 (Table 2, Fig. 2a and Supplementary Table 6). Controlling for rs13117307, stepwise logistic regression analysis revealed that there were no additional association signals in this region (Supplementary Table 7). The protein product of EXCO1 combines with seven other subunits (the products of EXCO2–EXCO8) to make up the exocyst complex, which facilitates the regulated exocytosis of membrane activity, vesicle transport machinery and cellular migration and secretion 15–18. The exocyst complex is also associated with the host innate immune response against DNA antigens of viral infection19. Several lines of evidence have suggested that the CD8 + T cell–mediated immune response is important in HPV infection and virus-associated neoplasia 20,21. Association of the exocyst complex with the NEF protein probably has an important role in downregulating the gene encoding MHC-I22 and modulating T-cell signaling

5 4 3 2 1 0 Chr1 Chr13

Chr2 Chr14

Chr3 Chr15

Chr4

Chr5

Chr16

Chr6 Chr17

Chr7 Chr18

Chr8

Chr9

Chr19

Chr20

Nature Genetics VOLUME 45 | NUMBER 8 | AUGUST 2013

Chr10 Chr21

Chr11 Chr22

Chr12 ChrX

Figure 1 Genome-wide association results of cervical cancer in Han Chinese individuals. Scatter plot of P values on the –log10 scale for 563,339 SNPs in the matched discovery set (1,305 cases and 1,444 controls; Online Methods). The red line represents P = 5.0 × 10−8, and the blue line represents P = 1.0 × 10−4. Chr, chromosome.

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letters Table 2 Association results for four SNPs in the GWAS, case-control validation and combined samples SNP

Chr.

Position

rs13117307 4q12

rs8067378

rs4282438

rs9277952

Locus

MA

56,446,497 EXOC1

17q12

T

35,304,874 GSDMB

6p21.32

6p21.32

33,312,252 Intergenic

MAF (case/control)

Stringently matched discovery study Follow-up 1d Follow-up 2 Combined resultsstringently matchede Stringently matched discovery study Follow-up 1d Follow-up 2 Combined resultsstringently matchede Stringently matched discovery study Follow-up 1d Follow-up 2 Combined resultsstringently matchede Stringently matched discovery study Follow-up 1d Follow-up 2 Combined resultsstringently matchede

G

33,180,150 HLA-DPB2

Stage

G

A

P 0.00497c

0.13/0.10

1.47 × 10−4 0.00103 9.69 × 10−9 0.00266c 0.00436 6.72 × 10−5 2.00 × 10−8 4.68 × 10−9, c 4.29 × 10−11 1.40 × 10−10 4.52 × 10−27 8.94 × 10−5, c 0.046 1.98 × 10−6 2.31 × 10−9

0.13/0.11 0.27/0.22 0.28/0.25 0.35/0.44 0.35/0.41 0.41/0.47 0.41/0.46

Qa

OR

95% CI

1.34 1.29 1.21 1.26 1.27 1.14 1.19 1.18 0.66 0.75 0.77 0.75 0.76 0.92 0.83 0.85

1.09–1.65 1.13–1.47 1.08–1.36 1.16–1.36 1.09–1.49 1.04–1.25 1.09–1.30 1.11–1.25 0.58–0.76 0.69–0.82 0.71–0.84 0.71–0.79 0.67–0.87 0.85–1.00 0.77–0.90 0.81–0.90

I 2, b

0.85

0.00

0.61

0.00

0.17 37.11

0.08 52.63

statistic for the combined results. bI2 value for the combined results. cPCA-adjusted P values. dMeta-analysis of follow-up 1. eCalculated with the results of the stringently matched discovery study. MA, minor allele; MAF, minor allele frequency; OR, odds ratio for the minor allele; 95% CI, 95% confidence interval; Chr., chromosome.

pathways23,24. It is probable that the exocyst complex proteins are key effectors of NEF-mediated enhancement of nanotube formation and microvesicle secretion25. Moreover, fusion of a NEF mutation with the HPV type-16 protein E7 induces an anti-E7 CD8 + cytotoxic T-lymphocyte response that correlates with protection against HPV-related tumors26. The interruption of the balance between the 10

r2

–log10 P

0.8 0.6 0.4 0.2

–9

Pcombined, stringently matched = 9.69 × 10

b 10

100 80

6

60

4

40

2

20

0

0

r2 rs8067378

8 6

npg

56.30

56.35

EXOC1

56.40

56.50

2

20

0

0 ZPBP2

GRB7

56.55

35.20

35.25

–log10 P

15

80 60 40

10

20

5

0

0 HLA–DPA1

HLA–DPB2

COL11A2 RING1

HLA–DPB1

VPS52

33.15

33.20

Position on chr6 (Mb)

35.40

MED24

35.45

33.25

33.30

r2


8

–9

0.8 0.6 0.4 0.2

6

100 80 60

4

40

2

20

0

0 COL11A2 RING1

HLA–DPB2

RXRB

B3GALT4

HSD17B8

33.20

33.25

VPS52 PFDN6 RPS18

SLC39A7

HSD17B8

33.10

35.35

rs9277952

HLA–DPB1

1 gene omitted

RXRB SLC39A7

33.05

PSMD3

Recombination rate (cM/Mb)

0.8 0.6 0.4 0.2

20

HLA–DOA

35.30

d 10

100 Recombination rate (cM/Mb)

r2

–27


BRD2

CSF3


rs4282438

25

GSDMA

SNORD124

–log10 P

30

GSDMB ORMDL3


c

60 40

CEP135

56.45

80

4

IKZF3 LOC644145

0.8 0.6 0.4 0.2

–8


100 Recombination rate (cM/Mb)

rs13117307 8

exocyst complex and T-cell signaling pathways may be important for the progression of cervical cancer. The most significant SNP in 17q12, rs8067378 (Pcombined, stringently matched = 2.00 × 10−8, per-allele ORstringently matched = 1.18), is located 9.5 kb downstream of GSDMB (Table 2, Fig. 2b and Supplementary Table 6). Controlling for rs8067378, stepwise

–log10 P

a

Recombination rate (cM/Mb)


aQ

33.30

DAXX

LYPLA2P1

TAPBP

2 genes omitted

ZBTB22

WDR46

33.35

33.40

33.45


Figure 2 Regional plots of the four identified marker SNPs. (a–d) Plots for rs13117307 at 4q12 (a), rs8067378 at 17q12 (b) and rs4282438 (c) and rs9277952 (d), both at 6p21.32. Results (−log10 P) are shown for SNPs in the regions flanking 150 kb on either side of the marker SNPs. The marker SNPs are shown in purple, and the r2 values for the rest of the SNPs are shown in different colors. The genes within the region of interest are annotated and indicated by arrows. The association results of both genotyped (circles) and imputed (Xs) SNPs (Online Method) in the matched discovery set (1,305 cases and 1,444 controls) and the combined results of four loci in the stringently matched subset are also shown.

920


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letters logistic regression analysis revealed no additional association signals (Supplementary Table 7). It has been previously reported that human GSDM-family genes may be involved in cancer development and progression27. Among these gene family members, GSDMB (encoding the cancer-associated gasdermin-like protein (GSDML)) is expressed in human cancer tissues, including gastric, hepatic and cervical cancers28,29. GSDML is expressed in the nuclei at higher levels in cervical cancer than in adjacent cancer and corresponding non-neoplastic tissues. Moreover, ectopic expression of GSDML increased the growth of cervical cancer cells in vitro, whereas inhibition of its endogenous expression decreased cell proliferation, suggesting that GSDML can promote the proliferation of cervical cancer cells and may be correlated with the development of cervical cancer29. Furthermore, GSDM, which is regulated by TGF-β signaling, shows apoptotic activity and is expressed in the pit cells of human epithelium tissue, suggesting that GSDM and TGF-β signaling form a regulatory pathway that directs noncancerous cells to apoptose30. On 6p21.32, within the MHC region, nine significant SNPs (4.52 × 10−27 < Pcombined, stringently matched < 2.31 × 10−9) are located within a 180-kb region that includes HLA-DPB1/2 and HLA-DPA1. The HLA-DPs belong to the HLA class-II molecules that form heterodimers on the cell surface and present antigens to CD4+ T lymphocytes. HLA-DPs are highly polymorphic, especially in exon 2, which encodes antigen-binding sites. Stepwise logistic regression identified two independent associations at rs4282438 (Pcombined, stringently matched = 4.52 × 10−27, per-allele ORstringently matched = 0.74; Table 2 and Fig. 2c) and rs9277952 (Pcombined, stringently matched = 2.31 × 10−9, per-allele ORstringently matched = 0.85; Table 2 and Fig. 2d). After conditioning on these two SNPs, the rest of the SNPs in this region showed no significance (P > 0.05; Supplementary Table 7). We investigated the pairwise LD between these two SNPs and tagging SNPs for HLA alleles (HLA-A, HLA-B, HLA-C, HLA-DQ and HLA-DR) in the HapMap CHB population31 (Online Methods and Supplementary Table 8) and found that rs4282438 was in strong LD with one tag SNP (rs6937034) for HLA-DQB*0402 (r2 = 0.924) and rs9277952 was in moderate LD with tag SNPs for HLA-DQB*0402 (rs6937034) and HLA-DRB*0410 (rs3130267; r2 > 0.2 for both). As researchers in a previous study did not include the HLA-DP genes in their investigation32, we genotyped HLA-DPA1 and HLA-DPB1 alleles by directly sequencing exon 2 (Online Methods). Our analysis revealed that HLA-DPA1*0103, HLA-DPA1*0401, HLA-DPB1*03:01 and HLA-DPB1*04:01 were associated with susceptibility to cervical cancer (P = 2.72 × 10−3, OR = 1.18; P = 6.35 × 10−4, OR = 1.78; P = 2.91 × 10−2, OR = 1.29; and P = 9.57 × 10−3, OR = 1.29, respectively), whereas HLA-DPA1*0202 and HLA-DPB1*05:01 showed protective effects (P = 8.01 × 10−6, OR = 0.793; and P = 2.38 × 10−9, OR = 0.714, respectively; Supplementary Table 9). These findings indicate that HLA alleles are probably associated with the tumorigenesis of cervical cancer. The expression of the candidate genes in cases and controls is listed in Supplementary Table 10, and the results of expression quantitative trait locus (eQTL) analysis between cervical cancer susceptibility alleles and the expression levels of the candidate genes are shown in Supplementary Table 11. By genotyping the additional 729 samples from individuals with CIN, we found that the 11 genome wide–significant SNPs in 4q12, 17q12 and 6p21.32 were also significantly associated with CIN, as well as CIN plus cervical cancer (P < 0.05; Supplementary Table 12), probably suggesting that those regions confer similar risk to patients with CIN as compared to patients with cervical cancer. Additionally, several previous reports have described the association of a series of candidate genes with the progression of cervical cancer Nature Genetics VOLUME 45 | NUMBER 8 | AUGUST 2013

(Supplementary Table 13), including HLA-DQB1, HLA-DRB1, HLA-DPB1, TP53, TNFA and FASL, among others. Some of these genes are regarded as essential factors of carcinogenesis. Of 1,445 variants identified in these previous studies, we found that 103 SNPs had PCA-adjusted P < 0.05. However, none of these results has ever indicated a possible involvement of the susceptibility loci at 4q12 and 17q12 (Supplementary Table 13). In summary, our GWAS of cervical cancer in the Han Chinese population identified two new cervical cancer susceptibility loci at 4q12 and 17q12. We also confirmed the previously reported association between susceptibility loci at 6p21.32 and cervical cancer. The identification of susceptibility loci in the EXOC1 and GSDMB regions, as well as in the HLA-DP alleles, suggests an essential role for T cell–mediated immune responses or tumor-cell proliferation, strengthening the hypothesis that inherited immunological and carcinogenic factors are prominent in determining the risk for cervical cancer, probably by affecting the mechanisms involved in the persistent infection and integration of HPV. URLs. GLOBOCAN 2008 database, International Agency for Research on Cancer, World Health Organization, http://www-dep.iarc.fr/; R, http://www.r-project.org/; PLINK, http://pngu.mgh.harvard. edu/~purcell/plink/; LocusZoom, http://csg.sph.umich.edu/locus zoom/; EIGENSTRAT, http://genepath.med.harvard.edu/~reich/ Software.htm; Haploview program, http://www.broadinstitute.org/ scientific-community/science/programs/medical-and-populationgenetics/haploview/haploview; The International HapMap project, http://hapmap.ncbi.nlm.nih.gov/; SHEsis, http://analysis2.bio-x.cn/ SHEsisMain.htm; 1000 Genomes Project, http://www.1000genomes. org/; Gene Expression Omnibus (GEO), http://www.ncbi.nlm.nih. gov/geo/; GTEx (Genotype-Tissue Expression) eQTL Browser, http:// www.ncbi.nlm.nih.gov/gtex/GTEX2/gtex.cgi; FIGO Global Guidance for Cervical Cancer Prevention and Control, http://www.figo.org/files/ figo-corp/English_version.pdf. Methods Methods and any associated references are available in the online version of the paper. Note: Supplementary information is available in the online version of the paper. Acknowledgments This study was funded by the Fund of the Key Basic Research and Development Program Foundation of China (973 Program) from the Ministry of Science and Technology of China (2009CB521808 and 2013CB911304), grants from the National Natural Science Foundation of China (81230038, 81025011, 81090414, 30973472, 81071663, 81130022, 81121001, 81230052, 81272302, 31000553, 81172464 and 81101964), Key Projects in the National Science and Technology Pillar Program during the Eleventh Five-Year Plan Period (2008BAI57B01), the High-Tech Research and Development Program of China (863 Program) (2012AA020801 and 2012AA02A515) and the Fund of the Key Laboratory of Cancer Invasion and Metastasis from Hubei Province and Tongji Hospital (HB001 and xkdy28). This work was also partially supported by the Hubei Research and Development Program (2008BCC005, 2009BCC001 and 2010BCB006), the Program for Changjiang Scholars and Innovative Research Team in University (IRT1025), the Foundation for the Author of National Excellent Doctoral Dissertation of China (201026), the Program for New Century Excellent Talents in University (NCET-090550) and the Shanghai Rising-Star Program (12QA1401900). We thank all participants recruited for this study. We would like to thank S. Liu, G. Wu, Z. Wang, S. Xie, S. Chen, Q. Wu, Y. Lu, B. Cao, Y. Li, Q. Chen, D. Zhu, M. Gong, S. Sun, Y. Wang, Y. Qin, R. Yang, J. Feng, T. Wang, L. Shi, J. Jiang, F. Rong, W. Zhou, M. Qian, X. Wu, X. Xia, Y. Yan, Y. Fan, M. Cao, C. Sun, Q. Ling, J. Yang, B. Zhou, Z. Zeng, L. Tang, L. Yu, Y. Han, J. Zhou, Y. Fang, P. Chen and Y. Meng for their useful help. AUTHOR CONTRIBUTIONS D.M. took full responsibility for the study, especially in conceiving, designing and supervising the research together with Y. Shi and X.X. S.L., L. Li, Z. Hu,

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letters Shixuan Wang and J.Z. participated in the study design. D.M., S.L., X.X., L. Li, Z. Hu and Shixuan Wang supervised the diagnosis of patients and subject recruitment. Y. Shi and S.L. supervised the experiments and data analyses. Y. Shi, Zhiqiang Li, X. Li, S.L., T.H. and Z. Han performed statistical analyses, and the results were interpreted by D.M., S.L., Y. Shi and X.X. D.M., X.X., L.H., Y. Shi, L. Li, H.S., Z. Hu, J.Z., Shixuan Wang, S.L., Jihong Liu, D. Lin, C. Wu, Y.C., H. Xing, S.Z., P.Q., Y. Fan, W.D., K.S., X.S., D.W., H.D., Z. Lin, D.D., L.X., X. Cheng, W. Lv, X. Han, G.T., X.M., H.W., F.Q., L. Liu, X. Chen, Jibin Liu, J.W., Yan Shen, L.Y., Z.Y., J.Y., G.M., X. Hu, Y. Feng, H.H., Y. Jiang, Z. Lei, C.L., Y.Z., Z.W., C.Y., S.C., Yuanming Shen, S. Wu, H. Xu, C. Wang and Q.S. contributed reagents, materials and analysis tools and provided samples from different hospitals. S.L., Y. Shi, T.H., Y. Jia, Shaoshuai Wang, R.Y., Z. Hu, L.W., X. Li, K.H., Z.C., Jian Shen, Q.Z., H.Z., F.T., E.G., D. Liu, J.J., Wenjin Li, Jiawei Shen, S.P., Zhuang Li, L.Z., X. Luan, Y.W., M.C., J.C., Jihong Liu and Zheng Li performed the experiments. D.W., X.M. and H.D. provided technical support. The manuscript was drafted by S.L. and Zhiqiang Li under the supervision of D.M., Y. Shi and X.X. All authors critically reviewed the article and approved the final manuscript. COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests.

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1Department

of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China. 3Shanghai Genome Pilot Institutes for Genomics and Human Health, Shanghai, China. 4Changning Mental Health Center, Shanghai, China. 5Department of Gynecologic Oncology, Guangxi Province Tumor Hospital, Nanning, China. 6State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China. 7Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, School of Public Health, Nanjing Medical University, Nanjing, China. 8Department of Gynecologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. 9State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China. 10Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 11Department of Gynecologic Oncology, Hunan Province Tumor Hospital, Changsha, China. 12Women’s Hospital, School of Medicine Zhejiang University, Hangzhou, China. 13Department of Obstetrics and Gynecology, Xiangyang Central Hospital, First Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China. 14Department of Obstetrics and Gynecology, Shengjing Hospital, China Medical University, Shenyang, China. 15Tianjin Central Hospital for Gynecology and Obstetrics, Tianjin, China. 16Wuhan Medical Center for Women and Children, Wuhan, China. 17Department of Gynecologic Oncology, the Second Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. 18Department of Obstetrics and Gynecology, the First Affiliated Hospital, Medical School of Xi’an Jiaotong University, Xi’an, China. 19Department of Obstetrics and Gynecology, Second Xiangya Hospital, Central South University, Changsha, China. 20Blood Center of Zhejiang Province, Key Laboratory of Blood Safety of Ministry of Health, Hangzhou, China. 21Ministry of Education Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 22Department of Obstetrics and Gynecology, Huangshi Central Hospital, Huangshi, China. 23Department of Obstetrics and Gynecology, Xiaogan Central Hospital, Xiaogan, China. 24Department of Gynecology, Obstetrics and Gynecology, Hospital of Fudan University, Shanghai, China. 25Department of Gynecology and Obstetrics, People’s Hospital of Shenzhen, Shenzhen, China. 26Nantong Tumor Hospital, Nantong, China. 27Department of Obstetrics and Gynecology, Guangxi Maternal and Child Health Hospital, Nanning, China. 28Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing, China. 29Maternal and Child Center, No. 181 Hospital of the People’s Liberation Army, Guilin, China. 30Department of Obstetrics and Gynecology, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China. 31Department of Obstetrics and Gynecology, the First Affiliated Hospital of Guangxi University of Medical Science, Nanning, China. 32Department of Obstetrics and Gynecology, Liuzhou Worker Hospital, Liuzhou, China. 33Department of Obstetrics and Gynecology, the People’s Hospital of Liuzhou, Liuzhou, China. 34Department of Obstetrics and Gynecology, Affiliated Hospital of Youjiang Medical College for Nationalities, Baise, China. 35Department of Obstetrics and Gynecology, The Red Cross Hospital of Yulin City, Yulin, China. 36Shanghai Institute of Mental Health, Shanghai, China. 37Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 38Department of Obstetrics and Gynecology, the First People’s Hospital of Jingzhou City, Jingzhou, China. 39Department of Gynecologic Oncology, Taihe Hospital of Shiyan, Shiyan, China. 40Department of Obstetrics and Gynecology, Renji Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China. 41Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. 42These authors contributed equally to this work. Correspondence should be addressed to D.M. ([email protected]), X.X. ([email protected]) or Y. Shi ([email protected]). 2Bio-X

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Samples. We performed a three-stage case-control analysis, including an initial discovery stage and two stages of follow-up (Table 1). The samples were enrolled from multiple collaborating hospitals and institutes in China. All Han Chinese cases were histopathologically diagnosed as having cervical squamous cell carcinoma or adenocarcinoma by at least two local experienced pathologists according to standard protocols (see URLs). Subjects with rare pathological types, complicating disease or previous malignant disease were excluded. All women considered to be healthy did not develop precancerosis or cervical cancer and had no family history of cervical cancer; these controls were randomly recruited from local communities or had received routine physical examinations in local hospitals. In the discovery stage, 1,364 individuals providing cervical cancer samples (mean age ± s.d., 49.04 ± 10.94 years) were mainly from central China (Hubei, Hunan, Zhejiang and Jiangsu), and the 3,028 controls providing samples (mean age ± s.d., 49.33 ± 10.33 years) were mainly from central and northern China (Hubei, Zhejiang, Jiangsu, Henan, Shandong, Beijing and Shanxi). The majority of these individuals were enrolled from Tongji Hospital, Women’s Hospital, the School of Medicine of Zhejiang University, Ministry of Education Key Laboratory of Modern Toxicology, Nanjing Medical University and Bio-X Institutes. The first follow-up (follow-up 1) included 1,824 cases (mean age ± s.d., 46.97 ± 9.59 years) and 3,808 controls (mean age ± s.d., 31.83 ± 9.34 years) from three independent sets: 600 cases and 1,617 controls from the northern Han Chinese population (Beijing, Tianjin, Jilin, Liaoning, Hebei, Shanxi, Henan and Shandong), 460 cases and 968 controls from the central Han Chinese population (Hubei, Hunan, Anhui, Shanghai and Jiangxi) and 764 cases and 1,223 controls from the southern Han Chinese population (Guangdong and Guangxi). The independent samples in the second follow-up stage (follow-up 2) consisted of 2,343 cases (mean age ± s.d., 50.97 ± 11.61 years) and 3,388 controls (mean age ± s.d., 48.76 ± 12.42 years) from the central Han Chinese population (Jiangsu, Zhejiang and Hubei). Additionally, 729 subjects with CIN (mean age ± s.d., 40.57 ± 8.43 years) were recruited mainly from central China (Hubei, Jiangsu and Zhejiang). All case and control subjects were unrelated ethnic Han Chinese. This study was approved by each participating center’s Institutional Ethical Committee and was conducted according to the principles of the Declaration of Helsinki. Written informed consent was obtained from all subjects. DNA extraction. Ethylenediaminetetraacetic acid disodium salt (EDTA2Na)-anticoagulated venous blood samples were collected from all participants. Genomic DNA was extracted from peripheral blood by standard procedures using Flexi Gene DNA kits (Qiagen) and the QuickGene DNA whole-blood kit (Fujifilm). The extracted DNA was diluted to working concentrations of 50 ng/µl for genome-wide genotyping and 15–20 ng/µl for the validation study. GWAS genotyping and quality control. Genome-wide genotyping was performed using the Axiom Genome-Wide CHB1 Array (Affymetrix). Qualitycontrol filtering of the GWAS data required a dish quality control (DQC) value >0.82 for further data analysis. DQC is a metric developed by Affymetrix that takes both interchannel and intrachannel signal separation and spread into account and is the recommended quality-control metric for Axiom arrays. A total of 4,576 arrays were used in the GWAS, including 45 arrays for the designed duplication of randomly selected samples and 67 arrays for rehybridization because DQC had failed. For the 45 pairs of designed duplicate samples, the genotyping reproducibility was >99.7%, and of the 67 rehybridization arrays, 27 arrays still failed DQC. Genotype data for the remaining 4,437 arrays were generated with Axiom Genotyping Algorithm v1 (Axiom GT1). For sample filtering, arrays with generated genotypes for 0.25, the member of the pair with the lower call rate was excluded from the analysis (30 samples); 1,364 cases and 3,028 controls were retained for further analysis. For SNP filtering (after sample filtering), SNPs with call rates