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. Author manuscript; available in PMC: 2014 Nov 1.
Published in final edited form as: Prostate. 2013 Sep 9;73(15):1623–1635. doi: 10.1002/pros.22629

Evaluation of Reported Prostate Cancer Risk-Associated SNPs From Genome-Wide Association Studies of Various Racial Populations in Chinese Men

Rong Na 1, Fang Liu 1,2,3, Penyin Zhang 2,3, Dingwei Ye 4,5, Chuanliang Xu 6, Qiang Shao 7, Jun Qi 8, Xiang Wang 1, Zhiwen Chen 9, Meilin Wang 10,11, Dalin He 12, Zhong Wang 13, Fangjian Zhou 14,15, Jianlin Yuan 16, Xin Gao 17, Qiang Wei 18, Jin Yang 19, Yang Jiao 8, Jun Ou-Yang 20, Yao Zhu 4,5, Qijun Wu 21, Hongyan Chen 2,3, Daru Lu 2,3, Rong Shi 22, Xiaoling Lin 1,2,3, Haowen Jiang 1, Zhong Wang 23, Deke Jiang 2,3, Jielin Sun 23, S Lilly Zheng 23, Qing Ding 1, Zengnan Mo 24,25, Yinghao Sun 6,*, Jianfeng Xu 1,2,3,23,**
PMCID: PMC3928594  NIHMSID: NIHMS549731  PMID: 24038036

Abstract

BACKGROUND

Several genome-wide association studies (GWAS) of prostate cancer (PCa) have identified many single nucleotide polymorphisms (SNPs) that are significantly associated with PCa risk in various racial groups. The objective of this study is to evaluate which of these SNPs are associated with PCa risk in Chinese men and estimate their strength of association.

METHODS

All SNPs that were reported to be associated with PCa risk in GWAS from populations of European, African American, Japanese, and Chinese descent were evaluated in 1,922 PCa cases and 2,175 controls selected from the Chinese Consortium for Prostate Cancer Genetics (ChinaPCa). A logistic regression analysis was used to estimate allelic odds ratios (ORs) of these SNPs for PCa.

RESULTS

Among the 53 SNPs, 50 were polymorphic in the Chinese population. Of which, 10 and 24 SNPs were significantly associated with PCa risk in Chinese men at P < 0.001 and <0.05, respectively. These 24 significant SNPs included 17, 5, and 2 SNPs that were originally discovered in European, Japanese, and Chinese descent, respectively. The estimated ORs ranged from 1.10 to 1.49 and the direction of association was consistent with previous studies. When ORs were estimated separately for PCa with Gleason score ≤7 and ≥8, a marginally significant difference in ORs was found only for two of the 24 SNPs (P = 0.02 and 0.04).

CONCLUSION

About half of PCa risk-associated SNPs identified in GWAS of various populations are associated with PCa risk in Chinese men. Information on PCa risk-associated SNPs and their ORs may facilitate risk assessment of PCa risk in Chinese men.

Keywords: prostate cancer, SNPs, genome-wide association, Chinese

INTRODUCTION

With an estimated 914,000 new cases and 258,000 deaths every year globally [1], prostate cancer (PCa) is a major public health concern worldwide. The highest incidence and mortality rates are found in the Western developed countries, whereas the lowest incident and mortality rates are reported in Asians. In China, while the incidence is relatively low, it is increasing rapidly, especially in developed metropolitan areas [2]. For instance, in Shanghai, the incidence of PCa rose from 1.6/100,000 person-years in 1972 to 7.7/100,000 person-years in 2000 [3].

The etiology of PCa is largely unknown and is likely to be multifactorial. Genetic susceptibility is a major risk factor for PCa, estimated to account for 42% variation of the disease [4]. Since 2006, more than 50 genomic regions that harbor inherited variants associated with PCa risk have been identified from multiple genome-wide association studies (GWAS) in populations of European, African American, Japanese, and Chinese descent [5–26]. These PCa risk-associated variants are common in respective populations and typically confer modest to moderate risk, with estimated odd ratios (ORs) ranging from 1.04 to 1.82. However, they have a stronger cumulative effect on PCa risk [27–30]. Risk assessment using combinations of these genetic variants are able to significantly discriminate individual’s risk to PCa, independent of family history and levels of prostate specific antigen (PSA). Several studies have demonstrated the clinical utility of these genetic variants in predicting outcomes of initial and repeat prostate biopsies in populations of European descent [31–32].

The clinical utility of genetic variants in assessing genetic risk to PCa relies strongly on the validity of PCa risk associated single nucleotide polymorphisms (SNPs) and their magnitude of risk to PCa. While PCa risk-associated SNPs and their ORs are well established in populations of European descent, there is limited information regarding these two critical components in the Chinese population. The objective of this study is to evaluate which of the ~50 known PCa risk-associated SNPs identified from various populations are associated with PCa risk in Chinese men and to estimate their strength of association.

METHODS

Study Subjects

The subjects included in this study are part of a Chinese Consortium for Prostate Cancer Genetics (ChinaPCa), which was initiated in 2010 and was designed to understand genetic determinants of PCa risk in Chinese men. Due to relatively low incidence of PCa in China and the lack of a national cancer registry, a consortium effort to recruit PCa patients was necessary to achieve a sufficiently large sample size. Any hospital in China that was interested in joining ChinaPCa and was approved by the Institutional Ethic Review Board was welcomed to the consortium. Inclusion criteria were pathology confirmation of PCa (prevalent or incident cases), availability of blood samples, and Han Chinese ethnicity. Because PSA screening for PCa is uncommon in China, most of the PCa patients were diagnosed due to PCa related symptoms. The ChinaPCa also included control subjects from two community-based studies, including male subjects from Taizhou, Jiangsu Province and the Pudong District in Shanghai. Both populations are located in the South-East of China. Control subjects were defined as men without a diagnosis of PCa. Age and other factors were not matched for PCa patients. Serum levels of prostate-specific antigen (PSA) are available for all control subjects.

This study was approved by the Institutional Review Board at Fudan University and each participating hospital.

SNP Selection and Genotyping

All established PCa risk-associated SNPs from published GWAS of PCa in populations of European, African American, Japanese, and Chinese descent were selected for study. They include 42, 1, 8, and 2 PCa risk-associated SNPs in populations of European [5–22], African [23], Japanese descent [24–25], and Chinese descent [26], respectively. The information of these SNPs is presented in Supplementary Table I.

Two subsets of cases and controls from ChinaPCa were evaluated for these SNPs. The first subset included 1,417 cases and 1,008 controls from Pudong District, Shanghai, as part of a GWAS using Illumina Human OmniExpress Bead Chips [26]. For SNPs that were not included in the GWAS chip, they were imputed based on haplotype data from the 1000 Genomes Project CHB + JPT subjects (Phase I integrated data version 3, released Mar 2012) using the IMPUTE2.2.2 computer program. A posterior probability of >0.90 was applied to call imputed genotypes. Genotype data were not available for three of the targeted SNPs (rs16902094, rs620861, and rs5945619) in this subset of subjects because they were not included in the GWAS chip and were not successfully imputed.

The second subset included 505 PCa cases and 1,167 controls from Taizhou, Jiangsu Province. SNP genotyping in this subset was performed using MassARRAY iPLEX (Sequenom, Inc., San Diego, CA) at the Fudan-VARI Center for Genetic Epidemiology at Fudan University. Duplicates from two subjects and two water samples (negative controls) were included in each 96-well plate for genotyping quality control. Four SNPs (rs10993994, rs7127900, rs130067, and rs10936632) were not evaluated in this subset of subjects because they failed quality control (missing data >5% or Hardy–Weinberg equilibrium P-value <0.001).

Statistical Methods

The allelic ORs and 95% confidence intervals (CI) for each SNP were estimated first in each of the two subsets of cases and controls using a logistic regression analysis assuming an additive model. A meta-analysis was then performed to obtain the pooled estimate of OR and its CI. Heterogeneity of OR between the two subsets was tested using the Q-statistic for heterogeneity and the I2 statistic, which measures the proportion of total variance in estimated ORs due to heterogeneity. The I2 statistic provides the degree of heterogeneity while the Q-statistic only provides the presence or absence of heterogeneity. A value of the I2 statistic >50% indicates a high degree of heterogeneity in estimated ORs between study populations. If there was evidence for heterogeneity in OR estimates (P < 0.05 for Q-statistic or I2 statistic >50%), a random effect was used for meta-analysis to calculate the pooled OR and 95% CI; otherwise, a fixed effect was used. Forest plots are provided to visually present the OR and 95% CI for each SNP. Two criteria were used to determine the significance of SNPs in this study. The first is a P of 0.001 to ensure a Type I error of 5% in the study when taking 50 independent tests into consideration. The second is a liberal criterion, with P of 0.05.

OR and 95% CI were also estimated separately for cases with Gleason score ≤7 and ≥8 by comparing to the control subjects.

All analyses and forest plots in this study were performed using R software.

RESULTS

The characteristics of study subjects in the two subsets are presented in Table I. Most of the PCa patients (79.8%) in this study had clinically significant disease, defined as serum PSA levels ≥20 ng/ml, T3 or higher, N+, M+, or Gleason score ≥8. In control subjects, 49 (2%) had serum PSA ≥4 ng/ml; they were not included in the association test.

TABLE I.

Characteristics of Study Subjects

Subset 1 (GWAS) Subset 2
Variables Cases (N = 1,417) Controls (N = 1,008) Cases (N = 505) Controls (N = 1,167)
Age, mean (SD), yeara 71.3 (8.1) 62.1 (10.0) 70.6 (8.3) 67.0 (6.6)
PSA levels, # (%), ng/mlb
  0–3.99 54 (4.0) 965 (95.9) 7 (1.9) 1,141 (99.3)
  4–9.99 187 (14.0) 32 (3.2) 52 (13.8) 6 (0.5)
  10–19.99 305 (22.8) 6 (0.6) 74 (19.7) 2 (0.2)
  20–49.99 312 (23.3) 3 (0.3) 71 (18.9) 0
  50–99.99 187 (14.0) 0 42 (11.2) 0
  ≥100 292 (21.9) 0 130 (34.6) 0
  Missing 80 2 129 18
T-stage, # (%)
  T1 180 (14.7) N/A 20 (6.7) N/A
  T2 547 (44.7) N/A 151 (52.2) N/A
  T3 359 (29.4) N/A 85 (29.4) N/A
  T4 137 (11.2) N/A 33 (11.4) N/A
  TX 194 N/A 216 N/A
N-stage, # (%)
  N0 786 (68.1) N/A 229 (76.3) N/A
  N+ 369 (31.9) N/A 71 (23.7) N/A
  NX 262 N/A 205 N/A
M-stage, # (%)
  M0 832 (65.4) N/A 217 (62.4) N/A
  M+ 440 (34.6) N/A 131 (37.6) N/A
  MX 145 N/A 157 N/A
Gleason score, # (%)
  ≤7 809 (60.1) N/A 184 (55.4) N/A
  ≥8 537 (39.9) N/A 148 (44.6) N/A
  Missing 71 N/A 173 N/A
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a

Age at diagnosis for cases or at recruitment for controls.

b

Serum PSA levels were measured at diagnosis for cases orobtained at recruitment for controls.

Among the 53 SNPs, three SNPs were not polymorphic (rs7210100 reported from African descent, rs4962416 and rs5919432 reported from the European descent) and therefore were not tested for their association with PCa risk. For the 50 polymorphic SNPs, 10 were significantly associated with PCa risk in Chinese men at P < 0.001 (Table II); they included five SNPs originally reported in subjects of European descent, three SNPs originally reported in subjects of Japanese descent, and two SNPs originally reported in subjects of Chinese descent. In addition, 14 additional SNPs were significantly associated with PCa risk in Chinese men at P < 0.05 (Table II); they included 12 SNPs originally reported in subjects of European descent and two SNPs originally reported in subjects of Japanese descent.

TABLE II.

Results of Association Test in Chinese Men for Reported PCa Risk-Associated SNPs From Genome-Wide Association Studies of Populations in European, African, Japanese, and Chinese Descent

Subset1 Subset 2 Meta-analysis
Origin of
GWAS		 Chr SNPs BP Alleles Risk
alleles		 Alele frequency ORa P-value Alele frequency OR P-value P for
Q statistic		 I2 OR P-value
Cases Controls Cases Controls
European 8q24 (Region 2) rs16901979 128,194,098 C/A A 0.336 0.265 1.4 (1.23–1.59) 2.74E−07 0.359 0.256 1.63 (1.38–1.91) 4.84E−09 0.1506 51.6 1.48 (1.34–1.63) 2.33E−14
European 8q24 (Region 1) rs1447295 128,554,220 C/A A 0.196 0.147 1.44 (1.23–1.68) 5.45E−06 0.218 0.153 1.55 (1.28–1.87) 4.60E−06 0.5543 0 1.48 (1.31–1.67) 1.54E−10
European 8q24 (Region 3) rs6983267 128,482,487 T/G G 0.467 0.411 1.28 (1.14–1.44) 3.71E−05 0.519 0.428 1.44 (1.24–1.67) 1.25E−06 0.211 36.08 1.34 (1.22–1.47) 4.55E−10
European 8p21 rs1512268 23,582,408 G/A T 0.324 0.277 1.27 (1.12–1.44) 2.42E−04 0.351 0.271 1.46 (1.24–1.71) 3.42E−06 0.1873 42.48 1.34 (1.21–1.48) 8.26E−09
Chinese 19q13.4 rs103294 59,489,660 T/C C 0.301 0.242 1.36 (1.2–1.56) 3.64E−06 0.292 0.241 1.3 (1.1–1.53) 2.03E−03 0.6465 0 1.34 (1.21–1.48) 3.15E−08
Chinese 9q31.2 rs817826 109,196,121 T/C C 0.107 0.075 1.53 (1.24–1.89) 5.52E−05 0.112 0.081 1.44 (1.13–1.84) 3.64E−03 0.6982 0 1.49 (1.27–1.75) 8.26E−07
Japanese 5p15 rs12653946 1,948,829 C/T T 0.410 0.362 1.23 (1.09–1.39) 6.70E−04 0.421 0.358 1.31 (1.12–1.52) 5.63E−04 0.5472 0 1.26 (1.15–1.38) 1.54E−06
Japanese 13q22 rs9600079 72,626,140 G/T T 0.487 0.444 1.19 (1.06–1.33) 3.96E−03 0.510 0.437 1.34 (1.15–1.55) 1.17E−04 0.2073 37.1 1.24 (1.13–1.36) 3.5E−06
Japanese 6q22 rs339331 117,316,745 T/C T 0.687 0.635 1.28 (1.13–1.44) 9.20E−05 0.685 0.653 1.16 (0.99–1.35) 7.42E−02 0.332 0 1.23 (1.12–1.35) 2.91E−05
European 17q12 rs4430796 33,172,153 T/C A 0.744 0.725 1.12 (0.98–1.28) 9.29E−02 0.766 0.706 1.36 (1.15–1.62) 4.08E−04 0.0733 68.81 1.2 (1.08–1.33) 5.15E−04
European 8q24 (Region 4) rs620861 128,335,673 G/A G 0.610 0.551 1.28 (1.1–1.48) 1.63E−03 1 0 1.28 (1.1–1.48) 1.63E−03
European 2p21 rs1465618 43,407,453 A/G T 0.744 0.714 1.18 (1.04–1.35) 1.35E−02 0.757 0.730 1.15 (0.97–1.36) 1.17E−01 0.7909 0 1.17 (1.05–1.3) 3.54E−03
European 3q23 rs6763931 142,585,523 C/T A 0.367 0.336 1.13 (1–1.28) 4.64E−02 0.371 0.334 1.18 (1.01–1.37) 3.80E−02 0.6844 0 1.15 (1.04–1.26) 4.38E−03
Japanese 10q26 rs2252004 122,834,699 G/T C 0.768 0.739 1.19 (1.02–1.37) 2.23E−02 0.752 0.723 1.16 (0.98–1.37) 8.71E−02 0.8402 0 1.17 (1.05–1.31) 4.42E−03
European 2p15 rs721048 62,985,235 G/A A 0.038 0.028 1.37 (0.98–1.9) 6.58E−02 0.039 0.027 1.44 (0.96–2.16) 7.85E−02 0.8458 0 1.39 (1.08–1.8) 1.14E−02
European 2q31 rs12621278 173,019,799 A/G A 0.749 0.724 1.14 (1–1.3) 5.60E−02 0.743 0.717 1.14 (0.96–1.35) 1.29E−01 0.9806 0 1.14 (1.03–1.26) 1.47E−02
European 17q12 rs11649743 33,149,092 C/T G 0.687 0.671 1.09 (0.97–1.24) 1.60E−01 0.683 0.652 1.15 (0.98–1.34) 9.14E−02 0.6438 0 1.11 (1.01–1.23) 3.15E−02
European 22q13 rs5759167 41,830,156 G/T G 0.730 0.710 1.1 (0.97–1.25) 1.54E−01 0.717 0.688 1.15 (0.97–1.35) 1.01E−01 0.6862 0 1.12 (1.01–1.23) 3.29E−02
European 12q13 rs10875943 47,962,277 T/C C 0.853 0.850 1.06 (0.9–1.24) 4.95E−01 0.887 0.852 1.36 (1.08–1.7) 7.69E−03 0.0774 67.93 1.15 (1.01–1.31) 3.56E−02
European 19q13 rs887391 46,677,464 T/C T 0.605 0.595 1.05 (0.93–1.18) 4.60E−01 0.627 0.581 1.21 (1.04–1.41) 1.42E−02 0.1408 53.9 1.1 (1.01–1.21) 3.66E−02
Japanese 2p24 rs13385191 20,751,746 G/A G 0.461 0.438 1.1 (0.98–1.23) 1.14E−01 0.471 0.447 1.1 (0.95–1.28) 2.04E−01 0.9775 0 1.1 (1–1.21) 4.17E−02
European 7p15 rs10486567 27,943,088 T/C G 0.148 0.127 1.19 (1–1.41) 4.89E−02 0.144 0.134 1.09 (0.88–1.35) 4.39E−01 0.5313 0 1.15 (1–1.31) 4.29E−02
European 7q21 rs6465657 97,654,263 C/T C 0.875 0.859 1.15 (0.97–1.37) 1.00E−01 0.863 0.849 1.13 (0.91–1.39) 2.68E−01 0.8744 0 1.14 (1–1.31) 4.77E−02
European 6q25 rs9364554 160,753,654 C/T C 0.678 0.657 1.11 (0.98–1.26) 8.86E−02 0.685 0.667 1.09 (0.93–1.27) 3.04E−01 0.824 0 1.1 (1–1.21) 4.83E−02
European 10q11 rs10993994 51,219,502 T/C T 0.511 0.484 1.12 (1–1.26) 5.89E−02 1 0 1.12 (1–1.26) 5.89E−02
European 11p15 rs7127900 2,190,150 G/A G 0.895 0.878 1.19 (0.99–1.43) 6.17E−02 1 0 1.19 (0.99–1.43) 6.17E−02
European 8q24 (Region 5) rs10086908 128,081,119 T/C T 0.830 0.820 1.06 (0.91–1.23) 4.85E−01 0.847 0.818 1.23 (1.01–1.5) 4.40E−02 0.2365 28.63 1.12 (0.99–1.26) 7.61E−02
European 4q24 rs7679673 106,280,983 A/C C 0.211 0.186 1.16 (1–1.34) 5.02E−02 0.203 0.198 1.03 (0.86–1.24) 7.63E−01 0.3293 0 1.11 (0.99–1.24) 8.39E−02
European 2p11 rs10187424 85,647,808 A/G T 0.626 0.612 1.05 (0.93–1.18) 4.28E−01 0.654 0.627 1.12 (0.96–1.31) 1.41E−01 0.4944 0 1.08 (0.98–1.18) 1.29E−01
European 6p21 rs130067 31,226,490 T/G G 0.328 0.307 1.1 (0.97–1.24) 1.40E−01 1 0 1.1 (0.97–1.24) 1.40E−01
European 11q13 rs10896449 68,751,243 A/G G 0.097 0.084 1.16 (0.95–1.43) 1.43E−01 0.000 0.038 NA 4.32E−04 1 0 1.16 (0.95–1.43) 1.43E–01
Japanese 6p21 rs1983891 41,644,405 C/T T 0.345 0.332 1.06 (0.93–1.2) 3.89E–01 0.330 0.313 1.08 (0.92–1.27) 3.55E–01 0.8522 0 1.07 (0.96–1.18) 2.13E–01
European 3q26 rs10936632 171,612,796 A/C A 0.269 0.253 1.09 (0.94–1.27) 2.59E–01 1 0 1.09 (0.94–1.27) 2.59E–01
European 5p12 rs2121875 44,401,302 T/G A 0.483 0.473 1.03 (0.91–1.15) 6.50E–01 0.413 0.495 0.72 (0.58–0.89) 1.93E–03 0.0034 88.33 0.94 (0.85–1.05) 2.72E–01
European 19q13 rs8102476 43,427,453 A/G C 0.374 0.360 1.08 (0.96–1.22) 2.08E–01 0.382 0.380 1.01 (0.87–1.18) 8.85E–01 0.5046 0 1.05 (0.96–1.16) 2.84E–01
European 3q21 rs10934853 129,521,063 C/A A 0.441 0.428 1.05 (0.93–1.18) 4.54E–01 0.434 0.421 1.06 (0.91–1.23) 4.79E–01 0.9295 0 1.05 (0.96–1.15) 3.04E–01
European 8p21 rs2928679 23,494,920 G/A A 0.134 0.120 1.15 (0.96–1.37) 1.24E–01 0.124 0.129 0.95 (0.76–1.19) 6.65E−01 0.1982 39.61 1.07 (0.93–1.23) 3.45E−01
Japanese 11q12 rs1938781 58,671,686 T/C G 0.328 0.309 1.08 (0.95–1.22) 2.50E−01 0.322 0.322 1 (0.86–1.18) 9.72E−01 0.4936 0 1.05 (0.95–1.16) 3.51E−01
European 8q24.21 rs16902094 128,320,346 A/G A 0.726 0.713 1.07 (0.9–1.26) 4.55E−01 1 0 1.07 (0.9–1.26) 4.55E−01
European 3p12 rs2660753 87,193,364 C/T T 0.310 0.287 1.09 (0.96–1.24) 1.88E−01 0.290 0.299 0.96 (0.81–1.13) 6.07E−01 0.2229 32.7 1.04 (0.94–1.15) 4.67E−01
European 17q24 rs1859962 66,620,348 T/G G 0.426 0.399 1.12 (1–1.27) 5.20E−02 0.394 0.420 0.9 (0.77–1.04) 1.57E−01 0.0209 81.27 1.03 (0.94–1.13) 5.07E−01
European 9q33 rs1571801 123,467,194 G/T T 0.053 0.050 1.09 (0.83–1.42) 5.40E−01 0.054 0.051 1.05 (0.75–1.46) 7.93E−01 0.8587 0 1.07 (0.87–1.32) 5.23E−01
European 11q13 rs12418451 68,691,995 G/A A 0.052 0.049 1.05 (0.8–1.37) 7.43E−01 0.053 0.065 0.8 (0.58–1.1) 1.67E−01 0.2061 37.45 0.93 (0.76–1.15) 5.24E−01
Japanese 3p11.2 rs2055109 87,550,022 C/T C 0.080 0.069 1.18 (0.94–1.47) 1.44E−01 0.064 0.073 0.87 (0.65–1.17) 3.60E−01 0.1083 61.22 1.06 (0.89–1.26) 5.33E−01
European 12q13 rs902774 51,560,171 G/A G 0.993 0.993 1 (0.49–2.04) 9.94E−01 0.991 0.988 1.41 (0.66–2.99) 3.69E−01 0.5188 0 1.18 (0.7–1.97) 5.36E−01
European 2q37.3 rs2292884 238,107,965 A/G A 0.717 0.714 0.99 (0.87–1.13) 9.25E−01 0.735 0.718 1.09 (0.92–1.29) 3.19E−01 0.3988 0 1.03 (0.93–1.14) 5.96E−01
European 4q22 rs17021918 95,781,900 C/T C 0.662 0.653 1.04 (0.92–1.17) 5.60E−01 0.663 0.661 1.01 (0.86–1.18) 9.10E−01 0.787 0 1.03 (0.93–1.13) 5.96E−01
European 19q13 rs2735839 56,056,435 G/A A 0.391 0.384 1.01 (0.9–1.14) 8.22E−01 0.382 0.377 1.02 (0.88–1.19) 7.70E−01 0.9285 0 1.02 (0.93–1.12) 7.18E−01
European Xp11 rs5945619 51,241,672 A/G T 0.932 0.931 1.02 (0.67–1.55) 9.17E−01 1 0 1.02 (0.67–1.55) 9.17E−01
European 22q13 rs9623117 38,782,065 T/C C 0.040 0.035 1.13 (0.83–1.54) 4.38E−01 0.032 0.038 0.83 (0.55–1.26) 3.86E−01 0.2471 25.36 1.01 (0.79–1.3) 9.18E−01
European 10q26 rs4962416 126,686,862 A/G G Not polymorphic
African 17q21.32 rs7210100 44,791,748 G/A A Not polymorphic
European Xq12 rs5919432 66,938,275 A/G A Not polymorphic
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a

Allelic odds ratio (OR) and 95% confidence interval (95% CI).

The OR of each SNP for PCa was estimated for the risk alleles identified in previous studies. For the 24 significant SNPs, the estimated ORs in the Chinese population were all >1.0 (i.e., consistent with previous studies) and ranged from 1.10 to 1.49 (Table II and Fig. 1). The top three SNPs that confer the strongest risk for PCa in the Chinese population were rs16901979 at 8q24 (Region 2), OR = 1.48, 95% CI: 1.34–1.63, P-value = 2.33 × 10−14, rs1447295 at 8q24 (Region 1), OR = 1.48, 95% CI: 1.31–1.67, P-value = 1.54 × 10−10, and rs817826 at 9q31, OR = 1.49, 95% CI: 1.27–1.75, P-value = 8.26 × 10−7. For the remaining 26 SNPs that were not significantly associated with PCa risk in the Chinese population, all but two SNPs had OR estimates >1.0.

Fig. 1.

Fig. 1

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Forest plots of PCa risk-associated SNPs identified from GWAS of various populations with PCa risk in Chinese men.

We also estimated ORs for PCa separately for high-grade PCa (Gleason score ≥8) and for low-grade PCa (Gleason score ≤7; Table III and Fig. 2). For the 24 SNPs that were significantly associated with PCa risk in the meta-analysis, significantly different ORs between the two types of PCa were found only for two SNPs (P < 0.05). One of these two SNPs was rs620861 at 8q24 (Region 4), in which the association was stronger for PCa of Gleason Score ≤7 (OR = 1.53, 95% CI: 1.21–1.92) than that of Gleason score ≥8 (OR = 1.10, 95% CI: 0.86–1.41), P = 0.04. The other SNP was rs10875943 at 12q13, in which the association was stronger for PCa of Gleason Score ≥8 (OR = 1.25, 95% CI: 1.04–1.50) than that of Gleason score ≤7 (OR = 0.98, 95% CI: 0.84–1.13, P = 0.02.

TABLE III.

Estimated OR for PCa With Gleason Score ≤7 or ≥8 in Chinese Men

Allele frequency Risk for PCa of
Gleason score ≤7		 Risk for PCa of
Gleason score ≥8
Origin of
GWAS		 Chr SNP BP Risk
allele		 Controls
(N = 2,126)		 Gleason
score ≤7
(N = 993)		 Gleason
score ≥8
(N = 684)		 ORa P-value OR P-value P for
different ORs
European 8q24 (Region 2) rs16901979 128,194,098 A 0.26 0.34 0.34 1.47 (1.31–1.66) 5.67E−11 1.48 (1.29–1.68) 7.03E−09 9.86E−01
European 8q24 (Region 1) rs1447295 128,554,220 A 0.15 0.20 0.20 1.43 (1.25–1.65) 3.20E−07 1.45 (1.24–1.69) 3.39E−06 9.11E−01
European 8q24 (Region 3) rs6983267 128,482,487 G 0.42 0.48 0.48 1.26 (1.14–1.41) 1.80E−05 1.31 (1.16–1.48) 0.00001526 6.15E−01
European 8p21 rs1512268 23,582,408 T 0.27 0.33 0.33 1.29 (1.15–1.45) 1.37E−05 1.31 (1.14–1.49) 0.00007238 8.98E−01
Chinese 19q13.4 rs103294 59,489,660 C 0.24 0.29 0.31 1.29 (1.15–1.46) 2.68E−05 1.45 (1.27–1.66) 4.83E−08 1.29E−01
Chinese 9q31.2 rs817826 109,196,121 C 0.08 0.10 0.11 1.4 (1.17–1.69) 2.56E−04 1.52 (1.24–1.86) 4.10E−05 4.72E−01
Japanese 5p15 rs12653946 1,948,829 T 0.36 0.42 0.40 1.28 (1.15–1.43) 9.79E−06 1.2 (1.06–1.36) 4.05E−03 3.81E−01
Japanese 13q22 rs9600079 72,626,140 T 0.44 0.50 0.49 1.26 (1.13–1.4) 2.33E−05 1.21 (1.07–1.37) 2.11E−03 5.81E−01
Japanese 6q22 rs339331 117,316,745 T 0.64 0.69 0.69 1.23 (1.1–1.38) 3.91E−04 1.22 (1.07–1.39) 2.76E−03 9.25E−01
European 17q12 rs4430796 33,172,153 A 0.71 0.75 0.75 1.2 (1.06–1.35) 4.04E−03 1.19 (1.04–1.37) 1.35E−02 9.77E−01
European 8q24 (Region 4) rs620861 128,335,673 G 0.55 0.65 0.58 1.53 (1.21–1.92) 2.90E−04 1.1 (0.86–1.41) 4.34E−01 4.36E−02
European 2p21 rs1465618 43,407,453 T 0.72 0.73 0.76 1.04 (0.92–1.18) 4.87E−01 1.21 (1.05–1.39) 9.94E−03 7.94E−02
European 3q23 rs6763931 142,585,523 A 0.34 0.37 0.36 1.16 (1.04–1.3) 7.99E−03 1.13 (0.99–1.28) 5.98E−02 6.99E−01
Japanese 10q26 rs2252004 122,834,699 C 0.73 0.77 0.76 1.23 (1.08–1.4) 2.18E−03 1.19 (1.03–1.39) 2.23E−02 7.37E−01
European 2p15 rs721048 62,985,235 A 0.03 0.04 0.05 1.29 (0.96–1.74) 9.60E−02 1.73 (1.27–2.37) 4.52E−04 9.36E−02
European 2q31 rs12621278 173,019,799 A 0.72 0.76 0.74 1.2 (1.06–1.35) 3.72E−03 1.11 (0.97–1.28) 1.32E−01 3.56E−01
European 17q12 rs11649743 33,149,092 G 0.66 0.69 0.69 1.14 (1.02–1.28) 2.23E−02 1.13 (0.99–1.28) 7.51E−02 8.47E−01
European 22q13 rs5759167 41,830,156 G 0.70 0.74 0.71 1.2 (1.06–1.35) 3.08E−03 1.05 (0.92–1.2) 4.74E−01 9.36E−02
European 12q13 rs10875943 47,962,277 C 0.85 0.85 0.88 0.98 (0.84–1.13) 7.60E−01 1.25 (1.04–1.5) 1.53E−02 1.62E−02
European 19q13 rs887391 46,677,464 T 0.59 0.61 0.60 1.11 (0.99–1.24) 6.87E−02 1.06 (0.93–1.2) 3.84E−01 5.21E−01
Japanese 2p24 rs13385191 20,751,746 G 0.44 0.46 0.47 1.06 (0.95–1.18) 2.69E−01 1.1 (0.98–1.25) 1.18E−01 5.97E−01
European 7p15 rs10486567 27,943,088 G 0.13 0.16 0.13 1.22 (1.05–1.42) 1.03E−02 1.01 (0.84–1.21) 8.99E−01 6.63E−02
European 7q21 rs6465657 97,654,263 C 0.85 0.88 0.87 1.24 (1.06–1.46) 7.51E−03 1.12 (0.94–1.34) 2.01E−01 3.38E−01
European 6q25 rs9364554 160,753,654 C 0.66 0.67 0.69 1.05 (0.94–1.18) 4.09E−01 1.17 (1.02–1.33) 2.10E−02 1.57E−01
European 10q11 rs10993994 51,219,502 T 0.48 0.51 0.51 1.11 (0.97–1.27) 1.26E−01 1.12 (0.97–1.3) 1.28E−01 8.73E−01
European 11p15 rs7127900 2,190,150 G 0.88 0.90 0.89 1.22 (0.99–1.51) 6.19E−02 1.14 (0.9–1.45) 2.67E−01 5.99E−01
European 8q24 (Region 5) rs10086908 128,081,119 T 0.82 0.83 0.84 1.09 (0.95–1.26) 2.25E−01 1.12 (0.95–1.32) 1.64E−01 7.64E−01
European 4q24 rs7679673 106,280,983 C 0.19 0.20 0.22 1.07 (0.94–1.22) 3.29E−01 1.15 (0.99–1.34) 6.91E−02 4.01E−01
European 2p11 rs10187424 85,647,808 T 0.62 0.63 0.63 1.03 (0.93–1.15) 5.56E−01 1.05 (0.93–1.19) 4.30E−01 8.07E−01
European 6p21 rs130067 31,226,490 G 0.31 0.32 0.35 1.06 (0.92–1.22) 4.46E−01 1.22 (1.04–1.43) 1.42E−02 8.71E−02
European 11q13 rs10896449 68,751,243 G 0.08 0.09 0.09 1.14 (0.91–1.43) 2.66E−01 1.19 (0.92–1.52) 1.79E−01 7.44E−01
Japanese 6p21 rs1983891 41,644,405 T 0.32 0.35 0.34 1.13 (1.01–1.28) 3.53E−02 1.07 (0.93–1.22) 3.27E−01 4.47E−01
European 3q26 rs10936632 171,612,796 A 0.25 0.27 0.25 1.11 (0.93–1.32) 2.57E−01 1.01 (0.83–1.22) 9.50E−01 3.52E−01
European 5p12 rs2121875 44,401,302 A 0.49 0.48 0.48 0.97 (0.87–1.09) 6.32E−01 0.97 (0.86–1.11) 6.81E−01 9.99E−01
European 19q13 rs8102476 43,427,453 C 0.37 0.38 0.38 1.05 (0.94–1.18) 3.50E−01 1.03 (0.91–1.17) 6.14E−01 7.83E−01
European 3q21 rs10934853 129,521,063 A 0.42 0.44 0.44 1.05 (0.95–1.17) 3.46E−01 1.06 (0.94–1.2) 3.48E−01 9.19E−01
European 8p21 rs2928679 23,494,920 A 0.12 0.14 0.13 1.1 (0.94–1.29) 2.29E−01 1.08 (0.9–1.29) 4.10E−01 8.41E−01
Japanese 11q12 rs1938781 58,671,686 G 0.32 0.32 0.33 1.03 (0.92–1.16) 5.75E−01 1.08 (0.95–1.23) 2.33E−01 5.37E−01
European 8q24.21 rs16902094 128,320,346 A 0.71 0.69 0.74 0.91 (0.72–1.15) 4.29E−01 1.15 (0.88–1.52) 3.08E−01 1.69E−01
European 3p12 rs2660753 87,193,364 T 0.30 0.32 0.30 1.13 (1.01–1.27) 3.32E−02 1.01 (0.88–1.15) 9.35E−01 1.18E−01
European 17q24 rs1859962 66,620,348 G 0.41 0.44 0.40 1.11 (1–1.24) 5.18E−02 0.95 (0.84–1.08) 4.41E−01 2.91E−02
European 9q33 rs1571801 123,467,194 T 0.05 0.04 0.06 0.89 (0.69–1.15) 3.64E−01 1.23 (0.94–1.59) 1.28E−01 4.04E−02
European 11q13 rs12418451 68,691,995 A 0.06 0.05 0.05 0.81 (0.63–1.04) 1.00E−01 0.83 (0.62–1.1) 1.88E−01 9.24E−01
Japanese 3p11.2 rs2055109 87,550,022 C 0.07 0.07 0.08 1.05 (0.85–1.29) 6.52E−01 1.19 (0.95–1.49) 1.23E−01 3.19E−01
European 12q13 rs902774 51,560,171 G 0.99 0.99 0.99 1.31 (0.73–2.37) 3.69E−01 1.7 (0.79–3.62) 1.67E−01 5.57E−01
European 2q37.3 rs2292884 238,107,965 A 0.72 0.71 0.72 0.98 (0.87–1.1) 7.53E−01 1 (0.88–1.15) 9.60E−01 7.73E−01
European 4q22 rs17021918 95,781,900 C 0.66 0.68 0.64 1.1 (0.98–1.23) 1.11E−01 0.93 (0.82–1.06) 2.80E−01 2.82E−02
European 19q13 rs2735839 56,056,435 A 0.38 0.38 0.40 0.98 (0.88–1.09) 6.82E−01 1.08 (0.96–1.23) 2.16E−01 1.58E−01
European Xp11 rs5945619 51,241,672 T 0.93 0.92 0.95 0.84 (0.47–1.48) 5.38E−01 1.48 (0.67–3.28) 3.28E−01 2.19E−01
European 22q13 rs9623117 38,782,065 C 0.04 0.04 0.04 1.02 (0.77–1.35) 9.19E−01 1.04 (0.75–1.43) 8.21E−01 9.04E−01
European 10q26 rs4962416 126,686,862 Not polymorphic
African 17q21.32 rs7210100 44,791,748 Not polymorphic
European Xq12 rs5919432 66,938,275 Not polymorphic
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a

Allelic odds ratio (OR) and 95% confidence interval (95% CI).

Fig. 2.

Fig. 2

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Forest plots of PCa risk-associated SNPs identified from GWAS of various populations with risk to PCa of Gleason score ≤7 and ≥8 in Chinese men.

DISCUSSION

Genetic susceptibility to PCa is well established [4]. GWAS of PCa in the past 5 years have identified more than 50 regions in the genome that were associated with PCa risk in various racial populations [6–26]. The potential clinical utility of these PCa risk-associated SNPs in predicting outcomes of initial and repeat prostate biopsy have been reported in populations of European descent [31–32]. However, such utility in the Chinese population has not been established, largely due to a lack of information on PCa risk-associated SNPs in Chinese and the degree of risk they confer. This study attempted to fill the gap by cataloging SNPs that are associated with PCa risk and the OR of these SNPs in Chinese. With the information of the 24 PCa risk-associated SNPs and their ORs, it is now possible to calculate a genetic score based on these SNPs for Chinese men and to assess their genetic risk for PCa.

Risk assessment of PCa using inherited genetic markers may be particularly important in Chinese men because family history of PCa, a commonly used marker for genetic risk to PCa in western countries, is essentially uninformative in China. The percentage of men with family history of PCa is extremely low in China because the disease was rarely detected in this country in past decades, likely due to low adoption of PSA screening and low life expectancy. Other issues such as small family size due to the family planning policies in China may further limit the value of family history in the future. Genetic score derived from PCa risk-associated SNPs, on the other hand, does not rely on information from relatives and therefore may overcome these limitations.

Several papers in the last 2 years reported PCa risk-associated SNPs in Chinese men. In a case–control study of Chinese men (1,108 cases and 1,525 controls) selected from the ChinaPCa, Liu et al. [33] studied the first 33 PCa risk-associated SNPs initially discovered from GWAS of European descent and found 11 of them were associated with PCa risk at P < 0.05. Eight of these 11 were also implicated in the current study. The statistical evidence for association of the other three SNPs was weaker in our current study of larger sample size. In another case–control study of Chinese men (1,524 cases and 2,169 controls) also selected from the ChinaPCa, Wang and colleagues assessed association of the first five PCa risk-associated SNPs identified from GWAS of a Japanese population and found three of which were associated with PCa risk at P < 0.05 [34]. These three SNPs were also implicated in this current study. Finally, in a multi-stage GWAS of PCa in Chinese men, Xu et al. [26] identified two SNPs that were significantly associated with PCa risk at a genome-wide significance level. This Chinese GWAS also reported significant associations of 12 SNPs initially reported from GWAS of European descent (N = 8) and Japanese descent (N = 4) in subjects of the first-stage GWAS (1,417 cases and 1,008 controls). All 12 of these SNPs were implicated in this current study. It is noted that the study subjects between the current study and the previous three studies overlapped; therefore, the findings are not completely independent and the similarities are not surprising. The current study, however, represents the most comprehensive analysis for all established PCa risk-associated SNPs to date from various racial populations in a larger number of Chinese men. As a result, it provided evidence for the largest number of PCa risk-associated SNPs in Chinese.

Although only 10 of the 50 tested SNPs were significantly associated with PCa at P < 0.001 and exceeded study-wise significance after Bonferroni correction, 14 additional SNPs that were associated with PCa risk at P < 0.05 may also be considered as good candidates for risk assessment among Chinese men. These SNPs likely represent true PCa associations because of their statistical evidence and the fact that the direction of association was consistent with previous studies. They may be used for assessment of individual risk for PCa. Although it is possible that some of these SNPs represent false positives, it is known that inclusion of false positive SNPs in risk assessment will not improve or reduce the predictive performance.

Among the 24 SNPs that were significantly associated with PCa risk in Chinese men, 13 were located within known genes, including NXX3.1 (rs1512268 at 8p21), LILRA3 (rs103294 at 19q13), RFX6 (rs339331 at 6q22), HNF1B (rs4430796 and rs11649743 at 17q12), THADA (rs1465618 at 2p21), ZBTB38 (rs6763931 at 3q23), EHBP1 (rs721048 at 2p15), ITGA6 (rs12621278 at 2q31), BIK (rs5759167 at 22q13), JAZF1 (rs10486567 at 7p15), LMTK2 (rs6465657 at 7q21), and SLC22A3 (rs9364554 at 6q25). The exact molecular mechanisms of these genetic variants are unknown. Additional functional studies are warranted.

In addition to common PCa risk associated SNPs discovered from GWAS, a rare but recurrent PCa risk-associated mutation, G84E of HOXB13, was noticed [35]. This gene was also evaluated in 1,518 cases and 1,536 controls selected from ChinaPCa. Although we did not observe the G84E mutation, we found a novel mutation (G125E) in five PCa cases but not in any of the controls [36]. We did not include the mutation in this current study because of its low frequency. However, this high-penetrance mutation should most likely be incorporated into PCa risk assessment for Chinese men.

There are several important limitations in this study. First, the sample size of this study remains limited, which affected statistical power to identify PCa risk-associated SNPs in Chinese men. However, this study nevertheless represents the largest collection of PCa cases in China. Due to the relatively low detection rate of PCa and the lack of a national cancer registry in China, it is a daunting effort to recruit case subjects. We intend to address this limitation by expanding ChinaPCa. Second, due to the open nature of this consortium, the clinical characterization of PCa patients was not consistent across hospitals in this study, which limited our ability to conduct an in-depth analysis based on clinical phenotypes. Third, most of PCa risk-associated SNPs implicated in this study did not distinguish aggressive from indolent disease. This limitation has been commonly encountered in prior genetic studies of PCa as well as other common cancers [37]. More effort should be devoted to the identification of SNPs that are associated with aggressive but not indolent PCa using case–case study designs. Such SNPs would be helpful to address over-diagnosis of indolent PCa.

In conclusion, by systematically evaluating PCa risk-associated SNPs identified from GWAS of various populations in this large Chinese study, we identified 24 PCa risk-associated SNPs in Chinese men and provided estimates of their risk. The information may facilitate risk assessment of PCa in Chinese men for targeted PCa screening, early detection of PCa, and possible chemoprevention.

Supplementary Material

suppl table

ACKNOWLEDGMENTS

We thank all of the subjects included in this study. This work was partially funded by the National Key Basic Research Program Grant 973 (2012CB518300) to Y.S., the National Key Basic Research Program Grant 973 (2012CB518301) to J.X., the Key Project of the National Natural Science Foundation of China (81130047) to J.X., intramural grants from Fudan University “Thousand Talents Program” and Huashan Hospital to J.X., the National Institutes of Health (NCI CA129684) to J.X., National Natural Science Foundation of China (30945204) to Z.M., National Natural Science Foundation of China (30973009) to D.Y.

Footnotes

Additional supporting information may be found in the online version of this article.

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