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. Author manuscript; available in PMC: 2011 Apr 18.
Published in final edited form as: Prostate. 2010 Mar 1;70(4):425–432. doi: 10.1002/pros.21076

Association of 17 Prostate Cancer Susceptibility Loci with Prostate Cancer Risk in Chinese Men

Siqun Lilly Zheng 1,2, Ann W Hsing 3,*, Jielin Sun 1,2, Lisa W Chu 3, Kai Yu 3, Ge Li 1,2, Zhengrong Gao 1,2, Seong-Tae Kim 1,2, William B Isaacs 4, Ming-Chang Shen 5, Yu-Tang Gao 6, Robert N Hoover 3, Jianfeng Xu 1,2
PMCID: PMC3078699  NIHMSID: NIHMS258751  PMID: 19866473

Abstract

Background

Several genome-wide association studies (GWAS) in populations of European descent have identified more than a dozen common genetic variants that are associated with prostate cancer risk.

Methods

To determine whether these variants are also associated with prostate cancer risk in the Chinese population, we evaluated 17 prostate cancer susceptibility loci in a population-based case-control study from Shanghai, including 288 prostate cancer cases and 155 population controls.

Results

After adjusting for age, two of the 17 loci were significantly associated with prostate cancer risk, while the other 15 loci were suggestively associated with prostate cancer risk in this population. The strongest associations were found for chromosome 8q24 Region 2 (rs1016343: OR=2.07, 95% CI: 1.35-3.20, P=9.4×10-4) and 8q24 Region 1 (rs10090154: OR=2.07, 95% CI: 1.31-3.28, P=0.002); additional single nucleotide polymorphisms (SNPs) assessed in these two 8q24 regions were also significant (ORRegion2=1.92-2.05, P=9.4×10-4-0.003, and ORRegion1=1.77-1.81, P=0.01 for all SNPs).

Conclusions

Our study shows that multiple prostate cancer risk loci identified in European populations using GWAS are also associated with prostate cancer risk in Chinese men, a low-risk population with mostly clinically relevant cancers. Larger studies in Chinese and Asian populations are needed to confirm these findings and the role of these risk loci in prostate cancer etiology in Asian men.

Keywords: prostate cancer, association, Asian, Chinese, 8q24

Introduction

Recent genome-wide association studies (GWAS) in 4 different study populations, all of which in European descents, have revealed more than a dozen loci associated with prostate cancer risk [1-9]. Confirmation of these risk loci among European descents has been reported extensively, but there are few studies in other racial/ethnic groups [10-19]. Most published studies in populations not of European descent were on the 8q24 loci in African American, Japanese American, Taiwanese, Latino American, Native Hawaiians, and Asian Indian populations [11-19]. Considering the fact that there are substantial differences in genetic background and lifestyles between racial groups, association studies in multiple race/ethnic populations may provide important insight into the role of genetics and gene-environmental interactions in prostate cancer etiology [20].

To date, recent GWAS have revealed 17 loci associated with prostate cancer risk in various populations worldwide [1-8,10,12-17], including 3 loci on chromosomal band 8q24 [represented by single nucleotide polymorphism (SNP) rs1447295 (Region 1), rs16901979 (Region 2), and rs6983267 (Region 3)] and 2 loci on chromosomal band 17q12 [represented by rs4430796 (Region 1) and rs11649743 (Region 2)] as well as 12 loci in other regions of the genome. To determine whether these risk loci also affect prostate cancer risk in Asian men, where prostate cancer risk is much lower but is rising steadily, we evaluated their relationship with prostate cancer risk in a population-based study conducted in Shanghai, China.

Materials and Methods

Study population

Details of this population-based case-control study have been reported elsewhere [21-23]. Briefly, cases were permanent residents of Shanghai who were newly diagnosed with prostate cancer between 1993 and 1995, identified through a rapid reporting system in 28 collaborating hospitals in urban Shanghai. The rapid reporting system captured > 95% of the cases diagnosed in urban Shanghai during the study period. Pathology material from cancer cases was reviewed by study pathologists in Shanghai and subsequently confirmed by pathologists at the Armed Forces Institute of Pathology in the USA. Based on records maintained at the Shanghai Resident Registry, male controls with no history of cancer were selected at random from the 6.5 million permanent Shanghai residents >18 years of age and frequency-matched to cancer cases by age (5-year intervals). Over 75% of the study subjects provided overnight fasting blood samples. DNA was extracted from the buffy coat fractions at the American Type Culture Collection (Manassas, VA). Only subjects with sufficient DNA available were included in the study for genotyping. In total, 288 prostate cancer cases and 155 population controls were included. There was no difference in demographic characteristics between those with and without DNA samples. This study was approved by the Institutional Review Boards at the National Cancer Institute and the Shanghai Cancer Institute and written informed consent was obtained from all study subjects.

Additional population controls

To increase the sample size of population controls for allele-based analysis, we included genotyping information from the Chinese subjects from the HapMap project in the study, including 162 Han Chinese from Beijing (CHB) and 140 Chinese from Metropolitan Denver, Colorado (CHD) [24-26]. Both CHB and CHD subjects were unrelated individuals who identified themselves as having at least three out of four Han Chinese grandparents. Data were pooled with Shanghai population controls for further analysis only if SNP allele frequencies were similar in both the Shanghai controls and HapMap subjects, as suggested by HapMap guidelines [24-26].

Selection of SNPs for evaluation and genotyping

We selected one SNPs from each of the 17 loci that were significantly associated with prostate cancer risk (P < 10-8) in four previous GWAS [1-8] and one followed-up fine mapping follow-up study [27]. These loci included three independent loci at 8q24 (Region 1 rs1447295, Region 2 rs16901979, and Region 3 rs6983267), two independent loci at 17q12 (Region 1 rs4430796 and Region 2 rs11649743), and one locus each at 2p15, 3p12, 6q25, 7p15, 7q21, 9q33, 10q11, 10q26, 11q13, 17q24.3, 19q13, and Xp11. We also selected 8 additional SNPs at 8q24 where prostate cancer associations have been reported in two fine mapping studies [15,28]. These include two additional SNPs in 8q24 Region 1 (rs4242382 and rs10090154), three additional SNPs in 8q24 Region 2 (rs1016343, rs13254738, and rs6983561), and 2 additional SNPs in 8q24 Region 3 (rs7837328 and rs7000448) as well as a SNP centromeric to the three 8q24 risk regions (rs979200).

The 25 SNPs were genotyped for all study subjects using a MassARRAY iPLEX system (Sequenom, Inc. San Diego, CA). Two duplicates and two water samples were included in each 96-well plate as PCR-negative controls. All assays were performed in a blinded fashion. The genotype call rates for these SNPs were 98% and the average concordance rate between samples was 100% among the duplicated quality control samples.

Statistical methods

Tests for Hardy-Weinberg Equilibrium (HWE) were performed for each autosomal SNP separately among control subjects, using Pearson's χ2 test. Unadjusted allelic odds ratios (ORs) and the associated 95% confidence interval (95% CI) were calculated for each risk allele (R) versus non-risk allele (N); risk alleles were defined based on European populations. Logistic regression analysis was used to test for the association between prostate cancer and the genotypes as well as for calculating ORs and 95% CI in models that were adjusted for age (4 categories: <65, 65-69, 70-74, and ≥75). Due to the limited size of the study, autosomal SNPs were modeled in a dominant mode of inheritance (NN vs NR+RR) if the risk allele frequency was less than 50% in the controls and in a recessive mode of inheritance (NN+NR vs RR) if the risk allele frequency was greater than 50% in the controls. Significant results are defined as P<0.05 (unadjusted and age-adjusted models) and P<0.002 (Bonferroni corrected for age-adjusted models). For SNPs on 8q24, pair-wise linkage disequilibrium (LD) was estimated in control subjects using Haploview [30]. Haplotype blocks were inferred using the default option of the Gabriel method [31].

Results

Table I shows selected demographic characteristics of study participants. Case and control subjects were of similar age and body size; over half of the subjects were in the normal body mass index (BMI) category (BMI of 18.5-22.9 kg/m2) as defined by the World Health Organization recommendations for Asian populations [29]. Over 55% of cases had PSA levels above 4 ng/ml. The median total PSA level for cases was 8.1 ng/mL with an interquartile range of 0.38 to 90.0 ng/mL and the median total PSA level for controls was 1.5 ng/mL with an interquartile range of 1.0 to 2.8 ng/mL. Because screening is relatively uncommon during our study period, only about 46% of the cases were localized, while over half were regional or remote. The majority of the cases (91%) were moderately or poorly differentiated.

Table I. Selected demographic characteristics of study subjects.

Characteristic Prostate Cancer Cases
N=288		 Controls
N=155
n % n %
Age (years)
 < 65 29 11.9 29 18.7
 65-69 42 17.3 26 16.8
 70-74 75 30.9 45 29.0
 ≥ 75 97 39.9 55 35.5
Body mass index (BMI; kg/m2)a
 Underweight (<18.5) 17 13.1 16 10.4
 Normal (18.5-22.9) 67 51.5 87 56.5
 Overweight (23-27.5) 35 26.9 38 24.7
 Obese (>27.5) 11 8.5 13 8.4
Prostate specific antigen (PSA; ng/ml)
 Normal (≤ 4) 128 44.6 129 83.2
 Elevated (> 4) 159 55.4 26 16.8
Clinical Stage of Cancer
 Localized 102 35.4 - -
 Regional 56 19.4 - -
 Remote 64 22.2 - -
 Unstaged 66 22.9 - -
Histological phenotype
 Well differentiated 18 6.3 - -
 Moderately differentiated 76 26.4 - -
 Poorly differentiated 111 38.5 - -
 Unknown 83 28.8 - -
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a

BMI categories are based on the World Health Organization recommendations for Asian populations [29]

All autosomal SNPs were in HWE (P≥0.05) among control subjects (Table II). Of the 17 loci evaluated (Table II, top panel), two were significantly associated with prostate cancer in unadjusted models, including 8q24 Region 2 rs16901979 (OR=1.93, 95% CI, 1.39-2.70) and 8q24 Region 1 rs1447295 (OR=1.52, 95% CI, 1.06-2.18), while 13 other loci were suggestively associated with prostate cancer risk. Because allele frequencies for all SNPs were very similar between our controls and HapMap subjects (CHB and CHD), we combined our controls with those from HapMap and used this larger combined control group for further analysis to increase the statistical power of the study. Analysis of allele frequencies of the cases and the combined controls showed that four of the 17 loci, including the two 8q24 regions described above, 7p15 rs10486567 (P=0.009), and 10q11 rs10993994 (P=0.04), were significantly associated with prostate cancer risk. The effect of both 8q24 Regions 1 and 2 persisted after further adjustment for age using logistic regression analysis; further adjustment for BMI did not change the results. In addition, although not statistically significant, 11 other loci were suggestively associated with increased risk (ORs after age-adjustment ranged from 1.11-2.02) and 4 loci were suggestively associated with decreased risk (ORs ranged from 0.82-0.96). In general, directions of the effects as well as effect sizes for both significant and non-significant loci are the same as that seen in the European populations in almost all risk regions examined.

Table II. Association of SNPs with prostate cancer risk in a Chinese population.

Chr SNP Locus Non-risk
allele
(N)		 Risk
allele
(R)a 		Controls
n=155		 HapMapd
N=302		 Prostate Cancer Cases
n=288
Risk allele
frequency		 Genotypesb HWE
Pc 		Risk allele
frequency		 Unadjusted Allelic ORe Genotypesb Age-adjusted ORg
NN NR RR Risk allele
frequency		 OR 95% CI P Pf NN NR RR Model h OR 95% CI P
Prostate cancer risk loci identified in populations of European descent
2 rs721048 2p15 G A 0.033 142 10 0 0.68 0.033 0.039 1.18 0.56 - 2.50 0.67 0.60 263 20 1 D 0.94 0.41 - 2.17 0.89
3 rs2660753 3p12 C T 0.314 70 70 13 0.44 0.323 0.310 0.99 0.74 - 1.32 0.92 0.64 142 105 35 D 0.82 0.54 - 1.23 0.34
6 rs9364554 6q25 C T 0.310 72 63 15 0.82 0.344 0.308 0.99 0.74 - 1.34 0.95 0.19 138 117 29 D 0.96 0.64 - 1.45 0.84
7 rs10486567 7p15 T C 0.128 116 35 2 0.72 0.126 0.181 1.47 1.00 - 2.17 0.05 0.009 192 73 14 D 1.53 0.97 - 2.43 0.07
7 rs6465657 7q21 T C 0.860 5 32 112 0.17 0.875 0.850 0.90 0.61 - 1.33 0.59 0.13 7 74 203 R 0.83 0.52 - 1.34 0.45
8 rs16901979 8q24 (Region 2) C A 0.235 85 52 8 0.99 0.264 0.366 1.93 1.39 - 2.70 1.0×10-4 2.0×10-4 110 139 34 D 2.03 1.33 - 3.10 0.001
8 rs6983267 8q24 (Region 3) T G 0.428 51 72 29 0.69 0.434 0.457 1.12 0.85 - 1.48 0.41 0.42 86 134 62 D 1.11 0.72 - 1.72 0.64
8 rs1447295 8q24 (Region 1) C A 0.156 110 35 6 0.15 0.130 0.222 1.52 1.06 - 2.18 0.02 3.7×10-5 173 96 15 D 1.81 1.16 - 2.83 0.009
9 rs1571801 9q33 G T 0.036 142 11 0 0.64 0.054 0.063 1.80 0.90 - 3.62 0.10 0.55 246 33 1 D 1.75 0.84 - 3.63 0.13
10 rs10993994 10q11 C T 0.434 43 77 24 0.29 0.443 0.504 1.32 0.99 - 1.75 0.06 0.04 75 130 77 D 1.12 0.70 - 1.77 0.64
10 rs4962416 10q26 A G 0.007 146 2 0 0.93 0.007 0.012 1.85 0.38 - 9.03 0.45 0.39 276 7 0 D 2.02 0.41 - 9.92 0.38
11 rs10896449 11q13 A G 0.086 122 21 2 0.33 0.084 0.111 1.35 0.82 - 2.23 0.24 0.12 221 63 0 D 1.53 0.89 - 2.64 0.13
17 rs11649743 17q12 (Region 2) A G 0.660 19 64 67 0.55 0.654 0.700 1.22 0.91 - 1.64 0.19 0.064 28 110 143 R 1.35 0.89 - 2.05 0.15
17 rs4430796 17q12 (Region 1) C T 0.720 11 63 78 0.72 0.724 0.740 1.14 0.83 - 1.56 0.43 0.42 17 110 155 R 1.18 0.78 - 1.78 0.43
17 rs1859962 17q24.3 T G 0.391 55 75 22 0.66 0.405 0.457 1.31 0.98 - 1.74 0.07 0.072 83 136 59 D 1.31 0.85 - 2.03 0.22
19 rs2735839 19q13 A G 0.580 24 79 47 0.34 0.596 0.610 1.14 0.85 - 1.52 0.38 0.69 46 131 107 R 1.15 0.66 - 2.00 0.63
X rs5945619 Xp11 A G 0.059 144i 9i 0.053 0.086 1.51 0.68 - 3.33 0.31 0.16 255i 24i 1.82 0.82 - 4.06 0.14
Additional SNPs genotyped at 8q24j
8 rs979200 8q24 T C 0.510 36 76 39 0.93 0.498 0.459 0.82 0.62 - 1.08 0.16 0.18 84 134 61 R 0.77 0.47 - 1.25 0.29
8 rs1016343 8q24 (Region 2) C T 0.330 66 65 16 1.00 0.341 0.453 1.76 1.29 - 2.41 3.7×10-4 1.0×10-4 76 159 49 D 2.07 1.35 - 3.20 9.4×10-4
8 rs13254738 8q24 (Region 2) A C 0.697 11 70 71 0.26 0.303 0.801 1.82 1.32 - 2.56 0.001 5.9×10-4 9 92 176 R 2.05 1.34 - 3.13 9.4×10-4
8 rs6983561 8q24 (Region 2) A C 0.245 80 53 8 0.84 0.245 0.368 1.85 1.33 - 2.59 2.9×10-4 3.1×10-4 109 141 34 D 1.92 1.25 - 2.93 0.003
8 rs7837328 8q24 (Region 3) G A 0.372 59 64 22 0.50 0.381 0.417 1.20 0.90 - 1.59 0.22 0.20 99 133 52 D 1.18 0.77 - 1.81 0.45
8 rs7000448 8q24 (Region 3) C T 0.307 75 55 18 0.12 0.307 0.360 1.26 0.93 - 1.70 0.13 0.12 114 133 35 D 1.43 0.94 - 2.16 0.09
8 rs4242382 8q24 (Region 1) G A 0.168 108 37 7 0.11 0.140 0.241 1.56 1.09 - 2.22 0.01 1.0×10-5 163 105 16 D 1.77 1.15 - 2.75 0.01
8 rs10090154 8q24 (Region 1) C T 0.142 112 30 6 0.04 0.142 0.223 1.70 1.17 - 2.48 0.006 0.004 170 98 14 D 2.07 1.31 - 3.28 0.0019
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a

Risk alleles are defined based on European populations

b

NN=homozygous non-risk alleles; NR=heterozygous; RR=homozygous risk alleles

c

HWE=Hardy-Weinberg Equilibrium

d

Chinese subjects from the HapMap Project: CHB (N=162) & CHD (N=140)

e

Based on analysis of Shanghai prostate cancer cases versus Shanghai controls; OR=Odds Ratio; CI=confidence interval

f

Based on analysis of Shanghai prostate cancer cases versus all controls (Shanghai controls, CHB and CHD; N=457)

g

Based on logistic regression analysis of Shanghai prostate cancer cases versus Shanghai controls adjusting for age (4 categories: <65, 65-69, 70-74, and ≥75)

h

D=dominant mode of inheritance; R=recessive mode of inheritance

i

Haploid

j

Additional SNPs were selected to be genotyped from previous fine mapping studies of 8q24 to provide better coverage of this region. These include two additional SNPs at Region 1, three additional SNPs at region 2, two additional SNPs at Region 3, and 1 SNP centromeric to the risk regions.

Additional SNPs genotyped in the three 8q24 risk regions (Table II, bottom panel and Figure 1a) show all SNPs in 8q24 Regions 1 and 2 were significantly associated with prostate cancer risk (P<0.05) in both unadjusted and adjusted models. The strongest associations were found for the additional three SNPs in Region 2 (rs1016343, rs13254738, and rs6983561; Padjusted ranged from 9.4×10-4-0.003), followed by the additional two SNPs in Region 1 (rs4242382, and rs10090154; Padjusted ranged from 0.002-0.01). The additional SNPs at region 3 (rs7837328, and rs7000448) were not significantly associated with prostate cancer similar to rs6983267, nor was rs979200 at the centromeric boundary of 8q24 (Padjusted>0.05). All SNPs within the same region were in significant LD with one another (Figure 1b). Among all 25 SNPs typed, three SNPs in the 8q24 Region 2 (rs16901979, rs1016343, and rs13254738) as well as one SNP in 8q24 Region 1 (rs10090154) remained significant after Bonferroni correction (P<0.002) in adjusted models.

Figure 1.

Figure 1

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A schematic view of genetic association between SNPs at 8q24 and prostate cancer risk. (Upper panel) Allelic association tests for SNPs at 8q24 (127,950,000-128,600,000) and prostate cancer risk in the Shanghai population. (Lower panel) Pair-wise LD and inferred haplotype blocks of SNPs at this region based on the control subjects in the Shanghai population. The color of each square represents the pair-wise D′; the darker dhases corresponding to stronger D′, with the brightest red representing D′=1 and pure white represents D′=0. The number in the square represents r2.

Discussion

In this population-based study of prostate cancer in Chinese men, we systematically evaluated 17 reported prostate cancer risk loci identified through GWAS in populations of European descent. We found that two of the 17 loci (8q24 Regions 1 and 2) were significantly associated with prostate cancer risk in Chinese men in all statistical models. These results suggest that some prostate cancer risk variants identified in populations of European descent are also relevant for Chinese men, a low-risk population with mostly clinically relevant cancers.

Evaluation of additional SNPs at the 3 risk loci on 8q24 in this Chinese population showed that SNPs in Regions 1 and 2 were significantly associated with prostate cancer risk while associations for SNPs in Region 3 were mostly null, although this result could be due to limited power related to minor allele frequencies of SNPs on Region 3. These results are consistent with those from three previous studies of 8q24 in Asian populations [12,16,17], which found significant associations with Regions 1 and 2 while results for Region 3 were mixed. Taken together, these results suggest that risk Regions 1 and 2 of 8q24 may be more important than Region 3 of 8q24 in relation to prostate cancer risk in Asian populations, although larger studies are needed to confirm these findings.

There are several notable advantages to replicating GWAS findings from populations of European descent in other racial/ethnic groups, including minority populations. First, most of the cases had aggressive prostate tumors, since PSA screening in China was relatively uncommon during the early 1990s in China when the parent study was conducted. Second, this is an efficient and cost-effective approach for identifying high-risk variants in minority populations. Targeted evaluations of a dozen high-risk loci for prostate cancer, identified from GWAS in European populations, in minority populations are feasible and efficient because smaller numbers of SNPs need to be typed and evaluated, thus incurring a lower cost. The identification of several regions associated with prostate cancer risk in this small Chinese study population successfully demonstrated the benefit. Third, genetic association studies in multi-ethnic populations provide important information in distinguishing true from false associations. For example, confirming the same risk loci in populations that have substantially different genetic backgrounds and lifestyle provides further support for the observed associations. For the SNPs that fail to be confirmed in other racial/ethnic groups given sufficient power, the substantially different genetic backgrounds and environmental exposures between the populations may provide a unique opportunity to study gene-gene and gene-environmental interaction in the disease risk [20].

Limitations of the study should be noted. The statistical power of this small study was limited. For example, we had only 80% power to detect SNPs with allelic OR ≥ 1.6 and minor allele frequency (MAF) ≥ 20% in this study population [32]. Therefore, caution should be exercised in interpreting these results (i.e., we could not exclude the possibility that some of the non-significant SNPs in this study may still be modestly associated with prostate cancer risk in the Chinese population). False positive associations due to multiple testing are likely. After adjustment for 25 tests, only four SNP in 8q24 remained significant after Bonferroni correction. Finally, although our cases and controls came from a relatively homogenous Chinese population in Shanghai, we could not rule out the possibility that some observed associations in the study may have been subject to potential population stratification, since the genetic background (i.e., ancestry informative markers) in our subjects had not been measured and controlled for.

Conclusions

In summary, we found that multiple prostate cancer risk-associated loci identified in populations of European descent using GWAS may be associated with prostate cancer risk in the Chinese population. Our study demonstrated an efficient and cost-effective approach to confirming in a Chinese population the genetic variants implicated in other study populations. Larger studies in the Chinese population are needed to confirm these findings and further evaluate additional genetic variants, especially those with a modest effect. Identification of variants associated with prostate cancer may improve our understanding of the disease etiology and have potential implications for the early detection, diagnosis, and treatment of prostate cancer.

Acknowledgments

We thank J.Cheng and the staff of the Shanghai Cancer Institute for specimen collection and processing; collaborating hospitals and urologists for data collection; and local pathologists for pathology review; S. Niwa and A. Truelove of Westat and G.Yuan of Information Management Systems for data management and preparation; and J. Koci of the Scientific Applications International Corporation for management of the biologic samples. This research was supported in part by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, and by National Cancer Institute CA105055, CA106523, and CA95052 to J.X. L.W.C. is supported by the Cancer Prevention Fellowship Program, Office of Preventive Oncology, National Cancer Institute.

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