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. Author manuscript; available in PMC: 2013 Mar 1.
Published in final edited form as: Prostate. 2011 Jun 10;72(4):376–385. doi: 10.1002/pros.21439

Association of prostate cancer risk with SNPs in regions containing androgen receptor binding sites captured by ChIP-on-chip analyses

Yizhen Lu 1,2,*, Jielin Sun 3,*, Andrew K Kader 3,4, Seong-Tae Kim 3, Jin-Woo Kim 3, Wennuan Liu 3, Jishan Sun 3, Daru Lu 1,2,5, Junjie Feng 3, Yi Zhu 3, Tao Jin 3, Zheng Zhang 3, Latchezar Dimitrov 3, James Lowey 6, Kevin Campbell 6, Edward Suh 6, David Duggan 6, John Carpten 6, Jeffrey M Trent 6,7, Henrik Gronberg 8, Siqun L Zheng 3, William B Isaacs 9,†, Jianfeng Xu 1,2,3,4,7,†
PMCID: PMC3366362  NIHMSID: NIHMS379957  PMID: 21671247

Abstract

Background

Genome-wide association studies (GWAS) have identified approximately three dozen single nucleotide polymorphisms (SNPs) consistently associated with prostate cancer (PCa) risk. Despite the reproducibility of these associations, the molecular mechanism for most of these SNPs has not been well elaborated as most lie within non-coding regions of the genome. Androgens play a key role in prostate carcinogenesis. Recently, using ChIP-on-chip technology, 22,447 androgen receptor (AR) binding sites have been mapped throughout the genome, greatly expanding the genomic regions potentially involved in androgen-mediated activity.

Methodology/Principal findings

To test the hypothesis that sequence variants in AR binding sites are associated with PCa risk, we performed a systematic evaluation among two existing PCa GWAS cohorts; the Johns Hopkins Hospital and the Cancer Genetic Markers of Susceptibility (CGEMS) study population. We demonstrate that regions containing AR binding sites are significantly enriched for PCa risk-associated SNPs, i.e. more than expected by chance alone. In addition, compared with the entire genome, these newly observed risk-associated SNPs in these regions are significantly more likely to overlap with established PCa risk-associated SNPs from previous GWAS. These results are consistent with our previous finding from a bioinformatics analysis that one-third of the 33 known PCa risk-associated SNPs discovered by GWAS are located in regions of the genome containing AR binding sites.

Conclusions/Significance

The results to date provide novel statistical evidence suggesting an androgen-mediated mechanism by which some PCa associated SNPs act to influence PCa risk. However, these results are hypothesis generating and ultimately warrant testing through in-depth molecular analyses.

Keywords: AR, prostate cancer, GWAS, pathway association study

Introduction

Prostate cancer (PCa) is the most common non-cutaneous cancer affecting men in the US. Inherited genetic alterations are hypothesized to contribute to the well-characterized familial aggregation of PCa. Since 2007, breakthroughs in genomic technologies have resulted in the discovery of at least 33 PCa risk-associated single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) (1-15). These risk-associated germ-line genetic markers have been consistently replicated in multiple case-control study populations of European descent as well as of African Americans and Asians (16-19). However, because most of these risk-associated SNPs reside in non-coding regions of the genome, the molecular mechanisms by which these SNPs function are uncertain. Recently, we applied a bioinformatics tool to map these PCa risk-associated SNPs to genomic functional annotation databases, including the encyclopedia of DNA elements (ENCODE), 11 genomic regulatory elements databases defined by the University of California Santa Cruz (UCSC) table browser, and androgen receptor (AR)-binding sites (20). We found that PCa risk-associated SNPs were significantly enriched in genomic regions containing AR-binding sites (P < 0.003). About one-third of the 33 risk SNP blocks are located within the regions containing AR-binding sites.

Androgens and their receptor (AR) have long been demonstrated to play a critical role in prostate carcinogenesis, and their importance in the development of PCa is further supported by the 23-25% reduction in PCa risk seen in two large clinical trials testing agents which block androgen activation via testosterone to dihydrotestosterone conversion (21-22). AR is a transcription factor which binds to regions of DNA to control the expression of target genes in the presence of androgens. Androgen-response elements (AREs) are the most well-known AR binding sites. The canonical AREs are partial palindromic repeats of 5′-TGTTCT-3′ in promoter domains. However, AR binding sites can also be operationally defined as DNA regions that are bound by AR. By combining chromatin immunoprecipitation with tiled oligonucleotide microarrays (ChIP-on-chip), Wang et al performed a genome-wide study and identified 22,447 regions in the genome containing putative AR binding sites, with a median size of 911 base pairs (bp) (ranging from 299 to 5,554 bp) (23). Importantly, most of these regions are not proximal to promoters, and instead map to distal, unannotated cis-regulatory elements. These findings have greatly expanded the genomic regions potentially involved in androgen-mediated activity.

In this study, we expanded our research on 33 known PCa risk-associated SNPs and tested the hypothesis that sequence variants in putative AR binding sites across the genome are associated with PCa risk. This was performed by systematically evaluating identified sequence variants in regions containing AR binding sites captured by ChIP-on-chip analyses for their association with PCa risk in two existing PCa GWAS datasets.

Methods

Study populations

Our primary population was from a Johns Hopkins Hospital (JHH) PCa GWAS which included 1,964 PCa cases and 3,172 control subjects. The cases are Caucasian PCa patients who underwent radical prostatectomy for the treatment of PCa at JHH from January 1, 1999, through December 31, 2008 (24). The clinical characteristics of these patients are presented in Supplementary Table 1. The control subjects for this population were an independent group of Caucasian individuals from the Illumina iControlDB (iControls) dataset (25). A second GWAS population was obtained from Stage 1 of the National Cancer Institute Cancer Genetic Markers of Susceptibility (CGEMS) study. It included 1,172 PCa cases and 1,157 control subjects, selected from the Prostate, Lung, Colon and Ovarian (PLCO) Cancer Screening Trial (6,9). The genotype and phenotype data of the study are publicly available and our use of the data was approved by CGEMS.

GWAS genotyping data, imputation and quality control

GWAS of the JHH case population was performed using the Illumina 610K chip (24) and the GWAS of the iControls population (25) was performed using Illumina Hap300 and Hap550 Chips. We imputed all the known SNPs that are catalogued in HapMap Phase II (26) using the IMPUTE computer program (27) with a posterior probability of 0.9 as a threshold to call genotypes. The following quality control criteria were used to filter SNPs: MAF < 0.01, HWE < 0.001 and call rate < 0.95.

SNPs in genomic regions containing AR binding sites

The data on the 22,447 putative AR binding sites across the genome discovered by ChIP-on-chip analysis in two PCa cell lines are publically available (23). We performed a search for all known SNPs in these AR binding sites based on the HapMap database (Build 36).

PCa association tests

Allele frequency differences between case patients and control subjects were tested for each SNP in regions of AR binding sites using a chi-square test with 1 degree of freedom. Allelic odds ratio (OR) and 95% confidence intervals (95% CI) were estimated based on a multiplicative model. In order to reduce spurious association results due to potential population stratification in the JHH study, the association tests were performed by adjusting the top five eigenvectors that were estimated using the EIGENSOFT software in the logistic regression analysis (28). A combined allelic test for JHH and CGEMS study populations was performed using the Cochran-Mantel-Haenszel test.

Linkage disequilibrium (LD) block

To identify SNPs that are independently associated with PCa risk, we used the LD block as a unit of association. LD block was defined as a set of SNPs within a genomic region of 1000 kb with pair-wise r2 value ≥ 0.5, and was estimated using the CLUMP function of the PLINK software (29).

Reported PCa risk-associated SNPs by GWAS

Based on PCa GWAS reported before December 2009, we selected 33 PCa risk-associated SNPs, each of which exceeded genome-wide significance levels in their initial reports (P < 10-7) and all have been replicated in independent study populations (1-15). Considering that many SNPs are in LD with each of these 33 SNPs, we used LD blocks (r2 value ≥ 0.5) to define the genomic regions of these established PCa risk SNPs based on CEU genotype data from HapMap release#27 (Phase II+PhaseIII). One SNP (rs16902094) was not included in the study because it was not available in the HapMap release data. Thus there were 32 PCa associated LD blocks. The SNP list and pair-wise r2 values of the PCa risk-associated SNPs are provided in Supplementary Table 2.

Results

The search for all known SNPs within the regions of 22,447 ChIP-on-chip detected AR binding sites in the HapMap database (Build 36) revealed a total of 18,401 SNPs (Figure 1a-b). These SNPs are listed in Supplementary Table 3. In the JHH study, 12,724 of these 18,401 SNPs were directly genotyped or successfully imputed and passed the quality control criteria outlined in the materials and methods (Figure 1c). Association of PCa risk with each of these 12,724 SNPs was performed using an allelic test. Given that some of these SNPs are in LD, we used the CLUMP function of the PLINK computer program to infer independent LD blocks (Figure 1d). Among the 9,500 independent LD blocks, we observed 4 blocks where at least one SNP was associated with PCa risk at P <10-5 (Figure 1e). This P-value cutoff was chosen to approximate a 5% Type I error after accounting for 9,500 tests using a Bonferroni correction. The four blocks were 8p21 at 3′ of NKX3.1, 10q11 at 5′ of MSMB, and regions 1 and 2 of 8q24 (Supplementary Table 4), all of which were known PCa risk-associated SNPs. To assess whether the observation of four significant blocks represented an enrichment of PCa risk-associated SNPs in the regions containing AR binding sites, we performed a simulation analysis under the null hypothesis of no association between SNPs in these 9,500 blocks and PCa risk. Among the 100,000 replicates, we found 10 replicates where 4 or more blocks were significantly associated with PCa risk at P < 10-5. Therefore, the excess of observed PCa risk-associated LD blocks (enrichment) was statistically significant, with an empirical P value of 0.0001 (10/100,000) (Figure 1f).

Figure 1.

Figure 1

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Flow chart of study design and key findings: a) number of AR binding sites in the genome defined by chromatin immunoprecipitation with tiled oligonucleotide microarrays (ChIP-on-chip) reported by Wang et al. (2009), b) number of known SNPs in the regions containing AR binding sites, c) number of SNPs that were directly genotyped or imputed with a call rate ≥ 95% in each study in the JHH, CGEMS, and combined JHH and CGEMS dataset, d) number of independent LD blocks, e) number of LD blocks where at least one SNP in the block was statistically associated with PCa risk (allelic test) in each study, and f) results from simulation analysis using a bootstrapping method.

We performed a similar analysis based on GWAS data from an independent study population, CGEMS. Among the 12,369 independent LD blocks containing AR binding sites, we found 2 blocks where at least one SNP was associated with PCa risk at P <10-5 (Figure 1c-f). These two blocks were at 15q21 and the region 1 of 8q24 (Supplementary Table 4), the latter block was also significant in the JHH study at P<10-5. Although not all the specific significant blocks observed in the JHH were replicated, the enrichment of PCa risk-associated SNPs in the regions containing AR binding sites was statistically significant. Based on 100,000 replicates simulated under the null hypothesis of no association between SNPs in these 12,369 blocks and PCa risk, the empirical p value of observing 2 or more blocks that were significantly associated with PCa risk at P < 10-5 of a replicate is 0.01. Similar findings were obtained when the two GWAS data were combined (Figure 1c-f).

The enrichment of PCa risk-associated SNPs in the regions containing AR binding sites was also found when less stringent P-value cutoffs (10-2, 10-3, and 10-4) were used to declare significant association, in the JHH, CGEMS, and combined JHH and CGEMS datasets (Supplementary Table 5).

When examining the blocks that are significantly associated with PCa risk, we found that many of them overlapped with LD blocks of known PCa risk-associated SNPs identified from GWAS (i.e., at least one common significant SNP located in both types of LD blocks), and the proportion of overlap was significantly higher than that of remaining significant LD blocks in the genome. For example, among the blocks that were significantly associated with PCa risk at P-value cutoffs of 10-2, 10-3, 10-4, or 10-5 in the combined JHH and CGEMS dataset, 4.3%, 26.1%, 71.4%, and 100%, respectively, overlapped with LD blocks containing 32 known PCa risk associated SNPs. In contrast, of the LD blocks that were associated with PCa risk at these P-value cutoffs throughout the genome, only 0.5%, 2.4%, 10.6%, and 42.9%, respectively, overlapped with the blocks of 32 known PCa risk associated SNPs (Table 1). Based on Fisher's exact tests, the difference between these two types of blocks was statistically significant in most scenarios (at a P-value cutoff of 10-2, 10-3, or 10-4). The difference was not statistically significant at the P-value cutoff of 10-5, likely due to small cell count. These results suggest that higher proportions of observed PCa risk-associated SNPs in the regions containing AR binding sites represent true association.

Table 1. Proportion of PCa risk-associated blocks in the regions containing AR binding sites or in the genome that overlap with known PCa risk-associated SNPs.

P-value cutoff for declaring significant association Number and (%) of significant blocks that overlap with known PCa risk-associated SNPs in regions of
AR binding sites genome Fisher's exact test
<10-2 6/139 (4.3%) 29/5847 (0.5%) 0.0001
<10-3 6/23 (26.1%) 20/821 (2.4%) 0.00003
<10-4 5/7 (71.4%) 14/132 (10.6%) 0.0005
<10-5 3/3 (100%) 9/21 (42.9%) 0.22
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The results were based on the combined GWAS dataset of JHH and CGEMS

JHH: Johns Hopkins Hospital

CGEMS: National Cancer Institute Cancer Genetic Markers of Susceptibility

Known prostate cancer (PCa) risk-associated SNPs were identified from previous GWAS (1-15).

Discussion

Utilizing GWAS data from two independent PCa association study populations, we obtained two new pieces of statistical evidence supporting an association of sequence variants in regions containing AR binding sites with PCa risk. The first finding was the statistically significant enrichment of PCa risk-associated SNPs in genomic regions containing AR binding sites. The second finding was a significantly higher proportion of overlap between established PCa risk-associated SNPs from previous GWAS, and the newly observed PCa risk-associated SNPs in these AR blocks, compared to other blocks in the genome. These two pieces of evidence, together with our previous result from a bioinformatics analysis demonstrating that 11 of the 33 known PCa risk-associated SNPs reside in regions containing AR binding sites (20), suggest that sequence variants in regions containing AR binding sites are associated with PCa risk. The importance of the findings lies in the provision of a common, potential pathway by which many of the SNPs with strong statistical associations with PCa risk but unknown function act to affect this risk. The findings are plausible because androgen and AR have long been demonstrated to play a critical role in prostate carcinogenesis and because AR binding sites are critical for AR mediated androgen action.

Detailed examination of two exemplified genomic regions where AR binding sites were mapped by Chip-on-chip and SNPs within the regions were highly associated with PCa risk provided further insight on the potential molecular mechanisms of these genetic findings. The first involves the 8p21 genomic region which contains the NKX3.1 gene that codes for an AR regulated prostate-specific homeodomain-containing transcription factor involved in prostate differentiation and tumor suppression. Sequence variants in the vicinity of NKX3.1 have been associated with PCa risk by GWAS in several independent study populations (11). In our combined analysis of the JHH and CGEMS dataset, multiple SNPs in a ∼50 kb region that includes the NKX3.1 gene, were strongly associated with PCa (P <1.0 × 10-5) (Figure 2a, light blue bar). Interestingly, a cluster of five AR binding sites is mapped within or near the ∼50 kb PCa risk-associated region. In contrast, no other AR binding sites mapped within the more than 1-Mb interval flanking this region (Figure 2b, short horizontal red and blue lines). The validity of ChIP-on-chip defined AR binding sites at this region was supported by a recently published study where three AREs were identified at the 3′ UTR of NKX3.1 (Figure 2c, three short vertical bars) (30). As shown in Figure 2d, the ARE1 contains 22 bp (sequences in black bold) and is within the 1,470 bp AR binding site mapped by ChIP-on-chip (grey highlighted). Multiple SNPs are listed in this region by the NCBI database. Genotype data were available for two of these SNPs (rs1567669 and rs4872176, red box) in the JHH study. Both SNPs were significantly associated with PCa risk (P =10-5 and 10-6, respectively). However, the closest of these two SNPs (rs4872176) remains 46 bp telomeric to the ARE1. Experiments, beyond the scope of this genetic study, are needed to test whether these sequence variants in the ARE1, and/or variants across the broader region of the AR binding site, may affect the AR mediated action and development of PCa.

Figure 2.

Figure 2

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PCa risk-associated SNPs and AR binding sites near NKX3.1 at 8p21: a) association of SNPs at 8p21 with PCa risk in the combined JHH/CGEMS analysis, b) AR binding sites at the region defined by ChIP-on-chip method (Wang et al. 2009), c) AREs of NKX3.1 reported by Thomas et al (2010), d) DNA sequences of the most centromeric AR binding site at the region, NKX3.1 ARE1, and known SNPs.

The second region is at 8q24, where multiple independent PCa risk associations have been strongly implicated in a ‘gene desert’ region (11,31-32). In contrast to NKX3.1, this region has not been previously associated with AR regulation, and the molecular mechanism by which SNPs at 8q24 affect PCa risk is unclear. Five broadly defined and independent PCa risk-associated regions at 8q24 have been documented by previous GWAS (Figure 3a, short horizontal blue bars). PCa association at regions 1, 2, 3, and 5 were also found in the combined JHH and CGEMS study (Figure 3b, blue dots). Within a 1.3 Mb span at 8q24 that encompasses the five broadly defined PCa risk-associated regions and two known genes (MYC on the telomeric side and FAM84B on the centromeric side), regions containing AR binding sites generally concentrated in five broad PCa risk-associated regions (Figure 3c). In particular, three PCa risk-associated regions at 8q24 (region 1, 2, and 4, dotted vertical lines) overlapped with regions containing the AR binding site. The region 3 of 8q24, a locus that is associated with risk to PCa and several non-androgen related cancers, including colorectal and ovarian cancer (33-34), did not overlap with an AR binding site. Of note, a SNP in PCa risk-associated region 3 (rs6983627) was recently reported by another group to have an allelic specific effect on a novel prostate enhancer whose expression mimics that of the MYC gene, suggesting that region 3 may affect PCa development through another mechanism (35). We also used Chromatin Immunoprecipitation data and real-time PCR to confirm the AR binding sites within the 8q24 PCa-associated region in the LNCaP cell line (Supplementary methods). All AR binding sites (LNCaP 1-4) detected by ChIP-on-chip at the 8q24 region were confirmed (Supplementary Figure 1). These findings may suggest a novel androgen-dependent mechanism that explains some of the PCa associations at 8q24.

Figure 3.

Figure 3

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PCa risk-associated SNPs and AR binding sites at 8q24: a) association of SNPs at 8q24 with PCa risk in a large published study (Olama et al 2009), b) association of SNPs at 8q24 with PCa risk in the combined JHH/CGEMS analysis, c) AR binding sites at the region defined by ChIP-on-chip methods (Wang et al. 2009), d) known genes at the region. The dashed vertical lines indicate SNPs that are located in the AR binding region.

Caution should be exercised when interpreting these results, as this study has several limitations. Given the study design, the statistical evidence presented in this study may represent Type I error (false positive). It is noted that multiple P-value cutoffs (10-2, 10-3, 10-4, and 10-5) were used to test for an enrichment of PCa risk-associated SNPs in genomic regions containing AR binding sites (Figure 1), and to test whether a higher proportion of the blocks containing AR binding sites are associated with established PCa risk SNPs from GWAS (Table 1). The primary purpose for utilizing these different P-value cutoffs was to demonstrate that similar results could be obtained regardless which cutoff was used. However, we recognize that results from different P-value cutoffs were dependent. For example, the number of blocks significantly associated with PCa risk at P < 10-2 included the number of blocks significantly associated with PCa risk at P < 10-3, 10-4, and 10-5. Therefore, it is difficult to assess the true statistical significance of these findings. Further replication in independent study populations would provide the best assessment of the validity. Second, it is important to emphasize that this study merely provides statistical evidence for a novel hypothesis that some PCa risk-associated sequence variants may mediate their activity through interaction with AR binding sites. These results are hypothesis generating and should ultimately be tested through in-depth molecular analyses. For example, experiments can be designed to test whether implicated SNPs in the AR binding sites affect local AR transcriptional activity, the association of AR or AR co-regulators with chromatin, and androgen signaling. These potential studies, which are beyond the scope of this manuscript, may lead to a better understanding of prostate carcinogenesis. Third, in-depth analyses should be performed to better define AR binding sites. The current AR binding sites were detected using ChIP-on-chip technology in two cell lines. A ChiP-seq approach that combines ChIP and next-generation sequencing could increase the resolution thus better define AR binding sites. When this method was recently applied in other PCa cell lines, it confirmed previously defined AR binding sites and allowed for the identification of novel AR binding sites (36). In addition, the identification of AR binding sites in normal prostate tissues is probably more relevant because the potential influence of SNPs on AR binding sites most likely occurs earlier in normal tissues, prior to the development of PCa. Lastly, additional fine mapping genetic analyses could also be performed by utilizing novel SNPs that were recently discovered in the 1,000 Genomes Project (1KG) (37). However, the false discovery rate of SNPs is relatively high, with an estimated ∼10% in the low coverage pilot study of the 1KG study. This limitation may be overcome in the future, when higher quality catalogues of SNPs, and SNP genotyping chips that include SNPs identified from the 1KG project, become available.

In summary, these findings provide novel statistical evidence for a mechanism by which inherited sequence variants in AR binding sites influence PCa risk. The significance of this novel pathway analysis likely goes beyond AR binding sites and PCa risk. This approach could be applied to sequence variants in other transcription factor binding sites and in other disease settings.

Supplementary Material

Supplementary Methods
Supplementary Table 3

Acknowledgments

The authors thank all the study subjects who participated in this study. The authors also thanks for the National Cancer Institute Cancer Genetic Markers of Susceptibility Initiative (CGEMS) for making the data available publicly.

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Associated Data

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Supplementary Materials

Supplementary Methods
NIHMS379957-supplement-Supplementary_Methods.doc (1.5MB, doc)
Supplementary Table 3
NIHMS379957-supplement-Supplementary_Table_3.doc (1.5MB, doc)

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