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. Author manuscript; available in PMC: 2011 Nov 1.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2010 Oct 26;19(11):2869–2876. doi: 10.1158/1055-9965.EPI-10-0601

GENOME-WIDE ASSOCIATION STUDY OF PROSTATE CANCER MORTALITY

Kathryn L Penney 1,2, Saumyadipta Pyne 3, Fredrick R Schumacher 4, Jennifer A Sinnott 2,5, Lorelei A Mucci 1,2, Peter Kraft 1,5, Jing Ma 2, William K Oh 3,6, Tobias Kurth 1,7, Philip W Kantoff 3, Edward L Giovannucci 1,2,8, Meir J Stampfer 1,2,8, David J Hunter 1,2,8,9, Matthew L Freedman 3,9
PMCID: PMC3197738  NIHMSID: NIHMS231484  PMID: 20978177

Abstract

Background

A pressing clinical issue in prostate cancer (PCa) is to distinguish which men will have an indolent or aggressive course of disease. Clinical variables such as Gleason grade and stage are useful predictors of lethal cancer; however, the low predictive values of the common Gleason scores, changes in grading over time, and earlier diagnosis of patients due to screening limits their clinical utility. Identifying genetic variants associated with lethal PCa could inform clinical decision making.

Methods

We conducted a genome-wide association study comparing lethal PCa cases to cases surviving at least ten years beyond their initial diagnosis. Genotyping was performed with the Affymetrix 5.0 chip (~500,000 single nucleotide polymorphisms (SNPs) and 1483 copy number variants (CNVs)) on DNA from participants in the Physicians’ Health Study and Health Professionals Follow-up Study (196 lethal cases, 368 long-term survivors). After excluding SNPs and individuals based on quality control criteria, logistic regression assuming an additive model was performed using PLINK software.

Results

No SNP reached genome-wide significance (p≤1×10−7), however three independent SNPs had p<1×10−5. One top-ranked SNP replicated (p=0.05) in an independent follow-up study. While no CNV had genome-wide significance, 14 CNVs showed nominal association with PCa mortality (p<0.05).

Conclusions

No variants were significantly associated at a genome-wide level with PCa mortality. Common genetic determinants of lethal PCa are likely to have odds ratios <2.0.

Impact

Genetic markers identified could provide biological insight to improve therapy for men with potentially fatal cancer. Larger studies are necessary to detect genetic causes of PCa mortality.

Keywords: genome scan, prostate cancer, mortality

Introduction

One of the most urgent clinical questions in prostate cancer (PCa) is how to predict an individual’s course of disease at the time of diagnosis. PCa is the most common incident cancer (other than non-melanoma skin cancer) and the second leading cause of cancer mortality in men in the United States (1). However, the vast majority of PCa patients will not die from their cancer. While early detection and treatment play a role in cancer survival, some treated individuals still succumb to PCa while many survive without medical intervention. A recent large trial found that men randomized to prostatectomy had only a small (though significant) absolute reduction in PCa death compared to those randomized to watchful-waiting (2). Albertsen et al. (3) followed 767 men with conservatively treated localized PCa for over twenty years and observed that the majority of men (70%) did not die of PCa.

What causes one PCa patient to develop metastases or die from their cancer while others survive with the disease for many years? At present, the most utilized predictors of outcome at diagnosis are age, clinical stage, PSA level, and Gleason score. Gleason score, a measure based on the histological patterns of prostate tumors, is currently one of the best predictors. In a study using re-reviewed Gleason score from prostatectomy specimens, those with Gleason 8 cancers had a hazard of lethal cancer (dying from PCa or developing distant metastases) that was 7.4 (95% confidence interval (CI): 2.5–22) times higher than those with Gleason 3+4; cases with Gleason 9–10 had an even higher risk of lethal cancer (hazard ratio (HR)=19.1; 95% CI: 7.4–49.7) (4). However, the positive predictive value (PPV) for mortality of a higher Gleason score, including the most common Gleason 7 as well as 8–10, is only 29% (5), and therefore far from optimal. Gleason score has additional limitations as a predictor because of scoring changes over time (6, 7) and inter-observer variability (8, 9).

Epidemiological and experimental evidence supports the hypothesis that aggressive cancer has an inherited component. A recent study showed concordance of survival and PCa mortality among fathers and sons with PCa, implying that prognosis itself may have a hereditary component (10). Laboratory experiments using a highly metastatic mouse mammary model crossed with several different strains showed that the genetic background of an animal can influence the metastatic efficiency (11). Further quantitative trait mapping work identified regions on chromosome 19 that were significantly associated with metastatic efficiency, suggesting that inherited variation may influence metastasis (12). Thus far, genetic studies in humans have focused on Gleason score as a proxy for aggressive disease. Several regions have been implicated in linkage scans, but three of the regions (5q31–33, 7q31–33 and 19q12-q13.3) were strongly significantly associated with high-grade cancer (p<0.001) and replicated in at least two independent studies suggesting a locus may be present under these peaks (13–16).

In PCa genetic association studies for risk, a combined analysis of two GWAS identified a variant at chromosome 9q33.2 in a putative tumor suppressor gene (DABP2IP) that was associated with risk of aggressive PCa, defined by Gleason grade and clinical stage (17). Another study found that the TT genotype of rs4054823 at 17p12 that increased risk of aggressive cancer compared to non-aggressive cancer, again defined by clinical variables (18). A germ-line deletion at 2p24.3 was more strongly associated with the risk of aggressive cancer than non-aggressive cancer (19).

Substantial longitudinal follow-up is required to capture information on PCa mortality, so this outcome is studied less frequently than Gleason score. However, we believe that a large-scale genetic study for the most important PCa outcome is crucial to improve our understanding of PCa aggressiveness. We therefore performed a genome-wide association study (GWAS) for PCa mortality in the Physicians’ Health Study and Health Professionals Follow-up Study, with a replication study in the Dana-Farber Harvard Cancer Center SPORE (Gelb Center) case-series. In addition to examining the association of genotypes, we also evaluated whether copy number variants (CNVs) were associated with PCa mortality.

Methods

Study Population

Physicians’ Health Study (PHS)

The PHS began as a randomized, double-blind trial of aspirin and β-carotene in the prevention of cardiovascular disease and cancer among 22,071 healthy US physicians; written consent was obtained from each participant at the time of initial enrollment and the investigation was approved by the Human Subjects Committee at Brigham and Women’s Hospital. Men were excluded if they had any serious medical conditions at baseline including all cancers (except non-melanoma skin cancer). Blood samples were collected from 68% of the physicians in 1982–1984, as described previously (20). Participants are followed through annual questionnaires to collect data on diet, health and lifestyle behaviors, and medical history, and biannually through postcards to ascertain health endpoints, including PCa. All self-reported PCa cases are verified through medical record and pathology review. Through this systematic medical record review we also abstract data on clinical information, such as Gleason score. Cause of death is determined by review of death certificates, medical records, and information from the family by a panel of three physicians. There is a high follow-up rate for both cancer incidence (96%) and mortality (98%). Metastases are reported on follow-up questionnaires sent to all men living with PCa and are confirmed through medical record review.

For the current study, we included incident PCa diagnosed between 1982 and 2003, and restricted participants to self-reported Caucasians to reduce potential population stratification. Due to cost restraints, we were unable to genotype all PHS PCa cases on whom blood had been collected. We therefore examined the two extremes of PCa cases: long-term survivors (patients who survived a minimum of 10 years after diagnosis until death or end of follow-up (March 1, 2008) and did not develop metastases to bone or organs or die from PCa; n=415) and lethal PCa cases (patients who developed metastases to bone or organs after diagnosis or died from PCa; n=176).

Health Professionals Follow-up Study (HPFS)

The HPFS, an ongoing prospective cohort study on the causes of cancer and heart disease in men, consists of 51,529 U.S. health professionals who were aged 40–75 years in 1986 (21). At baseline and then biennially participants responded to a mailed questionnaire that included questions on demographics, lifestyle, and medical history. Between 1993 and 1995, 18,018 of the men provided a blood specimen. When a participant reports a PCa diagnosis medical and pathology records are obtained. Study investigators review these records to confirm the diagnosis and to abstract stage at diagnosis and Gleason grade. Deaths among cohort members were identified by reports from next-of-kin, the postal service, or searches of the National Death Index. In order to increase the number of lethal cancers in this study, we included 46 PCa deaths from the HPFS (self-reported Caucasian) among cases diagnosed between 1993 and 2000; these were selected from a larger nested case-control study and had the most available DNA from a total of 53 PCa deaths.

Dana-Farber Harvard Cancer Center SPORE (Gelb Center)

The Gelb Center is a case series of PCa patients diagnosed between 1976 and 2007. A detailed description of this study has been published previously (22). The study captures detailed clinical information from multiple sources, including medical records and patient registration, and a blood sample collected after diagnosis. Follow-up of the participants occurs at clinic visits to the Dana-Farber Cancer Institute and by searching the National Death Index. Because cause of death is not always available or known, if an individual was known to have metastases before their death they were assumed to have died from PCa. For this study, after restricting to self-reported Caucasians, we included 155 long-term survivors (end of follow-up July 1, 2007) and 500 lethal cases as a replication set.

GWAS

Affymetrix Scan

The samples from the PHS and HPFS were included in the genome scan. DNA was extracted from peripheral blood samples for all participants. Genotyping was performed with the Affymetrix 5.0 SNP chip, which contains probes for 500,568 SNPs. Briefly, approximately 500 ng of DNA from each sample is digested with Nsp and Sty restriction enzymes. The digested segments were ligated to enzyme specific adaptors which incorporate a universal PCR priming sequence; PCR amplification using universal primers was performed in a reaction optimized to amplify fragments. The products are fragmented, end-labeled with biotinylated nucleotides, and hybridized to a chip and detected (23). The resulting intensities for each allele are used to make a genotype call. The “Birdseed” calling algorithm, an updated version of the Robust Linear Modeling using Mahalanobis distance (RLMM) calling algorithm developed at the Broad Institute of Harvard and MIT, was used for this study (24). More information on the technology, calling algorithm, and SNP coverage can be found at (25).

Samples and Quality Control

A total of 637 unique samples from PHS and HPFS were included in this study; deaths and long-term survivors were interspersed across seven 96-well plates and laboratory personnel were blinded to outcome. Each plate had two empty wells (negative controls) as well as two duplicates to be used for quality control.

We assessed the genotype concordance of 458 SNPs from 500 kb regions of chromosomes 1, 5, 10, 15, and 20 for the 14 duplicate pairs (concordance=99.9%). We also compared the genotype calls for 31 SNPs that had previously been genotyped on a subset of these PHS participants; concordance was 99.3% for >14,000 genotypes.

Data Analysis

The PLINK program(26) was used to analyze these genome scan data (27). Forty-six individuals (33 long-term survivors, 13 deaths) with <90% of genotype calls made were removed from the analysis; the average call rate in the remaining individuals was 98.8%. Of the SNPs genotyped, SNPs missing >10% of genotypes (14,704), with minor allele frequency (MAF) <1% (68,603), or with Hardy-Weinberg Equilibrium p<1×10−6 (1,979) were excluded, leaving 419,613 SNPs for analysis.

To address potential remaining population stratification, we utilized the Eigenstrat program (28). We ran this program for all participants with the default parameters (5 outlier iterations across the top 10 eigenvectors, with outliers exceeding 6 standard deviations along a top principal component excluded), and output the first two eigenvector values. Several individuals were not assigned values along these eigenvectors due to missing data (as described above) or were designated outliers (14 long-term survivors, 12 deaths); these individuals were excluded from further analysis. Using PLINK, for the main analysis we ran an unconditional logistic regression model adjusting for the first two eigenvectors (excluding 1 HPFS death missing age at diagnosis), outputting the additive model results for the association of each SNP with lethal PCa (n=196) versus long-term survival (n=368). We then ran secondary analyses additionally adjusting for age at diagnosis and restricting to men with Gleason score of 7.

Follow-up Study

The Gelb Center samples were utilized for a genetic replication study. We selected and designed assays for SNPs with p<1×10−3 that fell in previously identified linkage peaks for Gleason score (chr5q31–33 n=6, chr7q31–33 n=1); we then selected markers to capture the independent variation with p<5×10−4 (n=72). Genotyping was performed with Sequenom iPLEX matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry technology; see (29) for reaction details. The association of the additive model of these SNPs with lethal PCa versus long-term survival was performed using unconditional logistic regression. SNPs were excluded from analysis if they had <90% genotyping success rate. Of the 79 SNPs genotyped in the Gelb samples, 11 failed genotyping quality control. Replication was declared only if p≤0.05 and the direction of the effect was the same as in the GWAS; for the replicated SNP, a joint analysis with the original GWAS data was performed as a meta-analysis with a random-effects model. Analysis was performed with SAS v9.1 statistical software.

Copy Number Polymorphism analysis

We analyzed SNP chip based copy number polymorphism data as generated by the CNV detection software Canary (30) in the form of summarized intensity scores for 1483 CNVs and 565 subjects. We followed the subject filtering criteria as described above in our genotype analysis; individuals were excluded who were missing considerable data or were found to be outliers by Eigenstrat. Then we followed a likelihood ratio approach for testing association between each CNV and the binary status of mortality considered as a trait. The approach jointly fits two linear models, as outlined in Barnes et al. (31), and is described as follows. The first model classifies the summarized intensities for each CNV by fitting a finite mixture of Gaussian densities via an EM based algorithm that uses Bayesian Information Criterion (BIC) to select the optimal number of classes. Upon convergence, the classification assigns every individual subject to a copy number genotype, and given an optimal model with multiple copy number classes, we tested for its association with the subject’s trait with this joint model. This is done by fitting of a generalized logit linear model to test the null hypothesis H0 that there is no association between a subject’s copy number genotype and his binary PCa mortality trait (in this case, lethal/indolent). If the fitting is correct and there is indeed no association, then the computed likelihood ratio (LR) statistic is χ2 distributed with 1 degree of freedom, which leads to a corresponding p-value of association. The plots and statistics for CNV classification and the associated distribution of trait were generated with the BioConductor package CNVtools.

Results

GWAS results

A description of the PHS and HPFS participants is provided in Table 1. Although participants were restricted to self-reported Caucasians, residual population stratification was addressed with the Eigenstrat program (28). The correlation of eigenvectors 1 and 2 with outcome status was 0.046 and 0.009, respectively, demonstrating that overall population structure was not strongly related to outcome; the first two eigenvectors for the lethal PCa cases and long-term survivors are shown in Supplementary Figure 1.

Table 1.

Description of genome-wide association study and replication study participants

PHS† HPFS† Gelb Center
Clinical Characteristics lethal (n=176) long-term survivors (n=415) lethal (n=46) lethal (n=500) long-term survivors (n=155)
age at diagnosis, mean (s.d.) 70.4 (8.4) 67.8 (6.6) 71.8 (7.3) 62.4 (8.3) 61.8 (7.4)
Gleason score*, % (n=130) (n=362) (n=36) (n=433) (n=134)
 2–6 26.2 58.3 19.4 18.2 53.7
 7 36.2 32.3 47.2 32.8 34.3
 8–10 37.7 9.3 33.3 49.0 11.9
Clinical stage, % (n=161) (n=404) (n=38) (n=360) (n=97)
 T1, T2 58.4 91.6 47.4 65.8 99.0
 T3, T4, N1, M1 41.6 8.4 52.6 34.2 1.0
follow-up, median yrs (range) 5.5 (0.1–17.9) 15.4 (10–25.3) 4.9 (0.1–11.1) 5.7 (0.3–24.4) 13.7 (10.5–21.8)
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†

values for all cases and the subset included in the final analysis are comparable

*

Gleason score was only from biopsy for Gelb Center; preferentially from prostatectomy then from biopsy for PHS and HPFS

A set of 419,613 SNPs passed quality control and were used for subsequent analyses (see Methods). A q-q plot of the results compares the chi-square values obtained in this study with the expected distribution under the null hypothesis of no association between genetic variation and mortality (Figure 1). Although no SNPs reached genome-wide significance (p≤1×10−7), three independent SNPs had p<1×10−5; the plot of p-values (Figure 2) shows that there are peaks on chromosome 2q31.2, 11q12.2 and 11q14.1. The results for all SNPs with p<1×10−3 (n=277) are provided in Supplementary Table 1.

Figure 1.

Figure 1

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quantile-quantile plot, comparing observed statistics for all results to those expected based on the null distribution

Figure 2.

Figure 2

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p-values for the association of SNPs with PCa mortality plotted by chromosome and position

To determine the associations of SNPs on mortality independent of their possible associations with age at diagnosis, we ran the analysis adjusting for age at diagnosis (continuous) in addition to the top 2 eigenvectors. When adjusting for age at diagnosis, there are 3,767 results with p<0.01; 19% of these results are not among the 3,803 results with p<0.01 from our main analysis. However, the top SNPs from the non-age adjusted results (Supplementary Table 1) all have p<0.005 in the adjusted analysis, suggesting the overall effect of SNPs on mortality through age at diagnosis may not be substantial. We also examined the association of SNPs with lethal cancer restricting to cases with Gleason 7; again, no SNPs reached genome-wide significance. With this much smaller number of participants, half of the SNPs with p<0.001 had p<0.05 in the main analysis.

We examined the results for previously identified PCa risk SNPs in our scan. Sixteen of the 31 confirmed risk SNPs compiled by Varghese and Easton (32) were either directly genotyped in our scan or had a proxy with R2>0.8. The most significant finding was for rs16901979 where the risk allele decreased the probability of lethal cancer (OR=0.35, p=0.006); all results are reported in Supplementary Table 2.

Replication study results

Since the majority of the top ranked SNPs from the scan will be false positives, we performed a replication study in the Dana-Farber Harvard Cancer Center SPORE Gelb Center (500 lethal cases, 155 indolent). We selected top ranked SNPs (p<10−3) that were located in previously identified Gleason linkage peaks (n=7). We also then selected markers to capture the independent variation with p<5×10−4 (n=72). Of the seventy-nine SNPs selected, 68 were successfully genotyped. Six of these had p≤0.05, but for five the direction of the effect was not consistent with the scan. The one SNP that replicated with the effect in the same direction, rs6973814 (odds ratio=1.95, 95% CI: 1.01, 3.79; p=0.05), was ranked 66th in the original GWAS (odds ratio=3.07; p=0.0003) and is located on chromosome 7q11.2 (nearest gene, AUTS2, 600kb away). In a joint analysis with the scan results, the combined odds ratio was 2.50 (95% CI: 1.60, 3.90; p=6×10−5). All Gelb Center results are in Supplementary Table 3.

CNV results

The model fitting results and number of classes for all 1483 CNVs are provided in Supplementary Table 4. For the copy number variants where the classification (based on iterative EM modeling) converged and produced more than one CNV genotype class (N=341), we examined the association between the number of copies an individual carries and lethal PCa. Fourteen CNVs had a nominal p<0.05; however, none remained significant after correction for multiple testing (Supplementary Figure 2 and Table 2).

Table 2.

Significant associations (p<0.05) between CNVs and PCa mortality

CNV id Chromosome Start Stop Size (kb) P-value Gene (Location)
CNP11198 7p21 17060348 17062853 2.51 0.001
CNP11500 8q23 107607836 107615669 7.83 0.002 OXR1 (chr8:107351648-107834097)
CNP399 3p22 37957108 37961932 4.82 0.002 CTDSPL (chr3:37878672-38000964)
CNP10373 2q14 125368402 125374832 6.43 0.005 CNTNAP5 (chr2:124499333-125389333)
CNP10161 1q24 166355735 166358038 2.30 0.007 GPR161 (chr1:166320620-166372248)
CNP147 1q31 194997658 195068695 71.04 0.008 CFHR3 (chr1:195010552-195029496)
CNP10030 1p36 15665011 15683808 18.80 0.012 ELA2A (chr1:15655810-15671169)
CNP2113 15q24 74678296 74682830 4.53 0.014 SCAPER (chr15:74427591-74963247)
CNP2430 19q13 56834427 56840009 5.58 0.021 SIGLEC14 (chr19:56837617-56841944)
CNP207 2p22 34552819 34590561 37.74 0.023
CNP10834 4q32 162413794 162424561 10.77 0.031
CNP2150 16p12 19853151 19874863 21.71 0.033
CNP211 2p22 35831294 35841451 10.16 0.036
CNP11018 6p25 5978930 5979435 0.51 0.038
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Discussion

A number of recent GWAS and follow-up replication studies have identified over twenty bona fide genetic PCa risk loci (33–40). Importantly, these studies have provided a new look into the biology of developing the disease. Some of these variants have been tested for association with aggressiveness, typically using the Gleason grade as a proxy for aggressive disease. However, identifying genetic determinants of lethal cancer could improve on the current clinical predictive ability at diagnosis. Understanding who would and who would not benefit from intervention could impact the selection of appropriate medical therapy for the individual, preventing unnecessary treatments and the physical and psychological side effects. In addition, the markers themselves may provide biological insight that could lead to improved therapy for those with potentially fatal cancer.

In this GWAS for lethal cancer, although no SNPs reached genome-wide significance, we identified one top-ranked SNP that replicated in an independent population. The closest gene to the one SNP that was replicated is AUTS2. A recent study based on mRNA expression data reported that this gene was included in the top 100 potential genetic mediators for non-recurrent primary PCa (41), suggesting a possible biological function.

As noted by McCaroll (42), it is increasingly possible to extend GWAS to examine CNVs and their association with disease phenotypes. In recent years, the SNP arrays have been redesigned to contain probes at the majority of CNVs, which in turn take advantage of the recent high-resolution maps of the CNV locations (43, 44). In this direction, the present GWAS was extended to study CNV in the same SNP array data based on 1483 mapped CNVs using a robust statistical modeling algorithm for classification. While no CNV achieved genome-wide significance, we identified 14 CNVs nominally associated with PCa mortality. Subsequent data mining with alternate modeling strategies or larger studies may reveal further associations.

PCa mortality is one of the most important phenotypes of this disease. Unfortunately, due to the long follow-up time and the cost necessary to obtain this information, few studies have information on survival and cause of death or the numbers of lethal cases necessary to study this outcome. A major strength of this study is its ability to examine the primary prostate cancer endpoint, lethal disease, with a substantial number of participants from cohorts that have been followed for decades. The top results were then evaluated in a large case-series that also captures survival data.

Figure 3 demonstrates we are only powered to detect relatively strong effects (e.g., OR>2 with MAF>20%). While this is a limitation of our study, it also provides insight into the genetic variants involved in PCa aggressiveness. Based on our data, no common variant will have a large effect on aggressiveness, but rather will most likely have the same magnitude of effect as the alleles that have previously been identified for risk. Although our one SNP that did replicate had a larger combined OR of 2.5, in the replication dataset alone the OR=1.95, suggesting that the initial finding is likely overestimating the magnitude of the effect.

Figure 3.

Figure 3

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Power for genome scan

Power was calculated using the number of cases included in the final analysis (196 lethal PCa, 368 long-term survivors) with an alpha-level of 1×10−7 across a range of allele frequencies and additive model odds ratios

Another possible limitation (albeit one that exists in all studies of PCa mortality that are conducted in screened and treated populations) is misclassification of the outcome. Individuals who were labeled as having indolent cancer because they survived at least ten years without developing metastases or dying of cancer may only be in this category because they received aggressive medical treatment, without which they would have died. However, as the results of the Swedish randomized trial of prostatectomy versus watchful waiting suggest, the number needed to treat to save the life of one man with PCa is 19 (45); thus, the potential impact of misclassification is likely to be minimal. Additionally, it is important to investigate if these genetic variants predict PCa mortality independent of clinical variables such as treatment or Gleason score; however, missing data limits our ability to conduct these analyses. We did perform an analysis restricting to the most common Gleason score of 7; while results were somewhat similar to the overall analysis, a larger future study examining these associations among men with Gleason 7 would be interesting and could identify SNPs that are associated with lethal cancer independent of their effects on Gleason. A limitation in the CNV analysis is the number of probes included on this Affymetrix chip; a more comprehensive study of CNVs with PCa mortality should be performed.

Although several SNPs have been identified that are associated with risk of prostate cancer, these SNPs in general have not been found to confer an increased risk of aggressive compared to indolent disease. If lethal prostate cancer does indeed have a genetic component, this suggests that genetic variants determining aggressive disease are different from those that confer overall risk. It would be of clinical utility if future studies specifically focused on attempting to differentiate lethal from indolent cancer using germline genetic scans and follow-up studies.

Supplementary Material

1

Acknowledgments

This work was supported by a grant from the Doris Duke Clinical Scientist Development Award. The laboratory work at the Broad Institute Center for Genotyping and Analysis was supported by grant U54 RR020278 from the National Center for Research Resources. The PHS was supported by grants CA-34944, CA-40360, and CA-097193 from the National Cancer Institute and grants HL-26490 and HL-34595 from the National Heart, Lung, and Blood Institute, Bethesda, Maryland. The HPFS was supported by CA-55075 from the National Cancer Institute. Additional funding provided by the Dana-Farber/Harvard Cancer Center Prostate Cancer SPORE (P50 CA090381-08). The genotyping was performed by the Broad Institute Center for Genotyping and Analysis, which is supported by U54 RR020278 from the National Center for Research Resources. KLP was supported by a National Research Service Awards (T32 CA009001-34).

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