Abstract
BACKGROUND
Prostate cancer (PCa) is a common malignancy and a leading cause of cancer death among men in the United States with African-American (AA) men having the highest incidence and mortality rates. Given recent results from admixture mapping and genome-wide association studies for PCa in AA men, it is clear that many risk alleles are enriched in men with West African genetic ancestry.
METHODS
A total of 77 ancestry informative markers (AIMs) within surrounding candidate gene regions were genotyped and haplotyped using Pyrosequencing in 358 unrelated men enrolled in a PCa genetic association study at the Howard University Hospital between 2000 and 2004. Sequence analysis of promoter region single-nucleotide polymorphisms (SNPs) to evaluate disruption of transcription factor-binding sites was conducted using in silico methods.
RESULTS
Eight AIMs were significantly associated with PCa risk after adjusting for age and West African ancestry. SNP rs1993973 (intervening sequences) had the strongest association with PCa using the log-additive genetic model (P = 0.002). SNPs rs1561131 (genotypic, P = 0.007), rs1963562 (dominant, P = 0.01) and rs615382 (recessive, P = 0.009) remained highly significant after adjusting for both age and ancestry. We also tested the independent effect of each significantly associated SNP and rs1561131 (P = 0.04) and rs1963562 (P = 0.04) remained significantly associated with PCa development. After multiple comparisons testing using the false discovery rate, rs1993973 remained significant. Analysis of the rs156113–, rs1963562–rs615382l and rs1993973–rs585224 haplotypes revealed that the least frequently found haplotypes in this population were significantly associated with a decreased risk of PCa (P = 0.032 and 0.0017, respectively).
CONCLUSIONS
The approach for SNP selection utilized herein showed that AIMs may not only leverage increased linkage disequilibrium in populations to identify risk and protective alleles, but may also be informative in dissecting the biology of PCa and other health disparities.
Keywords: ancestry informative markers, African-American, health disparities
BACKGROUND
Prostate cancer (PCa) is a common malignancy in aging men and a leading cause of cancer death among men in the United States. There are three well-established risk factors for PCa: age, ethnicity and family history,1 but the molecular mechanisms underlying its development and progression remain poorly understood. With regards to ethnicity, the incidence and mortality rate of PCa among African-American (AA) men is twofold higher when compared with European Americans.
Recent years have seen significant progress in understanding population-level susceptibility to common cancers. Indeed, the previous candidate gene-based strategy was largely disappointing, producing conflicting results and publication bias. However, genome-wide association studies utilizing large numbers of cases and controls, supported by appropriate validation sets, have produced robust data in the field of PCa genetics.
For example, single-nucleotide polymorphisms (SNPs) in ribonuclease L (RNASEL), vitamin D receptor (VDR) and cyto-chrome P3A5 (CYP3A5) have been the subject several meta-analyses2–6 and have shown inconsistent associations with PCa risk.7–19 Previous studies in our laboratories have shown associations with PCa in the aforementioned regions.20–23 Possible reasons that may explain these inconsistencies include sample size, ethnicity and end points. These three loci emerged before the genome-wide association studies era and indeed the literature is conflicting regarding possible risk associations. Nevertheless, sophisticated efforts to tackle the ‘old’ loci may still be well worth conducting.
The potential existence of population-specific genetic factors in PCa was evaluated through the use of ancestry informative markers (AIMs) in the aforementioned candidate genes in AA men. Therefore, we chose a panel of AIMs to explore the role of African ancestry in the genetic etiology of PCa in AAs. We hypothesize that AIMs may help track PCa risk alleles in specific populations, as well as provide clues that may help elucidate the mechanisms of this disease. In this study, we utilized AIMs within an average of 25 megabases (Mb) in either direction of RNASEL, VDR and CYP3A5 to help elucidate the role of ancestry in PCa susceptibility and to identify variants associated with PCa in AA men.
MATERIALS AND METHODS
Study population
This study was approved by the Howard University (HU) Institutional Review Board. Briefly, unrelated men were enrolled at the HU Cancer Center sites for genetic association studies of risk factors for PCa. All PCa cases were between 35 and 93 years of age and were diagnosed within 1 year of enrollment. The group of men consisted of 358 unrelated AA men recruited from the Washington, DC area through the Division of Urology at the HU Hospital and PCa screening at the HU Cancer Center between 2000 and 2004. All control subjects had PSA levels <4.0 ng ml–1 and normal digital rectal exams. Blood samples were collected from each subject. PCa cases were diagnosed by trans-rectal ultrasound-guided biopsy using standard saturation technique.
Selection of AIMs for genotyping
We selected a total of 77 AIMs as follows: 21 SNPs in 1q23–32, 36 SNPs in 7q21–34 and 20 SNPs in 12p12–q14. The AIMs were chosen to cover an average of 25 Mb upstream and/or downstream of RNASEL, VDR and CYP3A5. Delta between parental populations for all AIMs was >0.30 (based on HapMap data) (Supplementary Table 1).
Genetic ancestry estimation
In order to control for population stratification, West African ancestry was estimated in cases and controls using AIMs. Individual ancestry was determined for each individual using 77 AIMs selected from regions within an average of 25 Mb in either direction of RNASEL, VDR and CYP3A5. Global individual ancestry (% West African and % European) was calculated from the genotype data using the Bayesian Markov Chain Monte Carlo method implemented in the program STRUCTURE 2.1.24 These ancestry estimates were used as covariates in the regression models.
Genotyping and haplotyping
Genotyping was performed using pyrosequencing (Qiagen, Germantown, MD, USA) techniques. Briefly, DNA samples were PCR amplified using whole genome amplified DNA, forward and reverse primers, MgCl2, deoxy-nucleotide triphosphates and platinum Taq DNA polymerase (Invitrogen, Grand Island, NY, USA). PCR products were then pyrosequenced. Results were analyzed with the PyroMark Q24 software (Qiagen). Duplicate test samples and negative controls were included in each 96-well plate.
In silico analysis
For SNPs found in putative promoter regions or in intervening sequences, in silico methods using AliBaba2.1 (BIOBASE, Beverly, MA, USA) were employed to determine if any of the associated SNPs disrupted or resulted in the appearance putative transcription binding sites. Specifically, DNA sequences were gathered from NCBI (http://www.ncbi.nlm.nih.gov/).
Statistical analysis
The statistical analyses for the case–control study were done with the SAS/STAT software, version 9.1 (SAS Institute, Cary, NC, USA) and R statistical software (version 2.9.0) (Vienna, Austria). Allele frequencies in controls were tested for Hardy–Weinberg equilibrium using χ2 analysis or Fisher's exact test when appropriate. The association of disease status with genotype, SNP combination and haplotype was analyzed by logistic regression. For this study, the major allele found in people of African descent was considered the reference allele. For each SNP genotype, tests using the genotypic, dominant, recessive and log-additive genetic models were performed. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated and adjusting for age. Two-sided P-values of ≤0.05 were considered as statistically significant. Using the Bonferroni test and the false discovery rate (FDR) adjustment was made for multiple comparisons testing. We also tested the independent effect of each SNP by including the most significant SNPs with the use of a backward-selection procedure. If more than one SNP was selected for analysis, a free web-based program SNPstats25 was used as the application then assumes that haplotype analysis is appropriate. To test the combinatorial effects of the SNPs and haplotypes, frequencies were estimated using the implementation of the EM algorithm coded into the haplostats package.26
RESULTS
Demographics
The mean age of cases was 65.33 (s.d.±9.33) compared with 58.42 (s.d.±11.33) years among the controls (Table 1). West African ancestry was not significantly different between cases and controls (Table 1). The rate of concordance between duplicate samples was 499%. All SNPs were in Hardy–Weinberg equilibrium (P>0.05).
Table 1.
Demographic information for the study population
| Controls | Cases | P-value | |
|---|---|---|---|
| Total recruited | 197 | 161 | |
| Mean age | 58.42 ±11.33 | 65.33 ± 9.33 | <0.01 |
| Family history of PCa | 18 (9.1%) | 36 (22%) | <0.05 |
| Average PSA | 3.11 ± 12.18 | 85.01 ± 391.58 | < 0.0001 |
| (mean ± s.d.) % WA ancestry | 80% | 80% |
Abbreviations: PCa, prostate cancer; WA, West African.
Genotyping results
Region 1q23–32
In the 1q23–32 region, only 1 of the 21 SNPs interrogated, rs911964, was found to be associated with PCa risk in our population even after adjusting for age using the dominant (OR = 2.13, 95% CI: 1.06–4.28; P = 0.032) and log-additive models (OR = 2.00, 95% CI: 1.02–3.93; P = 0.041). After multiple comparisons testing, rs911964 lost its significant association. In silico analysis of rs911964 using Alibaba 2.1 saw disappearance of an hepatocyte nuclear factor-1C-binding site associated with the variant C (risk) allele.
Region 7q21–34
In the 7q21–34 region, 2 of the 37 SNPs upstream and downstream of CYP3A5 were found to be associated with PCa risk (Table 2). For SNP rs219821, found in the intervening sequence region of 7q21, the dominant model emerged as significant (OR = 2.01, 95% CI: 1.12–3.51; P = 0.013), after adjusting for age. For SNP rs8177113, which is found in the first intron of the gene Ephrin type-B receptor 6 (EPHB6), the recessive model was the only one to emerge as significant before and after adjusting for age (OR = 2.32, 95% CI: 1.08–5.01; P = 0.028). Notably, haplotype analysis revealed several areas of high linkage disequilibrium. Specifically, AIMs rs883403 and rs1011024, rs7779406 and rs8177113, and AIMs rs4987682, rs4987677, rs4987657, rs4987649 and rs4987622 were all in linkage disequilibrium >0.80. Although rs8177113 was in high linkage disequilibrium with rs7779406, there was no association found with the SNP. However, after adjusting each SNP for multiple testing using the Bonferroni test, neither SNP found in the 7q21–34 region remained significantly associated with PCa. In silico analysis of rs8177113 revealed the emergence of a RARalpha, specific protein 1 and disappearance of a CREBP transcription factor binding sites associated with the variant G (risk) allele.
Table 2.
SNPs associated with PCa risk
| SNP ID | Chromosome | Contig position | Gene | Genetic model | Unadjusted |
Adjusted |
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Genotype | Controls | % | Cases | % | OR | Lower | Upper | P- value | OR | Lower | Upper | P- value | |||||
| rs911964 | 1q25 | IVS | Dominant | TT | 129 | 81.10 | 125 | 86.80 | 1.00 | 1.00 | |||||||
| TC/CC | 30 | 18.90 | 19 | 13.20 | 1.58 | 0.85 | 2.95 | 0.15 | 2.13 | 1.06 | 4.28 | 0.03 | |||||
| Log-additive | 0, 1, 2 | 1.46 | 0.80 | 2.67 | 2.00 | 1.02 | 3.93 | 0.04 | |||||||||
| rs219821 | 7q21 | IVS | Dominant | CC | 55 | 37.70 | 58 | 48.70 | 1.00 | 1.00 | |||||||
| CT/TT | 91 | 62.30 | 61 | 51.30 | 1.50 | 0.92 | 2.43 | 0.10 | 2.01 | 1.12 | 3.51 | 0.01 | |||||
| rs8177113 | 7q21 | 3150468 | EPHB6 | Recessive | CC/CG | 160 | 85.10 | 140 | 92.10 | 1.00 | 1.00 | ||||||
| C>G | |||||||||||||||||
| GG | 28 | 14.90 | 12 | 7.90 | 2.07 | 1.02 | 4.23 | 0.04 | 2.32 | 1.08 | 5.01 | 0.03 | |||||
| rs1993973 | 12q13 | 19070231 | IVS | Dominant | GG | 62 | 38.30 | 32 | 25.20 | 1.00 | 1.00 | ||||||
| G>A | |||||||||||||||||
| GA/AA | 100 | 61.70 | 95 | 74.80 | 0.55 | 0.33 | 0.77 | 0.02 | 0.46 | 0.26 | 0.81 | 0.00 | |||||
| Recessive | GG/GA | 141 | 87.00 | 92 | 72.40 | 1.00 | 1.00 | ||||||||||
| AA | 21 | 13.00 | 35 | 27.60 | 0.42 | 0.23 | 0.77 | 0.00 | 0.35 | 0.18 | 0.68 | 0.00 | |||||
| Log-additive | 0, 1, 2 | 130 | 44.40 | 163 | 55.60 | 0.59 | 0.42 | 0.82 | 0.00 | 0.51 | 0.35 | 0.74 | 0.00 | ||||
| rs1561131 | 12q13 | 22068055 | IVS | Recessive | CC/CT | 143 | 82.20 | 89 | 70.10 | 1.00 | 1.00 | ||||||
| G> A | |||||||||||||||||
| TT | 31 | 17.80 | 38 | 29.90 | 0.53 | 0.31 | 0.91 | 0.02 | 0.50 | 0.27 | 0.91 | 0.02 | |||||
| rs1963562 | 12q13 | 806202 | IVS | Dominant | CC | 151 | 85.30 | 109 | 74.20 | 1.00 | 1.00 | ||||||
| C>T | |||||||||||||||||
| CT/TT | 26 | 14.70 | 38 | 25.90 | 0.50 | 0.29 | 0.86 | 0.01 | 0.48 | 0.26 | 0.90 | 0.02 | |||||
| Log-additive | 0, 1, 2 | 152 | 46.10 | 178 | 53.90 | 0.54 | 0.33 | 0.90 | 0.02 | 0.56 | 0.31 | 0.99 | 0.04 | ||||
| rs615382 | 12q13 | 12556217 | RACGAP1 | Recessive | CC/CA | 172 | 92.00126 | 84.00 | 1.00 | 1.00 | |||||||
| C> A | |||||||||||||||||
| AA | 15 | 8.00 | 24 | 16.00 | 0.47 | 0.24 | 0.94 | 0.03 | 0.34 | 0.16 | 0.75 | 0.01 | |||||
| Log-additive | 0, 1, 2 | 155 | 45.20 | 188 | 54.80 | 0.75 | 0.55 | 1.03 | 0.08 | 0.66 | 0.46 | 0.94 | 0.02 | ||||
| rs585224 | 12q13 | IVS | Dominant | AA | 95 | 67.90 | 69 | 53.10 | 1.00 | 1.00 | |||||||
| AG/GG | 45 | 32.10 | 61 | 46.90 | 0.55 | 0.34 | 0.90 | 0.02 | 0.50 | 0.28 | 0.87 | 0.01 | |||||
| Log-additive | 0, 1, 2 | 0.68 | 0.46 | 1.01 | 0.05 | 0.61 | 0.39 | 0.95 | 0.03 | ||||||||
Abbreviations: EPHB6, Ephrin type-B receptor 6; IVS, intervening sequence; OR, odds ratio; PCa, prostate cancer; RACGAP1, Rac GTPase-activating protein 1; SNP, single-nucleotide polymorphism.
Region 12p12–q14. In the 12p12–q14 region surrounding the VDR gene, there were no AIMs in linkage disequilibrium. However, five SNPs in this region were found to be associated with PCa risk (Table 2). One of the four SNPs (rs615382) was found in a gene, Rac GTPase-activating protein 1 (RacGAP1). Age-adjusted ORs for rs615382 using the recessive and log-additive models were 0.34 (0.16–0.75; P = 0.0062) and 0.66 (0.46–0.94; P = 0.021), respectively.
SNPs rs1993973 and rs1561131 are both found in the intergenic region of 12p11. The unadjusted, adjusted ORs and P-values for rs1993973 and rs1561131 can also be found in Table 2. Specifically, SNP rs1993973 had the strongest overall association with PCa. After adjusting for age, each genetic model applied was significant, with the most significant model being the log-additive model (OR = 0.51, 95% CI: 0.35–0.74; P = 0.0003), followed by the recessive (OR = 0.51, 95% CI: 0.35–0.74; P = 0.0016) and dominant (OR = 0.46, 95% CI: 0.26–0.81; P = 0.01) models. In silico analysis revealed that rs1993973 variant G (risk) allele may result in the emergence of binding sites for transcription factors such as specific protein 1 and the disappearance of RNA polymerase II-associated protein and NFkappaB (Table 3). SNP rs1561131 (recessive OR = 0.50, 95% CI: 0.27–0.91; P = 0.02) remained statistically significant after adjusting for age.
Table 3.
SNPs found to be associated with PCa risk and variant implication
| SNP ID | Chromosomal region | Ancestral allele | Allele most prevalent in AAs | Risk allele | Transcription factor binding |
|---|---|---|---|---|---|
| rs911964 | 1q25 | T | T | C | –HNF-1C |
| rs8177113 | 7q21 | C | C | G | +SP1, +RARalpha, –CRE-BP1 |
| rs1993973 | 12p12 | A | G | G | -RAP, –NFkappaB, +SP1 |
| rs1963562 | 12q13 | C | C | C | +SP1, +USF, +AP1, –C/EBPalpha |
| rs585224 | 12q13 | G | A | A | +USF |
Abbreviations: AA, African-American; AP1, activator protein 1; HNF-1C, hepatocyte nuclear factor-1C; PCa, prostate cancer; RAP, RNA polymerase II-associated protein; SNP, single-nucleotide polymorphism; SP1, specific protein 1; USF, upstream stimulatory factor.
SNPs rs1963562 and rs585224 can be found in the 12q13 region. SNPs rs1963562 (dominant OR = 0.48, 95% CI: 0.26–0.90; P = 0.01 and log-additive OR = 0.56, 95% CI: 0.31–0.99; P = 0.04) and rs585224 (dominant OR = 0.50, 95% CI: 0.2–0.87; P = 0.01 and log-additive OR = 0.61, 95% CI: 0.39–0.95; P = 0.03) also remained statistically significant after adjusting for age. In silico analysis also revealed that rs1963562 ancestral C (risk) allele may result in the emergence of an specific protein 1, upstream stimulatory factor and activator protein-1α and disruption CCAAT-enhancer binding protein of transcription factor-binding sites (Table 3). For rs585224, In silico analysis using AliBaba2.1, revealed that the variant A (risk) allele resulted in the appearance of a upstream stimulatory factor transcription factor-binding site.
After multiple comparisons testing using the Bonferroni and FDR tests, only rs1993973 remained associated with PCa risk (P = 0.0004 and FDR P = 0.03). Additionally, after testing the independent effect of each significantly associated SNP using the backward selection technique, SNPs rs1561131 (P = 0.04) and rs1963562 (P = 0.04) were the only SNPs interrogated that remained independently significantly associated with PCa.
Haplotype analysis
Haplotype analysis was undertaken for significantly associated AIMs in the 12p12–q14 region. All haplotypes were compared with the most frequently found haplotype in AAs. Specifically, SNPs rs1561131, rs1963562 and rs615382 were examined together because for these SNPs the most frequently found alleles in this population were the ancestral alleles. Notably, these alleles were also found to be the risk alleles in this population (C, C and C, respectively) (Table 4). The haplotype encompassing the combination of protective alleles was one of the least frequently found haplotype in this population and was significantly associated with a decreased risk of PCa (OR = 0.31 95% CI: 0.11–0.90; P = 0.032). For AIMs rs1993973 and rs585224, also found in the aforementioned region, the ancestral alleles (A and G, respectively) were one of the least frequently found in this population. When compared with the most frequently found haplotype, the AG haplotype was also significantly associated with decreased risk of PCa (OR = 0.40 95% CI: 0.23–0.71; P = 0.0017) (Table 4).
Table 4.
Haplotypes associated with PCa risk adjusted for age
| Haplotype | rs1561131 | rs1963562 | rs615382 | Frequency | Odds ratio (95% CI) | P-value |
|---|---|---|---|---|---|---|
| 1 (Ancestral) | C | C | C | 0.37 | 1.00 | |
| 2 | T | C | C | 0.29 | 1.00 (0.58–1.73) | 1.00 |
| 3 | T | C | A | 0.13 | 0.67 (0.35–1.28) | 0.23 |
| 4 | C | C | A | 0.11 | 0.84 (0.38–1.86) | 0.67 |
| 5 | T | T | A | 0.05 | 0.31 (0.11–0.90) | 0.03 |
| 6 | T | T | C | 0.03 | 1.67 (0.36–7.72) | 0.51 |
| 7 | C | T | A | 0.02 | 0.67 (0.12–3.70) | 0.65 |
| 8 | C | T | C | 0.01 | 0.03 (0–820–31) | 0.50 |
| Global haplotype association P-value = 0.14 | ||||||
| rs1993973 | rs585224 | |||||
| 1 | G | A | 0.49 | 1.00 | ||
| 2 | A | A | 0.28 | 0.53 (0.32–0.90) | 0.02 | |
| 3 (Ancestral) | A | G | 0.15 | 0.40 (0.23–0.71) | 0.00 | |
| 4 | G | G | 0.08 | 0.70 (0.28–1.78) | 0.46 | |
| Global haplotype association P-value = 0.0016 | ||||||
| rs911964 | rs219821 | rs8177113 | ||||
| 1 (Ancestral) | T | C | C | 0.38 | 1.00 | |
| 2 | T | T | C | 0.24 | 1.83 (1.03–3.24) | 0.04 |
| 3 | T | C | G | 0.22 | 1.91 (1.07–3.39) | 0.03 |
| 4 | T | T | G | 0.08 | 1.55 (0.70–3.45) | 0.28 |
| 5 | C | T | C | 0.04 | 1.69 (0.58–4.99) | 0.34 |
| 6 | C | C | C | 0.03 | 3.87 (1.00–14.94) | 0.05 |
| 7 | C | T | G | 0.01 | 18 × 106 | < 0.0001 |
| Global haplotype association P-value = 0.014 | ||||||
Abbreviations: CI, confidence interval; PCa, prostate cancer.
To test the combinatorial effect of the SNPs on PCa, the other three SNPs on regions 1q and 7q were examined jointly. For AIMs rs911964 (region 1q23–32), rs219821 (region 7q21–34) and rs8177113 (region 7q21–34), the ancestral alleles (T, C and C, respectively) were also the most frequently found allele in this population. However, the variant alleles were found to be the risk alleles in our SNP analysis and remarkably the least frequently found in this population. In addition, analysis of these AIMs revealed that the most frequently found SNP combination comprised the most frequently found alleles (TCC frequency = 0.38) while the least frequently found SNP combination comprised the variant alleles (CTG frequency = 0.0103). This SNP combination conferred the highest risk (Table 4). SNP combinations TTC and TCG were also significantly associated with risk in our population (ORs = 1.83 (1.03–3.24) and 1.91 (1.07–3.39), respectively). The SNP combination association with PCa is 0.014.
DISCUSSION
In this study, we identified one SNP, rs1993973, associated with PCa risk after multiple comparisons testing. However, when not adjusting for multiple comparisons testing, SNPs rs1993973 (log-additive genetic model P = 0.002), rs1561131 (genotypic, P = 0.007), rs1963562 (dominant, P = 0.01) and rs615382 (recessive, P = 0.009) were significantly associated with PCa risk after adjusting for only age and ancestry. Additionally, after testing the independent effect of each significantly associated SNP, rs1561131 (P = 0.04) and rs1963562 (P = 0.04) remained significantly associated with PCa. Additionally, haplotype analysis revealed that in region 12p12–q14, the least frequently found alleles were associated with decreased risk of PCa.
AIMs are genetic markers with significant allele frequency differences between African and Caucasian populations. Herein, AIMs were used to explore the role of African ancestry in the etiology of PCa in AAs and to identify genetic variants associated with PCa in AAs. Of the SNPs evaluated, eight were found to be significantly associated with risk. Six of the SNPs were found in intergenic or non-genic regions of the chromosomes and the other two were found in specific genes. Although tests for multiple comparisons testing, Bonferroni and FDR diminished the statistical significance for most SNPs, for exploratory reasons, we are still reporting the clinical implications of such genetic associations, which warrant additional investigations.
SNPs in RacGAP1 and EPHB6 may be likely candidates for predisposition to PCa given their function and association with other cancers.27–34 RacGAP1 is part of a family of Rho GTPase proteins that have a role in management of actin and microtubule dynamics, myosin activity and cell adhesion. Notably, RacGAP1 has been shown to regulate cell migration and motility in cells by promoting lamellipodial protrusions in migrating cells.35 One could speculate that increased expression of this gene in cancer may lead to increased cell migration and ultimately malignancy and metastases. Remarkably, in hepatocellular cancer, high expression of RacGAP1 was found and silencing was associated with inhibited cell migration and invasion.30 Hu et al.27 also demonstrated that expression of RacGAP1 was associated with neuroendocrine development. It is noteworthy that increased neuroendocrine cell differentiation is associated with aggressive, androgen-independent PCa.36 The increased frequency of the rs615382 variant, located in the 5′UTR of RacGAP1, in the AA population (0.67 vs 0.06, AA vs Utah residents with northern and western European acestry (CEU), respectively) may potentially explain in part why AAs may be susceptible to a more aggressive type of PCa at a younger age compared with Caucasians. However, further functional studies of the consequences of this variant are warranted to test those hypotheses.
For EPHB6, the biology of their association may not be that clear. The Eph family of receptors is involved in a variety of functions, including mediating numerous developmental processes, particularly in the nervous system and alterations in the expression of EPH receptors have been observed in several cancers.31–34 Two other SNPs in the intergenic region, rs1993973 and rs1963562, could potentially be associated with the biology of PCa given their effects transcription-binding sites in putative promoters of downstream genes. However, the effects of these SNPs on actual transcription factor binding will also require functional studies.
Regarding haplotypes, the haplotypes investigated herein have never been explored elsewhere. Our studies showed that the most common haplotypes were associated with risk while the least common haplotypes were associated with protection. Conversely, when examining the combinatorial effects of SNPs on different chromosomal loci, the most frequently found alleles were found to be protective. Yet, these results indicate that use of AIMs may help identify potential genetic contributions to the disproportionate morbidity and mortality rates in AAs because of PCa, which could provide clues that may help elucidate the mechanisms of this disease.
In assessing these study results, it is important to consider the limitations and strengths of the study. Studying PCa in the AA male population is a very important subject. However, there were only a limited number of specimens available for research because of the low volume of men who agreed to participate or provide biospecimen in the study herein. Therefore, for some of the AIMs chosen, the population of 161 cases and 197 controls in this study may have limited our capacity to detect differences. Nonetheless, we are devising ways to increase participation of AA men, such as collection of saliva as an alternative to collection of blood, while continuing to recruit for the PCa study. This work will require further investigation of candidate genes and expansion to include post-genome-wide association studies loci, such as 8q24.
Additionally, ascertainment of controls status may have resulted in the misclassification of men according to disease status. Using a PSA <4 ng ml–1 and normal digital rectal exam as the criteria for defining men as controls from a screening population at the HU Cancer Center may have resulted in men with disease being included in the control group, introducing misclassification bias into this case–control study. However, during the recruitment period, 4.0 ng ml–1 was still generally accepted as a general cutoff point for ‘abnormal’ results. Thus, these patients would not have undergone prostate biopsy to rule out cancer. Although Morgan et al.37 and Moul et al.38 suggest that lowering PSA cutoffs to 2.5 may increase sensitivity of detection, they raise concerns about identifying clinically insignificant tumors (that is, over detection). Nevertheless, the authors recognize the limitations of the negative (and positive) predictive value of PSA as a screening tool for PCa. And actually, PSA velocity is probably a better predictor of clinically relevant tumors.39 Despite increasing data to support lower PSA cutoffs, there are no update ‘standards.’ This preliminary work is an important step toward a better understanding of AIMs in conferring PCa risk in AA and CEU populations.
Ultimately, the goal of this study was to address the potential genetic contributions to the disproportionate PCa incidence rates in AAs. We showed that the identification of gene variants disproportionately distributed among populations may be a useful method that could help prioritize the selection of SNPs to be interrogated in studies of health disparities. The latter underscores the possible relevance of population-based differences in SNPs in refining the identification of individuals at risk for disease and potentially, disease diagnosis, treatment and prevention in the emergent era of personalized medicine.
Supplementary Material
ACKNOWLEDGEMENTS
This project was supported in whole or in part with Federal funds from the National Center for Research Resources (NCRR) (UL1RR031975), National Institutes of health (NIH), through the Clinical and Translational Science Awards Program (CTSA), from the RCMI Program at Howard University (G12 RR003048), Division of Research Infrastructure, NCRR, NIH and the Howard University Cancer Center/Johns Hopkins Cancer Center Partnership (U54 CA091431), NCI, NIH.
Footnotes
Author contributions LJR-S, VA, TM and GB participated in the design of the study, performed the statistical analysis and helped draft the manuscript. TM, BW, MA, WH, SH and MD carried out the molecular genetic studies, participated in SNP annotation, and helped draft the manuscript. CA, GD and RK conceived the study, participated in its design and coordination and helped to draft the manuscript.
Supplementary Information accompanies the paper on the Prostate Cancer and Prostatic Diseases website (http://www.nature.com/pcan)
CONFLICT OF INTEREST
The authors declare no conflict of interest.
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