ARTICLE

Sequence Variants in Toll-Like Receptor Gene Cluster (TLR6-TLR1-TLR10) and Prostate Cancer Risk

Jielin Sun, Fredrik Wiklund, S. Lilly Zheng, Baoli Chang, Katarina Bälter, Liwu Li, Jan-Erik Johansson, Ge Li, Hans-Olov Adami, Wennuan Liu, Amy Tolin, Aubrey R. Turner, Deborah A. Meyers, William B. Isaacs, Jianfeng Xu, Henrik Grönberg

Affiliations of authors: Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC (JS, SLZ, BC, LL, GL, WL, AT, ART, DAM, JX); Department of Radiation Sciences and Oncology, University of Umeå, Umeå, Sweden (FW, HG); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (KB, H-OA); Department of Urology and Clinical Medicine, Örebro University Hospital, Örebro, Sweden, and Regional Oncological Center, University Hospital, Uppsala, Sweden (J-EJ); Department of Urology, Johns Hopkins School of Medicine, Baltimore, MD (WBI)

Correspondence to: Jianfeng Xu, Dr PH, MD, Center for Human Genomics, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157 (e-mail: jxu{at}wfubmc.edu).


    ABSTRACT
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Background: Chronic inflammation plays an important role in several human cancers and may be involved in the etiology of prostate cancer. Toll-like receptors (TLRs) are important in the innate immune response to pathogens and in cross-talk between innate immunity and adaptive immunity. Our previous finding of an association of TLR4 gene sequence variants and prostate cancer risk provides evidence for a role of TLRs in prostate cancer. In this study, we investigated whether sequence variants in the TLR6-TLR1-TLR10 gene cluster, residing within a 54-kb region on 4p14, were associated with prostate cancer risk. Methods: We selected 32 single-nucleotide polymorphisms (SNPs) covering these three genes and genotyped these SNPs in 96 control subjects from the Cancer Prostate in Sweden (CAPS) population-based prostate cancer case–control study. Five distinct haplotype blocks were inferred at this region, and we identified 17 haplotype-tagging SNPs (htSNPs) that could uniquely describe >95% of the haplotypes. These 17 htSNPs were then genotyped in the entire CAPS study population (1383 case subjects and 780 control subjects). Odds ratios of prostate cancer for the carriers of a variant allele versus those with the wild-type allele were estimated using unconditional logistic regression. Results: The allele frequencies of 11 of the 17 SNPs were statistically significantly different between case and control subjects (P = .04–.001), with odds ratios for variant allele carriers (homozygous or heterozygous) compared with wild-type allele carriers ranging from 1.20 (95% confidence interval [CI] = 1.00 to 1.43) to 1.38 (95% CI = 1.12 to 1.70). Phylogenetic tree analyses of common haplotypes identified a clade of two evolutionarily related haplotypes that are statistically significantly associated with prostate cancer risk. These two haplotypes contain all the risk alleles of these 11 associated SNPs. Conclusion: The observed multiple associated SNPs at the TLR6-TLR1-TLR10 gene cluster were dependent and suggest the presence of a founder prostate cancer risk variant on this haplotype background. The TLR6-TLR1-TLR10 gene cluster may play a role in prostate cancer risk, although further functional studies are needed to pinpoint the disease-associated variants in this gene cluster.



    INTRODUCTION
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Prostate cancer is one of the most common cancers and a major public health problem in many industrialized countries (1). Multiple pieces of evidence from family, twin, and segregation studies consistently suggest that genetic susceptibility is a major factor in determining the individual risk of prostate cancer (2).

Chronic or recurrent inflammation has been implicated in the initiation and development of several human cancers, including those of the stomach, liver, colon, and urinary bladder (1,35), and a role for chronic inflammation in the etiology of prostate cancer has been proposed (6,7). Two of three candidate prostate cancer susceptibility genes (MSR1 and RNASEL) are involved in innate immunity and inflammation, further suggesting a link between inflammation and prostate cancer (8,9). In addition, our recent finding of an association between Toll-like receptor 4 (TLR4) sequence variants and prostate cancer risk provides a specific link between TLRs and their fundamental role in innate immunity and prostate cancer risk (10).

TLRs recognize ligands such as conserved pathogen-associated molecular patterns, which are found in pathogens but not in hosts (11). Binding of ligands to TLRs results in the production of various proinflammatory cytokines and chemokines. Malfunction or improper regulation of TLRs may lead to an unbalanced ratio of pro- to-anti-inflammatory cytokines and chemokines in the host, contributing to the onset of a number of inflammatory diseases and of cancer. At present, 10 members of the human TLR family have been identified (12). TLR6, TLR1, and TLR10 are located within a 54-kb region on chromosome 4p14 and encode proteins that share a high degree of homology in their overall amino acid sequences (13). The ligands recognized by TLR6 and TLR1 are diacylated lipoprotein and triacylated lipoprotein, respectively (14). No specific ligand for TLR10 has been identified. The TLR1 and TLR6 proteins each form heterodimers with TLR2 to establish a combinational repertoire that distinguishes among the large number of pathogen-associated molecular patterns existing in nature (1518). Heterodimer formation, particularly with TLR1, may be important in regulating other TLR responses (19). For example, TLR1 can associate with TLR4 and inhibit its signaling in endothelial cells (20), and TLR1 can associate with TLR2 to inhibit the TLR2-mediated response to phenol-soluble modulin (18). Sequence variants in TLR genes may disrupt the inflammatory response, thereby contributing to the onset of cancer.

In light of the potential importance of inflammation and inflammatory genes, especially the TLR genes, in the development of prostate cancer, we hypothesized that sequence variants in TLR6, TLR1, and TLR10 may be associated with prostate cancer susceptibility. To test this hypothesis, we systematically evaluated the association of sequence variants in the TLR6-TLR1-TLR10 gene cluster with prostate cancer in a large population-based case–control study of prostate cancer in Sweden.


    METHODS
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Study Population

The study design has been described in detail elsewhere (10). Briefly, Cancer Prostate in Sweden (CAPS) is a large-scale, population-based case–control study. Prostate cancer patients were identified and recruited from four of the six regional cancer registries in Sweden. The inclusion criteria for case subjects were pathologically or cytologically verified adenocarcinoma of the prostate and diagnosis between July 1, 2001, and September 30, 2002. Control subjects were randomly selected from the continuously updated Swedish Population Register and were frequency matched according to age (within 5 years) and geographic origin of the case subjects. In total, 1444 case and 866 control subjects were recruited. Among all participants, DNA samples and questionnaires were available for 1383 case and 780 control subjects, representing an 83% and 52% participation rate among all eligible case and control subjects, respectively. Clinical information such as Tumor-Node-Metastasis stage (21), Gleason grade, and prostate-specific antigen (PSA) levels at diagnosis were available from either cancer registries or the National Prostate Cancer Registry for 94% of the case subjects. The clinical characteristics of the study subjects are presented in Table 1. The case subjects were classified as having advanced disease (i.e., prone to progressive disease) if they met any of the following criteria: T3/4, N+, M+, Grade III, Gleason score sum of 8–10, or a PSA level of >50 ng/mL. All other case subjects were classified as having localized disease. The study received institutional approval, and the participants provided written informed consent.


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Table 1.  Characteristics of CAPS subjects

 
Genotyping Methods

DNA was extracted from leukocytes by using a Puregene Kit (Gentra Systems, Minneapolis, MN). Genotyping was performed using the MassARRAY system (SEQUENOM, Valencia, CA). For the MassARRAY assay, polymerase chain reaction (PCR) and extension primers for the sequence variants were designed using SpectroDesigner software (SEQUENOM). The primer information is available at http://www.wfubmc.edu/genomics (last accession February 25, 2005). PCR was performed according to the manufacturer's instructions, and extension product sizes were determined by mass spectrometry. Each sample, blinded to the technician, was run in duplicate, and two water samples (PCR negative controls) were included in each 96-well plate. The average concordance rate between samples was >99%. We also tested each single-nucleotide polymorphism (SNP) for agreement with Hardy–Weinberg equilibrium as another quality control check.

Selection of SNPs

The TLR6, TLR1, and TLR10 gene cluster is located on chromosome region 4p14. The full-length transcripts for TLR6, TLR1, and TLR10 are 2753 bases, 8518 bases, and 3270 bases, respectively. The TLR6 gene contains one exon, the TLR1 gene contains four exons, and the TLR10 gene contains three exons. The SNP information for these genes was obtained from the database of the Innate Immunity PGA website (http://innateimmunity.net; last accessed April 17, 2003). Two criteria were used to select SNPs: 1) a minor allele frequency of at least 5%, at a resolution of 1 SNP per kb of DNA across the genomic region of each gene, including 2.5 kb of the upstream promoter sequence; and 2) all SNPs that lead to an amino acid substitution.

Haplotype Block Construction and Haplotype-Tagging SNP (htSNP) Identification

DNA from 96 control subjects selected at random from CAPS was genotyped for the SNPs chosen on the basis of the two criteria given above. The haplotypes of these SNPs were then estimated using a computer program, PHASE 2.0 (http://www.stats.ox.ac.uk/mathgen/software.html; last accessed September 7, 2003) (22). Haplotype blocks, in which SNPs are inherited in blocks, at this gene cluster were constructed using the Web-based program Haploblockfinder (http://cgi.uc.edu/cgi-bin/kzhang/haploBlockFinder.cgi/; last accessed April 22, 2004). We used the default threshold of minimal pairwise D' ≥ .8 to define a haplotype block. In addition, we used htSNP2 computer software (http://www.gene.cimr.cam.ac.uk/clayton/software/stata; last accessed February 24, 2005) to identify htSNPs that could uniquely represent 95% of the haplotypes among these 96 subjects. All of the htSNPs were then genotyped in all 1383 case subjects and 780 control subjects.

Phylogenetic Tree Generation

The phylogenetic tree of major haplotypes (frequency cutoff: .01) was constructed using MEGA version 2.1 (23). The neighbor-joining method (24), a simplified version of the minimum evolution method (25), was used to investigate the relationships among the major haplotypes. In the minimum evolution method, evolutionary distance measures that correct for multiple mutational events at the same nucleotides are used, and a topology showing the smallest value of the sum of all branches (S) is chosen as an estimate of the correct tree. In the neighbor-joining method, the S value is not computed for all or many topologies, but the examination of different topologies is embedded in the algorithm, so that only one final tree is produced (26). The neighbor-joining method produces an unrooted tree because it does not require the assumption of a constant rate of evolution. However, MEGA displays neighbor-joining trees in a manner similar to that for rooted trees for the ease of inspection (http://www.megasoftware.net; last accession July 23, 2001). We performed a bootstrap test (10 000 replicates) to assess the reliability of an inferred tree (27). In addition to the neighbor-joining and minimum evolution methods, maximum parsimony (28) and UPGMA (26) were used in MEGA to construct phylogenetic trees.

Statistical Analysis

A Hardy–Weinberg equilibrium test was performed using the Fisher probability test statistic (29), as implemented in software package Genetic Data Analysis (http://statgen.ncsu.edu/brcwebsite/software_BRC.php#GDA). Empirical P values for the Hardy–Weinberg equilibrium test were based on 10 000 permutations.

Allele frequency differences between two groups were tested for each SNP using the chi-square test with 1 degree of freedom. Genotype frequency differences were tested using the chi-square test with 2 degrees of freedom. Both tests were performed using the SAS/Genetics computer program (SAS Institute Inc, Cary, NC). Odds ratios (ORs) of prostate cancer for the carriers of the risk allele (homozygous and heterozygous) versus noncarriers (i.e., carriers of the homozygous wild-type non-risk allele) were estimated using unconditional logistic regression, adjusted for age and geographic region. Attributable risk was estimated using the formula 100% x p(OR – 1)/[p(OR – 1) + 1], in which p is the prevalence of risk genotypes in a population (30). Because CAPS is a population-based study, the genotype frequencies in this study population approximate that of the prevalence in Sweden.

Associations between haplotypes and prostate cancer risk were calculated using a likelihood ratio test implemented in SAS/Genetics (SAS Institute Inc.) and a score test developed by Schaid et al. (31), as implemented in the computer program HAPLO.SCORE (http://www.mayo.edu/statgen; last accessed April 5, 2002). All statistical tests were two-sided.


    RESULTS
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Selection of SNPs, Haplotype Block Construction, and htSNP Identification

On the basis of the two a priori criteria, we selected 32 SNPs in the three TLR genes, including nine SNPs in TLR6, 11 SNPs in TLR1, and 12 SNPs in TLR10 (Fig. 1, A). Nine of these SNPs are nonsynonymous changes; i.e., they cause amino acid changes (P249 and V427A in TLR6; R80T and N248S in TLR1; and N241H, I369L, I473T, R525T, and I775V in TLR10). All 32 SNPs were first genotyped in DNA from 96 control subjects. All of the SNPs were found to be in Hardy–Weinberg equilibrium (all P>.05). Five haplotype blocks in the ~54-kb region of chromosome 4p14 were inferred, in which the D' was ≥.8 for all pairs of SNPs within a block (Fig. 1, B). All nine SNPs in the TLR6 gene reside in block 1, the 11 SNPs in the TLR 1 gene reside in blocks 2–4 and part of block 5, and all 12 SNPs in the TLR10 gene reside in block 5. Seventeen htSNPs that uniquely represented ≥95% of the haplotypes in this chromosomal region were identified (Fig. 1, C), including seven nonsynonymous changes (P249S and V247A of TLR6; A80T of TLR1; and N241H, I369L, I473T, and I775V of TLR10).



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Fig. 1. Single nucleotide polymorphisms (SNPs) of the Toll-like receptor (TLR) genes TLR6, TLR1, and TLR10. A) TLR6-TLR1-TLR10 gene structure in which shaded bars represent exons and the locations of the 32 selected SNPs (relative to the transcriptional start sites) are shown. SNPs that result in an amino acid substitution are shown. Vertical arrows represent transcription start sites. B) Haplotype blocks inferred from the 32 SNPs identified from 96 control subjects. Vertical lines identify the location of the SNPs. C) Locations of the 17 haplotype-tagging SNPs that were selected to represent at least 95% of the haplotype information.

 
Association Tests of htSNPs With Prostate Cancer Risk

Next, we genotyped the 17 htSNPs among all 1383 case and 780 control subjects. The SNPs were consistent with Hardy–Weinberg equilibrium (all P>.05), with the exception of the SNP TLR1 -7202, which statistically significantly deviated from Hardy–Weinberg equilibrium in case subjects (P = .02) but not in control subjects (P = .39). Allele frequencies in 11 of 17 SNPs were statistically significantly different between case and control subjects, with P values ranging from .04 to .001 (Table 2). These 11 SNPs included two of five SNPs in the TLR6 gene, three of five SNPs in the TLR1 gene, and six of seven SNPs in the TLR10 gene. The SNP with the strongest evidence for an association with prostate cancer was a promoter variant of the TLR6 gene (–1401), with observed frequencies for allele ‘A’ of .19 in case subjects and .14 in control subjects (P = .001). Strong evidence for an association with prostate cancer was also observed for three SNPs in the TLR1 gene (–7202, –6399, and –833), with P values ranging from .002 to .007. The allele frequencies of two nonsynonymous changes in the TLR6 gene (P249S and V427A) and one nonsynonymous change in the TLR1 gene (A80T) were not statistically significantly different between case and control subjects. Three of four nonsynonymous changes (N241H, I369L, and I775V) in the TLR10 gene were, however, weakly associated with prostate cancer risk, with P values of .04, .01, and .01, respectively.


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Table 2.  Allele frequencies of 17 htSNPs in prostate cancer case and control subjects*

 
We estimated prostate cancer risk associated with the genotypes containing risk alleles (in heterozygous or homozygous form) for each of these SNPs, adjusting for age (Table 3). Statistically significantly increased risks were observed for nine of 17 SNPs, including two in the TLR6 gene, three in the TLR1 gene, and four in the TLR10 gene. The odds ratios for prostate cancer in homozygous and heterozygous carriers if the variant allele was compared with wild-type allele carriers ranged from 1.20 (95% confidence interval [CI] = 1.00 to 1.43) to 1.38 (95% CI = 1.12 to 1.70). Similar to the results of the allele frequency test, a stronger risk was observed for the SNPs in the TLR6 and TLR1 genes than for the SNPs in the TLR10 gene.


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Table 3.  Odds rations for prostate cancer associated with single-nucleotide polymorphisms within the Tool-like receptor gene cluster (TLR6-TLR1-TLR10) among case subjects and control subjects from the Cancer Prostate in Sweden study*

 
Association Tests of Haplotypes With ProstateCancer Risk

Because of the strong linkage disequilibrium among these SNPs, especially for the SNPs within a haplotype block, haplotype analyses were performed to investigate the phase of the risk alleles in these SNPs and to test for a founder effect. Within each haplotype block in which there were multiple SNPs (blocks 1, 4, and 5), haplotypes were estimated and their associations with prostate cancer were tested (Table 4). The distribution of within-block haplotypes was statistically significantly different between case subjects and control subjects for block 1 (P = .01) and block 4 (P<.001) but not for block 5 (P = .13). In block 1, the frequency of the haplotype ‘GATCT’ was statistically significantly higher in case subjects than in control subjects (.18 versus .15, respectively; haplotype-specific P = .01). Similarly, in block 4, frequency of the ‘CG’ haplotype was statistically significantly higher in case subjects than in control subjects (.24 versus .20, respectively; haplotype-specific P = .002). Extending the haplotype analysis to the 17 SNPs in the entire TLR6-TLR1-TLR10 gene cluster, we found that one haplotype (H10: ‘GATCT G C CG ACTACCTG’) had a statistically significantly higher frequency in case subjects than in control subjects (.13 versus .11, respectively; haplotype-specific P = .03). This haplotype consists of the risk haplotype identified in blocks 1 and 4 and, in fact, contains all of the risk alleles of the 11 SNPs that were associated with prostate cancer risk in the allele test (Table 2). Although the results of these analyses provided further evidence that the TLR6-TLR1-TLR10 gene cluster was associated with prostate cancer risk, they suggest that the evidence for an association between prostate cancer and multiple SNPs is not independent and is likely the result of a single founder haplotype that is associated with a disease-related variant.


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Table 4.  Haplotype frequencies with the Toll-like receptor (TLR) gene cluster among prostate cancer case subjects and control subjects from the Cancer Prostate in Sweden study

 
Phylogenetic Analysis of Haplotypes

To explore this inferred founder haplotype among the population, we performed a phylogenetic tree analysis of all haplotypes that occurred with a frequency of at least .01 using multiple methods of analysis, including neighbor-joining, minimum evolution, maximum parsimony, and UPGMA. Although only the results from the neighbor-joining method are presented (Fig. 2), all methods consistently identified one clade (branch) that contains the prostate cancer risk haplotype (H10) and another haplotype (H11) that differs from H10 only at one SNP. On the basis of the frequency of these two haplotypes, one likely scenario is that the rarer haplotype H11 (2.06% in case subjects and 1.80% in control subjects) is an outcome of a recent mutational event at SNP TLR10 1417 on the H10 background. The observation that both haplotypes were more frequent in case subjects (13.32% of H10 and 2.06% of H11) than in control subjects (10.91% of H10 and 1.80% of H11) provides further evidence for a founder prostate cancer risk allele on this haplotype background. However, the high similarity between the two haplotypes provided little information regarding the exact location of this potential risk allele. The frequency of these two haplotypes was statistically significantly higher in case subjects (15.29%) than in control subjects (12.77%) (P = .02). Men who were heterozygous or homozygous for both haplotypes had a 1.24-fold (95% CI = 1.01 to 1.51) increased risk of prostate cancer compared with men who did not carry either of these two haplotypes. The attributable risk associated with these two haplotypes was estimated to be 5.4% in this population, assuming that the frequency of these two haplotypes in the general Swedish population was 12.8%.



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Fig. 2. Neighbor-joining trees representing the haplotype relationships in the Toll-like receptor (TLR6-TLR1-TLR10) gene cluster. Bootstrap values, if higher than 50%, are indicated at nodes (numeric number with box). Branch length is expressed as a p-distance of sequences. The length of lines is proportional to the p-distance, which was obtained by dividing the number of nucleotide differences by the total number of nucleotides compared. The hatched-lined box indicates the clade of two haplotypes that may contain the prostatecancer risk–associated alleles. The percentages of case and control subjects expressing each of the haplotypes are shown. P values were derived by chi-square tests.

 
We also performed a case-only study to assess associations between sequence variants of the 17 htSNPs and risk for advanced prostate cancer. The frequencies of the risk alleles in 11 SNPs that are associated with prostate cancer risk were all higher in 591 patients with advanced disease than in 792 patients with localized disease, although the differences were not statistically significant. When the haplotypes of 17 SNPs were examined, the frequency of the two risk haplotypes (H10 and H11) was 16.0% in patients with the advanced disease and 14.8% in patients with localized disease.


    DISCUSSION
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Recent findings from epidemiologic and genetic association studies suggest that chronic inflammation may play an important role in the development of prostate cancer development (32). Because TLRs regulate pathogen-mediated innate immunity and chronic inflammation, and because we previously found an association between sequence variants in the TLR4 gene and prostate cancer risk in a large prostate cancer case–control study in Sweden (10), we initiated a systematic test for an association between sequence variants in the other TLR genes and prostate cancer risk. In this article, we report our analysis of the TLR6-TLR1-TLR10 gene cluster, which spans approximately 54 kb on chromosome 4p14. We used an htSNP approach, in which a subset of SNPs represents the majority of genetic variation in a defined genomic region, to comprehensively assess sequence variants in the gene cluster. Statistically significant differences in allele frequencies between 1383 prostate cancer patients and 780 control subjects were observed for 11 of 17 SNPs evaluated, providing strong evidence for association between the TLR6-TLR1-TLR10 gene cluster and prostate cancer risk. Haplotype analysis identified one risk haplotype that was statistically significantly associated with prostate cancer risk. This risk haplotype contains all the risk alleles of the 11 associated SNPs, suggesting that the observed multiple associated SNPs in this TLR6-TLR1-TLR10 gene cluster were not independent. Phylogenetic tree analysis of common haplotypes identified a clade of two haplotypes—the risk haplotype and a haplotype that differs by one SNP and likely originates from a recent mutational event at one SNP on this risk haplotype. The observation that both haplotypes were more frequent in case subjects than in control subjects provided further evidence for a founder prostate cancer risk variant on this haplotype (H11) background. Although the strong linkage disequilibrium between SNPs in this Swedish population made it possible to identify a genomic segment associated with prostate cancer risk using an htSNP approach, it also makes it difficult to pinpoint the exact location of the underlying risk variant.

Results from this study are subject to type I error and/or errors resulting from population stratification. However, the large sample size and relatively homogeneous study population minimize the impact of these errors on the results. The positive findings are unlikely to be a result of an inflated type I error associated with multiple tests, because 11 of the 17 primary tests (for each of the 17 SNPs) were statistically significant and because these tests were not independent due to the strong linkage disequilibrium among the SNPs. Furthermore, the false-positive-report probability of our finding is likely to be small (33). For example, if we assume a prior probability of .1 or .01, the false-positive-report probability for the SNP TLR1 -1401A/G was .03 or .24, respectively, for the TLR6-TLR1-TLR10 gene cluster mutation to confer an odds ratio of 1.5 for prostate cancer risk. The range of these false-positive-report probability estimates was below the recommended criterion of .5, suggesting that the results are reliable (33). Although any positive genetic association findings from case–control studies could be the result of differences in the genetic background between case and control subjects rather than differences in disease status (i.e., population stratification), such an effect is unlikely in this study because there is no evidence to suggest genetic heterogeneity in the Swedish population. No statistically significant difference in allele frequencies of the 17 SNPs was found among the 1383 cases when we stratified the population according to geographic region (data not shown). In addition, this population-based study was carefully designed, and almost all patients who met the inclusion criteria enrolled in the study. Control subjects were frequency matched to case subjects on the basis of residence area and age. Finally, the large number of study subjects decreases the possibility for statistical fluctuation and increases our confidence in the validity of the results.

This study successfully demonstrated the utility of using an htSNP approach to identify disease-associated variants in a relatively homogeneous population. If a genetic variant increases susceptibility to a disease, then the variant and variants that are in linkage disequilibrium with it would be present more often in patients with the disease than in control subjects. Therefore, by systematically studying a set of htSNPs that themselves may or may not be functionally relevant to a disease but represent sequence variations in the entire gene, it is possible to assess whether sequence variants in a gene are associated with a particular disease risk. Because it is expected that multiple variants that are in linkage disequilibrium with a particular or specific variant of interest would be associated with a disease even if there is only one underlying risk variant, the htSNP approach alone cannot be used to dissect which of the related sequence variants is specifically implicated as the disease-associated allele. Other study designs, such as in vitro and in vivo functional evaluation of sequence variants, are needed to address this question. However, the ability to identify a small segment within a genomic region that is associated with a disease is an important step to understanding the genetic etiology of common diseases that are likely to involve many genes.

Our results suggest that a prostate cancer risk variant may exist somewhere in this gene cluster, most likely within the TLR1 and TLR6 genes based on the haplotype analysis (Table 4). Any of the variants that have higher frequencies in case subjects than in control subjects may potentially be the disease-associated allele. However, sequence variants that were not directly assessed may also be the disease-associated variant. In the TLR6 gene, there are 24 known sequence variants, including four changes that are nonsynonymous and 11 others in the promoter. The nonsynonymous changes are unlikely to be the disease-associated alleles because they were either not associated with prostate cancer (P249S and V427A) or were present at a very low frequency, with a minor allele frequency of <.05. For variants in other genomic regions of the TLR6 gene, bioinformatics analysis indicated that none appeared to affect major transcription binding sites, splicing efficiency, or mRNA stability (data not shown). In the TLR1 gene, there are 36 known sequence variants, including four changes that are nonsynonymous, two changes in the untranslated regions, and four changes in the promoter. The nonsynonymous changes are unlikely to be the disease-associated allele because they were either not associated with prostate cancer (A80T) or were present at a low frequency, with a minor allele frequency of <.05. A SNP in the promoter (-7202A/G), however, is worth noting. The variant allele G, which occurred at a statistically significantly higher frequency in case subjects than in control subjects (.23 versus .19, respectively) (P = .003) alters the putative core binding site of the proto-oncogene PU.1 (AAAAGGAGAAG) (34). PU.1 is expressed primarily in B cells and macrophages (35). There is no apparent change in other sequence variants that may affect major transcription binding sites, splicing efficiency, or mRNA stability (data not shown). In the TLR10 gene, there are 55 known sequence variants, including nine changes that are nonsynonymous, four changes in the untranslated regions, and 28 changes in the promoter. Although four nonsynonymous changes were weakly associated with prostate cancer risk, they are unlikely to be the disease-associated variants because the changes are predicted to have little impact on protein function on the basis of two algorithms [Sorting Intolerant from Tolerant (SIFT) (36) and Polymorphism Phenotyping (PolyPhen) (37)]. There is no apparent change in other known sequence variants that may affect major transcription binding sites, splicing efficiency, or mRNA stability (data not shown).

In summary, this study is, to our knowledge, the first comprehensive genetic study of the TLR6-TLR1-TLR10 gene cluster in prostate cancer and in human diseases. Our findings build upon our previous observation of an association between TLR4 and prostate cancer risk, as well as other lines of evidence, and suggest that innate immunity and chronic inflammation are important in prostate cancer development. Our study provides strong evidence that the TLR6-TLR1-TLR10 gene cluster region harbors a sequence variant that statistically significantly increases prostate cancer risk in the Swedish population. The exact location and the biological function of this sequence variant remain to be identified. The relevance of this genetic association between TLR6-TLR1-TLR10 sequence variants and prostate cancer risk in other study populations is of great interest.


    NOTES
 Top
 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
This study has been supported by grants from the Swedish Cancer Foundation and a Spear grant from the Umeå University Hospital, Umeå, Sweden. This study was also partially funded by an NCI grant (CA 1R01CA105055–01A1 to J. Xu) and by the Center for Human Genomics at Wake Forest University School of Medicine. The authors thank all study participants in the CAPS study. We thank Ulrika Lund for coordinating the study at Karolinska Institute and thank all urologists who recruited their patients for this study and provided clinical data to the national registry of prostate cancer. We also thank Karin Andersson, Susan Okhravi-Lindh, Gabriella Thorén-Berglund, and Margareta Åswärd at the Regional Cancer registries in Umeå, Uppsala, Stockholm-Gotland, and Lindköping. In addition, we thank Sören Holmgren and the personnel at the Medical Biobank in Umeå for skillfully handling the blood samples.


    REFERENCES
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 Notes
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

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Manuscript received September 3, 2004; revised January 6, 2005; accepted February 14, 2005.


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