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).
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ABSTRACT |
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INTRODUCTION |
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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 casecontrol study of prostate cancer in Sweden.
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METHODS |
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The study design has been described in detail elsewhere (10). Briefly, Cancer Prostate in Sweden (CAPS) is a large-scale, population-based casecontrol 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 810, 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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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 HardyWeinberg 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 HardyWeinberg 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 HardyWeinberg 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.
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RESULTS |
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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 HardyWeinberg 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 24 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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Next, we genotyped the 17 htSNPs among all 1383 case and 780 control subjects. The SNPs were consistent with HardyWeinberg equilibrium (all P>.05), with the exception of the SNP TLR1 -7202, which statistically significantly deviated from HardyWeinberg 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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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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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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DISCUSSION |
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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 casecontrol 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.
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NOTES |
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REFERENCES |
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![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
(1) Nelson WG, De Marzo AM, Isaacs WB. Prostate cancer. N Engl J Med 2003;349:36681.
(2) Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, et al. Environmental and heritable factors in the causation of canceranalyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med 2000;343:7885.
(3) Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet 2001;357:53945.[CrossRef][ISI][Medline]
(4) Coussens LM, Werb Z. Inflammation and cancer. Nature 2002;420:8607.[CrossRef][ISI][Medline]
(5) Nelson WG, DeWeese TL, DeMarzo AM. The diet, prostate inflammation, and the development of prostate cancer. Cancer Metastasis Rev 2002;21:316.[CrossRef][ISI][Medline]
(6) De Marzo AM, Marchi VL, Epstein JI, Nelson WG. Proliferative inflammatory atrophy of the prostate: implications for prostatic carcinogenesis. Am J Pathol 1999;155:198592.
(7) Shah R, Mucci NR, Amin A, Macoska JA, Rubin MA. Postatrophic hyperplasia of the prostate gland: neoplastic precursor or innocent bystander? Am J Pathol 2001;158:176773.
(8) Carpten J, Nupponen N, Isaacs S, Sood R, Robbins C, Xu J, et al. Germline mutations in the ribonuclease L gene in families showing linkage with HPC1. Nat Genet 2002;30:1814.[CrossRef][ISI][Medline]
(9) Xu J, Zheng SL, Komiya A, Mychaleckyj JC, Isaacs SD, Hu JJ, et al. Germline mutations and sequence variants of the macrophage scavenger receptor 1 gene are associated with prostate cancer risk. Nat Genet 2002;32:3215.[CrossRef][ISI][Medline]
(10) Zheng SL, Augustsson-Balter K, Chang B, Hedelin M, Li L, Adami HO, et al. Sequence variants of toll-like receptor 4 are associated with prostate cancer risk: results from the CAncer Prostate in Sweden Study. Cancer Res 2004;64:291822.
(11) Singh BP, Chauhan RS, Singhal LK. Toll-like receptors and their role in innate immunity. Curr Sci 2003;85:115664.[ISI]
(12) Barton GM, Medzhitov R. Toll-like receptors and their ligands. Curr Top Microbiol Immunol 2002;270:8192.[ISI][Medline]
(13) Chuang T, Ulevitch RJ. Identification of hTLR10: a novel human Toll-like receptor preferentially expressed in immune cells. Biochim Biophys Acta 2001;1518:15761.[ISI][Medline]
(14) Yamamoto M, Takeda K, Akira S. TIR domain-containing adaptors define the specificity of TLR signaling. Mol Immunol 2004;40:8618.[CrossRef][ISI][Medline]
(15) Takeuchi O, Kawai T, Muhlradt PF, Morr M, Radolf JD, Zychlinsky A, et al. Discrimination of bacterial lipoproteins by Toll-like receptor 6. Int Immunol 2001;13:93340.
(16) Takeuchi O, Sato S, Horiuchi T, Hoshino K, Takeda K, Dong Z, et al. Cutting edge: role of Toll-like receptor 1 in mediating immune response to microbial lipoproteins. J Immunol 2002;169:104.
(17) Ozinsky A, Underhill DM, Fontenot JD, Hajjar AM, Smith KD, Wilson CB, et al. The repertoire for pattern recognition of pathogens by the innate immune system is defined by cooperation between toll-like receptors. Proc Natl Acad Sci U S A 2000;97:1376671.
(18) Hajjar AM, O'Mahony DS, Ozinsky A, Underhill DM, Aderem A, Klebanoff SJ, et al. Cutting edge: functional interactions between toll-like receptor (TLR) 2 and TLR1 or TLR6 in response to phenol-soluble modulin. J Immunol 2001;166:159.
(19) Janssens S, Beyaert R. Role of Toll-like receptors in pathogen recognition. Clin Microbiol Rev 2003;16:63746.
(20) Spitzer JH, Visintin A, Mazzoni A, Kennedy MN, Segal DM. Toll-like receptor 1 inhibits Toll-like receptor 4 signaling in endothelial cells. Eur J Immunol 2002;32:11827.[CrossRef][ISI][Medline]
(21) Schroder FH, Hermanek P, Denis L, Fair WR, Gospodarowicz MK, Pavone-Macalus M. The TNM classification of prostate cancer. Prostate Suppl 1992;4:12938.[Medline]
(22) Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 2001;68:97889.[CrossRef][ISI][Medline]
(23) Kumar S, Tamura K, Nei M. MEGA: molecular evolutionary genetics analysis software for microcomputers. Bioinformatics 1994;10:18991.[Abstract]
(24) Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 1987;4:40625.[Abstract]
(25) Rzhetsky A, Nei M. Theoretical foundation of the minimum-evolution method of phylogenetics inference. Mol Biol Evol 1993;10:107395.[Abstract]
(26) Nei M, Kumar S. Molecular evolution and phylogenetics. New York (NY): Oxford University Press; 2000.
(27) Falsenstein J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 1985;39:78391.[ISI]
(28) Fitch WM. Towards defining the course of evolution: minimum change for a specific tree topology. Syst Zool 1971;20:40616.[ISI]
(29) Weir BS. Genetic analysis II: methods for discrete population genetic data. Sunderland (MA): Sinauer; 1996.
(30) Lilienfeld AM, Lilienfeld DE. Foundations of epidemiology. 2nd ed. New York (NY): Oxford University Press; 1980.
(31) Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet 2002;70:42534.[CrossRef][ISI][Medline]
(32) Platz EA, De Marzo AM. Epidemiology of inflammation and prostate cancer. J Urol 2004;171:S36S40.[CrossRef][Medline]
(33) Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 2004;96:43442.
(34) Pahl HL, Scheibe RJ, Zhang DE, Chen HM, Galson DL, Maki RA, et al. The proto-oncogene PU.1 regulates expression of the myeloid-specific CD11b promoter. J Biol Chem 1993;268:501420.
(35) Klemsz MJ, McKercher SR, Celada A, Van Beveren C, Maki RA. The macrophage and B cell-specific transcription factor PU.1 is related to the ets oncogene. Cell 1990;61:11324.[CrossRef][ISI][Medline]
(36) Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:38124.
(37) Sunyaev S, Ramensky V, Koch I, Lathe W, III, Kondrashov AS, Bork P. Prediction of deleterious human alleles. Hum Mol Genet 2001;10:5917.
Manuscript received September 3, 2004; revised January 6, 2005; accepted February 14, 2005.
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