A Genome-Wide DNA Microsatellite Association Screen to Identify Chromosomal Regions Harboring Candidate Genes in Diabetic Nephropathy

Amy Jayne McKnight,* A. Peter Maxwell,* Stephen Sawcer,† Alastair Compston,† ʈ Efrosini Setakis,‡ Chris C. Patterson,§ Hugh R. Brady, and David A. Savage* *Nephrology Research Group and §Department of Epidemiology & Public Health, Queen’s University Belfast, Belfast, Northern Ireland; †Department of Neurology, University of Cambridge, Cambridge, United Kingdom; ‡Department of ʈ Epidemiology and Public Health, Imperial College, London, United Kingdom; and Conway Institute, University College Dublin, Dublin, Ireland

In an effort to accelerate the identification of susceptibility genes in diabetic nephropathy, the first genome-wide fluorescence- association screen was performed, using pools of genomic DNA derived from Irish (6000 ؍ based DNA microsatellite (n type 1 diabetic nephropathy. Allele image profiles were generated (200 ؍ and without (controls; n (200 ؍ patients with (cases; n for 5353 (89.2%) microsatellite markers for both case and control pools. Allele counts (estimated from allele image profiles) were compared in case versus control groups, and empirical P values were generated. Markers then were ranked on the basis of their empirical P values (lowest to highest). Repeat PCR amplification and electrophoresis of pooled samples were performed systematically on ranked markers until the 50 most associated markers with consistent results were identified. DNA samples that composed the pools then were genotyped individually for these markers. Two markers on chromosome 10, ؍ ؍ D10S558 (Pcorrected 0.005) and D10S1435 (Pcorrected 0.016), revealed statistically significant associations with diabetic nephropathy. An additional four markers (D6S281, D4S2937, D2S291, and D17S515) also are worthy of further investigation. Relevant functional candidate genes have been identified in the vicinity of these markers, demonstrating the feasibility of low-resolution genome-wide microsatellite association screening to identify possible candidate genes for diabetic nephropathy. J Am Soc Nephrol 17: 831–836, 2006. doi: 10.1681/ASN.2005050493

iabetic nephropathy (DN) is an extremely serious clinical occurred by the time microalbuminuria is identified. Disease complication of diabetes in which patients exhibit persis- prevention therefore is an important goal. D tent proteinuria, hypertension, declining renal function, Evidence from epidemiologic and family-based studies and an increased premature mortality largely as a result of cardio- strongly suggest a role for both susceptibility and protective vascular disease. The pathophysiology of DN involves glomerular genes in the development of DN in type 1 diabetes. This is capillary hypertension, glomerular hyperfiltration, mesangial matrix illustrated by the observation that the disease generally devel- expansion, and glomerulosclerosis (1–3). In Western populations, DN ops within 20 yr after diagnosis of type 1 diabetes, whereas the has emerged as the leading cause of ESRD (4,5), resulting in patients’ risk for developing nephropathy decreases in patients who requiring dialysis and transplantation. have diabetes without nephropathy after 20 yr of duration of The risk for developing nephropathy in patients with diabe- diabetes (10). Also, there is strong concordance for nephropa- tes becomes greater with increasing duration of diabetes and thy status among siblings with diabetes (11,12), and there is poor regulation of blood glucose and BP. Studies have shown, evidence to support the involvement of genes with major and however, that irrespective of good glycemic control, some pa- minor effects in the pathogenesis of this disease (12,13). tients with diabetes still develop nephropathy (6,7). In addition, Identification of validated genetic markers for high and low although administration of antihypertensive therapy to mi- risk for nephropathy will facilitate prediction of nephropathy in croalbuminuric patients may retard progression toward ESRD patients with diabetes; this information also could lead to the (8,9), irreversible structural changes in the kidney have already development of pharmacogenetic treatments to prevent this disease. Genome-wide linkage analysis, however, has been complicated largely by difficulties in obtaining sufficient num- Received May 12, 2005. Accepted January 3, 2006. bers of sibling pairs who have diabetes and are concordant or Published online ahead of print. Publication date available at www.jasn.org. discordant for nephropathy. Consequently, case-control associ- Address correspondence to: Dr. David A. Savage, Nephrology Research Group, ation studies, using a candidate gene approach, have become Queen’s University Belfast, c/o Medical Genetics, Floor A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, Northern Ireland. Phone: ϩ44-0- the method of choice for identifying causal gene variants that 28-90329241 ext. 2149; Fax: ϩ44-0-28-90236911; E-mail: [email protected] contribute to DN. A number of such studies have been per-

formed, but no consistent positive associations have been iden- 30 s followed by 20 cycles of 89°C for 15 s, 55°C for 15 s, 72°C for 30 s, tified (14–18). Conflicting findings may be due to several fac- and finally 72°C for 20 min. Each PCR product was diluted 10-fold in ␮ ␮ tors, including genetic heterogeneity, inadequate sample sizes, ddH2O; 1 l of diluted PCR product then was combined with 12 lof ␮ and use of inappropriate selection criteria for patients (19). ROX/HiDi (prepared by combining 0.3 l of GS-400HD [ROX] and 11.7 ␮l of HiDi formamide, both supplied by Applied Biosystems), and the In an attempt to identify chromosomal regions that harbor mixture was subjected to capillary electrophoresis on a 3100 genetic possible candidate genes for DN, we performed a low-resolu- analyser (Applied Biosystems). The raw data were exported to ABI tion genome-wide microsatellite [i.e., anonymous nucleotide Genescan software to confirm size labeling. Files then were imported repeat units, e.g., (CA)n] association screen, using the same into ABI Genotyper software, where alleles were identified appropriately. method and markers used in the Genetic Analysis of Multiple sclerosis in EuropeanS (GAMES) collaborative project (20–23; http://www-gene.cimr.cam.ac.uk/MSgenetics/GAMES). Sep- Genome-Wide Microsatellite Genotyping The 6000 microsatellite markers that were used in the screening arate pools of DNA, derived from Irish patients with type 1 phase consist of 811 that make up the Applied Biosystems high-density diabetes and with (cases) and without (controls) nephropathy, linkage mapping set (LMS-HD5) together with an additional 5189 were screened for association using 6000 fluorescently labeled identified from the Co-operative for Human Linkage Centre database microsatellite markers (the GAMES marker set). A comparison (http://gai.nci.nih.gov/CHLC), the Genethon database (http://www. of allele image profiles (AIP; a series of peaks of varying heights genethon.fr), and the Genome database (http://www.gdb.org). The reflecting the allele frequency distribution within each ana- total number of markers from each chromosome was chosen to reflect lyzed DNA pool) from cases and controls permitted ranking of the number of genes that map to that chromosome (according to the markers according to empirical P values; individual genotyp- National Center for Biotechnology Information web site in December ing then was performed on the 50 most associated markers. In 1999, http://www.ncbi.nlm.nih.gov). The density of markers therefore this report, we describe the major findings of this genome-wide is biased toward gene-rich and against gene-poor chromosomes. How- association screen and demonstrate the feasibility of this ap- ever, markers were not selected to be necessarily intragenic. Within each chromosome, markers were selected so as to make the distribution proach for identifying possible candidate genes in DN. of markers as uniform as possible according to their location scores on the genetic location database (http://cedar.genetics.soton.ac.uk/ Materials and Methods public_html). The 6000 microsatellite markers that were used in this Patient Groups and Selection Criteria study resulted in an average intermarker spacing of 0.6 cM (or 0.5 Mb; Patients were recruited from Northern Ireland (n ϭ 105 cases; n ϭ 105 Appendix 1); gender-averaged cM measurements were derived from controls) and the Republic of Ireland (n ϭ 95 cases, n ϭ 95 controls). the Marshfield Master Map (http://www.ncbi.nlm.nih.gov/mapview). Parents and grandparents of patients were born in Ireland. All patients There are 329 gaps Ͼ2 cM in gene-poor regions, 11 of which are Ͼ10 were white and had type 1 diabetes, defined as diabetes that was cM. These gaps correspond mainly to pericentromeric regions, partic- diagnosed before the age of 31 yr and required insulin from diagnosis. ularly on acrocentric chromosomes, and sections of the genome that Cases with nephropathy were defined by development of persistent generally contain very few genes. In total, 4723 (79%) of the markers are proteinuria (Ͼ0.5 g protein/24 h) at least 10 yr after diagnosis of dinucleotides, 1019 (17%) are tetranucleotides, and 258 (4%) are diabetes, hypertension (BP Ͼ135/85 mmHg and/or treatment with trinucleotides. The estimated average heterozygosity (according to data antihypertensive agents), and presence of diabetic retinopathy. In con- in the originating databases) of the 6000 markers is 70%. Details of the trast, controls were patients who had diabetes duration of at least 15 yr markers, including primer sequences, are available on the GAMES web and urinary albumin in the normal range and were not receiving site (http://www-gene.cimr.cam.ac.uk/MSgenetics/GAMES). antihypertensive treatment. Patients with microalbuminuria were ex- Aliquots of DNA (2.5 ␮l; 25 ng) from each pool were PCR amplified cluded from both groups. Ethical approval was obtained from the by individual primer pairs that flanked the 6000 microsatellite markers appropriate research ethics committees, and written informed consent as described above for pool assessment. Amplification products were was obtained from patients before this study was conducted. subjected to capillary electrophoresis on a 3700 genetic analyser (Ap- plied Biosystems), either as a uniplex or a triplex essentially as described above. For PCR products with AIP peak heights above 10,000 Construction and Assessment of DNA Pools (i.e., saturated), the products were diluted and run again. To minimize Genomic DNA samples from cases and controls were quantified electrophoretic artifacts, we performed repeat electrophoresis on all using the Picogreen method (Molecular Probes, Eugene, OR) and nor- PCR products; when contradictory results were observed, PCR prod- ␮ malized to 10 ng/ l in 10 mM Tris/0.1 mM EDTA (pH 7.6). Equal ucts were run on a third occasion to assist in resolving ambiguities. ␮ ␮ volumes (200 l) of individual DNA samples (10 ng/ l) from cases and Repeat PCR amplification and electrophoresis were performed system- controls were combined to generate the DNA pools. Before use in the atically on ranked markers with the smallest empirical P values as genome-wide screen, the pools were validated by comparing estimated described in the Statistical Analyses section to facilitate the identifica- allele frequencies that were generated from pools (five replicate AIP in tion of the 50 most associated markers. each case) with allele frequencies obtained by individual genotyping of samples that composed the pools, for two randomly selected microsatellite markers, D19S49 (dinucleotide) and D7S1821 (tetranucleotide), as Statistical Analyses follows. PCR reactions (15 ␮l in total) that contained 25 ng DNA, 5 Clinical characteristics of the patients were compared by the inde- pmol of each primer (forward primer for both markers was labeled pendent samples t test. The electrophoretic output for a given micro- with FAM), and 9 ␮l of True Allele PCR Premix (Applied Biosystems, satellite marker using pooled DNA comprises a series of peaks, an AIP Warrington, UK) were set up. All PCR reactions were performed using (Figure 1), where the height of each peak reflects the frequency of the an MJ Tetrad thermal cycler using the following cycling conditions; corresponding microsatellite allele. Allele counts were estimated by 95°C for 12 min, 10 cycles at 94°C for 15 s, 55°C for 15 s, and 72°C for normalizing the observed peak heights according to the number of J Am Soc Nephrol 17: 831–836, 2006 Microsatellite Screen in Diabetic Nephropathy 833

average BP was higher in cases versus controls, despite the use of antihypertensive drug treatment by patients with DN.

Figure 1. Allele image profiles for D10S1435 marker in case and Assessment of DNA Pools and Genome-Wide Screen control pools. Excellent concordance was found between allele frequencies that were determined by pooling versus individual genotyping methods for D19S49 and D7S1821, in both case and control pools (data not alleles in the respective pool. Allele counts from cases and controls then shown). Interpretable AIP were generated for both case and control ␹2 were compared using a test. Because pooling introduces additional pools in 5353 (89.2%) markers. A total of 647 markers were excluded sources of variance above and beyond the expected sampling variance, because of PCR failures in case and/or control pools or difficulties in exact P values cannot be determined for these statistics. We therefore data interpretation (e.g., weak or diffuse peaks in AIP). Markers were used the observed distribution of results to calculate empirical P values for each marker and therefore enable their ranking (23), i.e., markers ranked according to empirical P value (lowest to highest). To refine that were ranked according to lowest empirical P value showed the the marker ranking, we systematically performed repeat PCR ampli- greatest difference between case and control AIP. To refine this ranking fication and electrophoresis of pooled samples on the 63 most highly and reduce the effects of the additional sources of error introduced by ranked markers until 50 markers that showed consistent results were pooling, we generated replicate AIP from both the case and control identified. Empirical P values that were obtained for these markers pools for each of the 63 most highly ranked markers; the total data then were not corrected. These markers then were genotyped individually were reanalyzed, and the markers were reranked. in the same samples that composed the pools (Appendix 2). Of note, Individual genotyping then was performed in the 50 most highly ranked two markers on chromosome 10 (D10S558 and D10S1435) revealed markers to distinguish definitively those that showed genuine allele fre- significant differences in allele distribution between groups after cor- quency differences from those that ranked highly as a result of pooling- rection by the stringent Bonferroni method (Table 2). The allele dis- induced variance. Results from the individual typing were tested for signifi- tributions for these two markers are presented in Table 3 and indicate cance using a ␹2 test after amalgamation of rare alleles. When any cell in the contingency table had an expected value Ͻ5, the associated allele was pooled that larger D10S558 alleles were associated with increased risk for with the next rarest allele and this process was repeated until no cell had an nephropathy (OR 2.50; 95% CI 1.83 to 3.41), whereas the D10S1435 expected value Ͻ5. This analysis was performed using the T2 statistic in the 272 allele was associated with decreased risk (OR 0.44; 95% CI 0.32 to CLUMP program (24). P values for individual genotyping were corrected for 0.61). Furthermore, four additional markers (D2S291, D4S2937, the total number of comparisons made in the initial analysis by DNA pooling D6S281, and D17S515) gave small uncorrected P values, and al- (5353). Markers that attained significance were examined using the Clump T4 though they failed to achieve statistical significance after Bonferroni statistic; the alleles were pooled into two groups in such a way as to maximize correction, they warrant further consideration (Table 2). the ␹2 test statistic. This pooling enabled odds ratios (OR) and 95% confidence intervals (CI) to be obtained. Discussion Results Genetic association studies that have used a candidate gene ap- Clinical Characteristics of Patients proach have been unsuccessful to date in identifying single-nucle- Table 1 lists the clinical characteristics of the case and control otide polymorphisms (SNP) that show a consistent association with groups. There were a total of 84 and 124 women in the case and DN in type 1 diabetes. In addition, high-resolution whole-genome control groups, respectively. The age at diagnosis of diabetes was SNP screening in DN currently is not feasible, partly because of the not significantly different between the groups. The mean duration extremely high total cost of SNP genotyping and uncertainty as to the of diabetes was at least 25 yr for both groups. As expected, the composition of an appropriate panel of SNP for a comprehensive scan. However, with recent advances in novel, low-cost, high- throughput genotyping technologies (25,26) and improvements in Table 1. Clinical characteristics of cases (n ϭ 200) and statistical methods for data analysis (27), as well as projects such as a controls (n ϭ 200) who composed DNA pools the International HapMap (28), such screening certainly will be feasible in the future. It is interesting that a recent Japanese study Cases Controls assessed Ͼ80,000 SNP for association with DN in type 2 diabetes and Age at diagnosis of 17.3 Ϯ 7.2 16.9 Ϯ 6.7 a significant SNP association was reported between the engulfment diabetes (yr) and cell motility (ELMO 1) gene and DN (29). Duration of diabetes (yr)b 29.7 Ϯ 7.8 25.6 Ϯ 5.5 In this report, we applied a highly efficient but low-resolu- c Ϯ Ϯ HbA1c (%) 8.9 1.4 8.3 0.9 tion approach for whole-genome association screening in DN Systolic BP(mmHg)c 142.4 Ϯ 16.7 124.8 Ϯ 11.4 that uses 6000 microsatellite markers and a DNA pooling strat- Diastolic BP (mmHg)c 81.2 Ϯ 8.0 76.6 Ϯ 5.4 egy; this strategy originally was used in the GAMES collabo- ESRD 114 (57%) N/A rative project (20–23). Importantly, the GAMES project suc- ceeded in identifying the well-established association of a Ϯ Data are n, mean SD. multiple sclerosis with the MHC region, confirming that the bDuration of diabetes was calculated from the date of diagnosis of diabetes to date of recruitment. approach is able to identify microsatellites in linkage disequi- cValues are the average of three most recent measurements librium with genuine but modest susceptibility factors. prior to recruitment. Strict phenotyping criteria were used in the selection of cases 834 Journal of the American Society of Nephrology J Am Soc Nephrol 17: 831–836, 2006

Table 2. P values for individual genotyping of case and control DNA samples that composed pools for markers that revealed statistically significant differences between groups and those that warrant further investigation

Markera Chromosome P P b Functional Candidate Genes for DN uncorrected corrected within 1.5 Mb of Marker

D10S558 10 0.000001 0.005 GTPBP4 D10S1435 10 0.000003 0.016 GTPBP4 D6S281c 6 0.00007 NS THBS2, SMOC2, TBP D4S2937c 4 0.0001 NS NDST3, NDST4 D2S291c 2 0.0002 NS ADD2 D17S515c 17 0.0003 NS SSTR2, SLC9A3R1, ITGB4, CDK3, GALR2

aPCR primer sequences for these markers may be obtained from http://www-gene.cimr.cam.ac.uk/MSgenetics. bP values were corrected on the basis of the total number of comparisons made (5353 markers in initial screen by DNA pooling) in accordance with the stringent Bonferroni method. cMarkers that revealed small uncorrected P values and warrant further investigation.

Table 3. Comparison of allele distributions between cases (n ϭ 200) and controls (n ϭ 200) from individual genotyping for two markers that remained significant after Bonferroni correction

Sizea Total 178 180 196 198 204 206 208 210 212 214

D10S558b case 16 148 53 70 10 21 37 23 19 3 400 control 22 220 57 33 4 10 16 18 16 4 400 264 268 272 276 Total D10S1435c case 108 147 82 63 400 control 97 113 148 42 400

aSized (base pairs) referenced to ROX-400HD size standard. bOdds ratio (Ն198 versus Յ196) ϭ 2.50 (95% confidence interval 1.83 to 3.41). cOdds ratio (272 versus 264,268,276) ϭ 0.44 (95% confidence interval 0.32 to 0.61). and controls in our study to reduce the possibility of false phoretic and/or PCR artifacts (e.g., stutter bands, length-depen- outcomes related to phenotype misclassification. In addition, dent amplification). Recently, it was shown that multiple dis- all participants were white and of Irish descent, therefore re- joined pools are preferable to single pools (32), although there ducing potential problems associated with population stratifi- are additional cost considerations associated with this strategy. cation. We identified the 50 most associated markers that The power of our study is limited by the sample size used, the showed consistent results on repeat analysis using DNA pools. confounding produced by our use of the pooling approach, and Individual genotyping of D10S558 and D10S1435 markers on the low density of markers used. We recognize that genes that chromosome 10 revealed statistically significant differences in exert small effects or are not in linkage disequilibrium with the the distribution of marker alleles in cases compared with con- markers used cannot be identified by this method. Nevertheless, trols (Table 2). It is interesting that suggestive linkage of we have shown that this genome-wide microsatellite screen, using D10S1435 in early-onset nondiabetic ESRD was reported re- pools of DNA, is a cost-effective strategy for identifying positive cently in black patients (30). Also, a recent study reported associations between microsatellite markers and DN. Further- linkage of a locus on chromosome 6q (D6S281) to essential more, we have demonstrated the utility of the GAMES method for hypertension in UK patients (31). Relevant functional candidate the identification of possible functional candidate genes for this genes are located within the vicinity of D10S558 and D10S1435, disease. We intend to assess haplotype tag SNP (33) within these as well as for four other markers (D2S291, D4S2937, D6S281, candidate genes for association with DN using relatively large and D17S515) that are worthy of further investigation (Table 2). case-control studies such as the Juvenile Diabetes Research Foun- The discrepancies that were observed in allele frequency data dation–funded Genetics of Kidneys in Diabetes (United Kingdom) that were obtained for DNA pooling and individual genotyp- case-control collection and Diabetes UK Warren 3 family collec- ing are not unexpected. However, this was minimized by care- tion; replication studies will be performed in case and control ful construction and assessment of the pools and applying collections derived from other populations, such the US Genetics checks for false-positive outcomes that resulted from electro- of Kidneys in Diabetes collection. J Am Soc Nephrol 17: 831–836, 2006 Microsatellite Screen in Diabetic Nephropathy 835

Appendix 1. Marker densities (cM and Mb) in each Appendix 2. P values for individual genotyping of case chromosome for 6000 microsatellite markersa and control DNA samples that composed pools for the 50 highest ranked markers derived from DNA pooling Marker Marker Length Length and listed by chromosome Chromosome (cM) Density (Mb) Density (cM) (Mb) a Marker Chromosome Puncorrected 1 289.67 0.47 245 0.40 D1S2877 1 0.03 2 269.07 0.60 242 0.54 D1S420 1 0.06 3 228.14 0.58 200 0.51 D1S211 1 0.61 4 211.66 0.73 191 0.66 D1S2709 1 0.80 5 197.54 0.66 180 0.60 D1S496 1 0.97 6 193.14 0.52 171 0.46 D2S291 2 0.0002 7 181.97 0.59 158 0.51 D2S2289 2 0.009 8 167.91 0.71 146 0.62 D2S152 2 0.99 9 168.98 0.69 138 0.57 D4S2937 4 0.0001 10 173.13 0.65 135 0.50 D4S2973 4 0.56 D4S243 4 0.57 11 147.78 0.43 134 0.39 D4S1575 4 0.72 12 168.79 0.54 132 0.42 GABRB1 4 0.99 13 114.99 0.83 114 0.83 D5S393 5 0.004 14 138.19 0.67 106 0.51 D5S107 5 0.004 15 122.15 0.61 100 0.50 D5S2041 5 0.85 16 134.13 0.80 88 0.53 D5S656 5 0.94 17 126.46 0.52 80 0.33 D6S281 6 0.00007 18 126.00 1.25 76 0.75 D6S1690 6 0.02 19 105.02 0.49 64 0.30 D6S1717 6 0.03 20 101.22 0.68 62 0.42 D6S1697 6 0.05 21 57.78 0.95 46 0.75 TNF 6 0.99 D7S1493 7 0.99 22 62.32 0.58 49 0.46 D7S2494 7 0.99 X 104.83 0.36 154 0.53 D8S546 8 0.008 Total: 3590.87 0.65 3011 0.53 D8S1469 8 0.98 aData obtained from NCBI Genome Build 35.1, Released D8S1743 8 0.99 June 4, 2004 (accessed September 14, 2005). D9S1831 9 0.03 D9S1674 9 0.82 D9S1797 9 0.89 D10S558 10 0.000001 Acknowledgments D10S1435 10 0.000003 Some of these data were presented at the American Society of Ne- D10S568 10 0.01 phrology, San Diego, CA, November 12 to 17, 2003. D10S188 10 0.01 We thank the Northern Ireland Research and Development Office D10S1643 10 0.61 and the Northern Ireland Kidney Research Fund for supporting this D10S1659 10 0.67 project. We also thank Julia Gray and Tai Wai Yeo (Neurology Unit, D10S529 10 0.99 University of Cambridge) and Nick Leaves (HGMP Resource Centre, D10S578 10 0.99 Cambridge) for technical assistance. D11S1760 11 0.002 D11S971 11 0.94 D11S4095 11 0.98 References GGAT1E2 12 0.19 1. Hostetter TH: Mechanisms of diabetic nephropathy. Am J D14S61 14 0.97 Kidney Dis 23: 188–192, 1994 D14S1048 14 0.99 2. Mauer SM: Structural-functional correlations of diabetic ne- D17S515 17 0.0003 phropathy. Kidney Int 45: 612–622, 1994 D17S789 17 0.77 3. Mogensen CE: Glomerular hyperfiltration in human diabetes. D19S891 19 0.001 Diabetes Care 17: 770–775, 1994 D19S865 19 0.03 4. UK Renal Registry Report, 2002. Available: www.renalreg. D21S1922 21 0.18 com. Accessed March 2005 D22S535 22 0.25 5. United States Renal Data System: Annual Data Report 2003. aPCR primer sequences for these markers may be obtained Incidence and Prevalence of ESRD. Available: www.usrds. from http://www-gene.cimr.cam.ac.uk/MSgenetics. org/2003/. Accessed March 2005 6. The Diabetes Control and Complications Research Group: The effect of intensive treatment of diabetes on the develop- 836 Journal of the American Society of Nephrology J Am Soc Nephrol 17: 831–836, 2006

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