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Genetic Variation in the CCL18-CCL3-CCL4 Chemokine Gene Cluster Influences HIV Type 1 Transmission and AIDS Disease Progression

2006; Elsevier BV; Volume: 79; Issue: 1 Linguagem: Inglês

10.1086/505331

ISSN

1537-6605

Autores

William S. Modi, James A. Lautenberger, Ping An, Kevin Scott, James J. Goedert, Gregory D. Kirk, Susan Buchbinder, John Phair, Sharyne Donfield, Stephen J. O’Brien, Cheryl A. Winkler,

Tópico(s)

T-cell and B-cell Immunology

Resumo

CCL3 (MIP-1α), CCL4 (MIP-1β), and CCL18 (DC-CK1/PARC/AMAC-1) are potent chemoattractants produced by macrophages, natural killer cells, fibroblasts, mast cells, CD4+ T cells, and CD8+ T cells. CCL3 and CCL4 are natural ligands for the primary human immunodeficiency virus type 1 (HIV-1) coreceptor CCR5 and are also known to activate and enhance the cytotoxicity of natural killer cells. Genomic DNAs from >3,000 participants enrolled in five United States–based natural-history cohorts with acquired immunodeficiency syndrome (AIDS) were genotyped for 21 single-nucleotide polymorphisms (SNPs) in a 47-kb interval on chromosome 17q12 containing the genes CCL3, CCL4, and CCL18. All 21 SNPs were polymorphic in African Americans (AAs), whereas 7 of the 21 had minor-allele frequencies 3,000 participants enrolled in five United States–based natural-history cohorts with acquired immunodeficiency syndrome (AIDS) were genotyped for 21 single-nucleotide polymorphisms (SNPs) in a 47-kb interval on chromosome 17q12 containing the genes CCL3, CCL4, and CCL18. All 21 SNPs were polymorphic in African Americans (AAs), whereas 7 of the 21 had minor-allele frequencies 3,000 participants enrolled in five United States–based longitudinal HIV-1/AIDS cohorts. Two primary questions were addressed in the study. First, what is the extent and nature of SNP and haplotype variation in and around these three genes in two racial groups? Second, do genetic variants exist in these genes that influence HIV-1 infection and/or AIDS disease progression? Participants were enrolled in five longitudinal cohorts. The AIDS Link to the Intravenous Experience (ALIVE) is a community-based cohort of adult injection drug users in Baltimore.11Vlahov D Graham N Hoover D Flynn C Bartlett JG Margolick JB Lyles CM Nelson KE Smith D Holmberg S Farzadegan H Prognostic indicators for AIDS and infectious disease death in HIV-infected injection drug users: plasma viral load and CD4+ cell count.JAMA. 1998; 279: 35-40Crossref PubMed Scopus (184) Google Scholar The racial distribution is 92.4% AA and 7.6% EA. The Hemophilia Growth and Development Study (HGDS) is a multicenter prospective study that enrolled children with hemophilia.12Hilgartner MW Donfield SM Willoughby A Contant Jr, CF Evatt BL Gomperts ED Hoots WK Jason J Loveland KA McKinlay SM Stehbens JA Hemophilia Growth and Development Study: design, methods, and entry data.Am J Pediatr Hematol Oncol. 1993; 15: 208-218Crossref PubMed Scopus (103) Google Scholar The cohort consists of 126 HIV-1–uninfected and 207 HIV-1–infected children who were exposed to HIV-1 through blood products between 1982 and 1983. The racial distribution is 72% EA, 15% Hispanic, and 11% AA. The Multicenter Hemophiliac Cohort (MHCS) is a prospective study that enrolled persons with hemophilia.13Goedert JJ Kessler CM Aledort LM Biggar RJ Andes WA White II, GC Drummond JE Vaidya K Mann DL Eyster ME Ragni MV Lederman MM Cohen AR Bray GL Rosenberg PS Friedman RM Hilgartner MW Blattner WA Kroner B Gail MH A prospective study of human immunodeficiency virus type 1 infection and the development of AIDS in subjects with hemophilia.N Engl J Med. 1989; 321: 1141-1148Crossref PubMed Scopus (490) Google Scholar The racial distribution is 90% EA, 6% AA, and 3% Hispanic. The Multicenter AIDS Cohort Study (MACS) is a longitudinal study of men who have sex with men (MSM) from four U.S. cities: Chicago, Baltimore, Pittsburgh, and Los Angeles.14Kaslow RA Ostrow DG Detels R Phair JP Polk BF Rinaldo Jr, CR The Multicenter AIDS Cohort Study: rationale, organization, and selected characteristics of the participants.Am J Epidemiol. 1987; 126: 310-318Crossref PubMed Scopus (968) Google Scholar, 15Detels R Liu Z Hennessey K Kan J Visscher BR Taylor JMG Hoover DR Rinaldo Jr, CR Phair JP Saah AJ Giorgi JV for the Multicenter AIDS Cohort StudyResistance to HIV-1 infection.J Acquir Immune Defic Syndr. 1994; 7: 1263-1269Crossref PubMed Scopus (66) Google Scholar The racial distribution is 83.3% EA, 10% AA, and 5% Hispanic. The San Francisco City Clinic Study (SFCC) is a cohort of MSM originally enrolled in a hepatitis B study in 1978–1980.16Rutherford GW Lifson AR Hessol NA Darrow WW O'Malley PM Buchbinder SP Barnhart JL Bodecker TW Cannon L Doll LS Holmberg SD Harrison JS Rogers MF Werdegar D Jaffe HW Course of HIV-I infection in a cohort of homosexual and bisexual men: an 11 year follow up study.BMJ. 1990; 301: 1183-1188Crossref PubMed Scopus (220) Google Scholar The cohort consists of 211 individuals, 203 of whom are EA. The majority of subjects were enrolled into the cohorts during the following years: ALIVE, 1988–1989; MACS, 1984–1985; MHCS, 1982–1985; and SFCC, 1978–1980. All individuals used for association analyses were (1) uninfected individuals who have undocumented levels of exposure but who belong to an HIV-1 risk group; (2) high-risk, exposed, uninfected (HREU) individuals with documented high risk for HIV-1 exposure; (3) HIV-1–positive seroconvertors, with seroconversion dates estimated as the midpoint between the last visit with seronegative test results and the first visit with seropositive test results; or (4) seroprevalent individuals who were infected with HIV-1 at study enrollment. The HREU participants were exposed to HIV-1 by receptive anal intercourse with multiple partners,15Detels R Liu Z Hennessey K Kan J Visscher BR Taylor JMG Hoover DR Rinaldo Jr, CR Phair JP Saah AJ Giorgi JV for the Multicenter AIDS Cohort StudyResistance to HIV-1 infection.J Acquir Immune Defic Syndr. 1994; 7: 1263-1269Crossref PubMed Scopus (66) Google Scholar, 16Rutherford GW Lifson AR Hessol NA Darrow WW O'Malley PM Buchbinder SP Barnhart JL Bodecker TW Cannon L Doll LS Holmberg SD Harrison JS Rogers MF Werdegar D Jaffe HW Course of HIV-I infection in a cohort of homosexual and bisexual men: an 11 year follow up study.BMJ. 1990; 301: 1183-1188Crossref PubMed Scopus (220) Google Scholar by transfusion with nonheated units of factor VIII or IX between 1982 and 1983,17Kroner BL Rosenberg PS Aledort LM Alvord WG Goedert JJ HIV-1 infection incidence among persons with hemophilia in the United States and western Europe, 1978-1990. Multicenter Hemophilia Cohort Study.J Acquir Immune Defic Syndr. 1994; 7: 279-286PubMed Google Scholar or by injection drug use.11Vlahov D Graham N Hoover D Flynn C Bartlett JG Margolick JB Lyles CM Nelson KE Smith D Holmberg S Farzadegan H Prognostic indicators for AIDS and infectious disease death in HIV-infected injection drug users: plasma viral load and CD4+ cell count.JAMA. 1998; 279: 35-40Crossref PubMed Scopus (184) Google Scholar A concise summary of these cohorts is available.18Modi WS Scott K Goedert JJ Vlahov D Buchbinder S Detels R Donfield S O'Brien SJ Winkler C Haplotype analysis of the SDF-1 (CXCL12) gene in a longitudinal HIV-1/AIDS cohort study.Genes Immun. 2005; 6: 691-698PubMed Google Scholar Over 3,000 individuals were genotyped, and the data were used to estimate allele frequencies, but genotypes from only 1,326 individuals were used in disease association analyses: 449 AAs (159 seronegative individuals and 290 seroconvertors) and 877 EAs (216 seronegative individuals and 661 seroconvertors). Since antiretroviral treatment history was unavailable for a majority of subjects, participants were censored for highly active antiretroviral therapy (HAART), instead of the effects of HAART being adjusted for in the models.19Detels R Munoz A McFarlane G Kingsley LA Margolick JB Giorgi J Schrager LK Phair JP for the Multicenter AIDS Cohort Study InvestigatorsEffectiveness of potent antiretroviral therapy on time to AIDS and death in men with known HIV infection duration.JAMA. 1998; 280: 1497-1503Crossref PubMed Scopus (621) Google Scholar The censoring date was the earlier of two dates: the last recorded follow-up visit or December 31, 1995, for the MACS, MHCS, HGDS, and SFCC cohorts and the last recorded visit or July 31, 1997, for the ALIVE cohort. A later censoring date was used for the ALIVE cohort because administration of HAART was delayed.20Celentano DD Galai N Sethi AK Shah NG Strathdee SA Vlahov D Gallant JE Time to initiating highly active antiretroviral therapy among HIV-infected injection drug users.AIDS. 2001; 15: 1707-1715Crossref PubMed Scopus (178) Google Scholar The median follow-up times were 5.1, 7.3, 10.7, and 13.4 years for the ALIVE, MACS, MHCS, and SFCC cohorts, respectively. Three separate end points reflecting advancing AIDS pathogenesis were considered for seroconvertors: (1) the Centers for Disease Control and Prevention (CDC) definition of AIDS in 1993 (AIDS-93): HIV-1 infection plus a CD4+ T cell count of 3,000 bp were analyzed on DNAs from 36 unrelated EAs and 36 unrelated AAs presenting with various HIV-1/AIDS phenotypes. A total of 21 SNPs were genotyped (table 1) by use of either PCR-RFLP or TaqMan allelic discrimination (Applied Biosystems) techniques (table 2).25Modi WS Goedert JJ Strathdee S Buchbinder S Detels R Donfield S O'Brien SJ Winkler C MCP-1-MCP-3-Eotaxin gene cluster influences HIV-1 transmission.AIDS. 2003; 17: 2357-2365Crossref PubMed Scopus (61) Google ScholarTable 1MAFs and Gene Positions for 21 SNPs in the CCL18, CCL3, and CCL4 Chemokine GenesMAFaMAFs were determined by genotyping 675–1,116 AAs and 978–2,054 EAs for any given SNP.SNPdbSNPAAEALocationPositionInter-SNP DistancebDistance from the previous SNP. The total distance between SNPs 1 and 21 is 47,637 bp. (bp)1rs712040.497.108CCL18 promoter−6111 C/T…2rs2015086.384.13CCL18 promoter−86 C/T6,0253rs2015070.039.087CCL18 intron 1+81 A/G1664rs712043.058.302CCL18 intron 1+4965 C/T4,8845rs854472.062.304CCL18 intron 1+5906 A/G9416rs1719220.055.277CCL18 3′ UTR+12575 A/G6,6697rs1634481.055.271CCL3 3′ UTR+11730 A/G1,3988rs1719127.121.004CCL3 3′ UTR+1829 A/G9,9019rs8951.081.218CCL3 exon 3+1685 C/T14410ss46566437.083.004CCL3 exon 3+1342 G/T (E79D)34311rs1719130.194.24CCL3 intron 2+1159 C/T18312rs1130371.154.234CCL3 exon 2+868 C/T (P60P)29113rs1719133.065.005CCL3 intron 1+740 A/G12814rs1719134.151.24CCL3 intron 1+459 A/G28115ss46566438.083.005CCL3 intron 1+113 C/T34616ss46566439.08.004CCL3 promoter−891 T/C1,00217rs1634498.077.003CCL3 promoter−2021 C/T1,13118rs1634507.113.237CCL4 promoter−5725 A/C6,14919rs1719144.054.222CCL4 intron 1+104 A/T5,82920rs1719146.0320CCL4 exon 2+663 C/T (T39T)55821rs1719153.077.233CCL4 3′ UTR+1931 A/T1,268a MAFs were determined by genotyping 675–1,116 AAs and 978–2,054 EAs for any given SNP.b Distance from the previous SNP. The total distance between SNPs 1 and 21 is 47,637 bp. Open table in a new tab Table 2Primers and Probes and the Restriction Enzymes Used in Genotyping AssaysSNPdbSNPPCR Primer(s)TaqMan Probes or Restriction Enzyme1rs712040ABI Assays-on-Demand, hCV2555159ABI Assays-on-Demand2rs2015086ABI Assays-on-Demand, hCV2555168ABI Assays-on-Demand3rs2015070ABI Assays-on-Demand, hCV7449055ABI Assays-on-Demand4rs712043ABI Assays-on-Demand, hCV2262137ABI Assays-on-Demand5rs854472ABI Assays-on-Demand, hCV7449039ABI Assays-on-Demand6rs1719220ABI Assays-on-Demand, hCV2555178ABI Assays-on-Demand7rs1634481ABI Assays-on-Demand, hCV2555183ABI Assays-on-Demand8rs17191275′-GGGAAATAATAAAGATGCTCTTTTAAAA-3′; 5′-TGCCTATGATTCCTCTTAACC-3′BstNI9rs89515′-CCCTTCCCTCACACCGC-3′; 5′-AACTCAATACTGGTTTACCTTTTAAAAGAG-3′5′-CCTACACAGGCTGATGACAGCCACT-3′; 5′-CCTACACAGGCCGATGACAGCC-3′10ss465664375′-ACAGCTTCCTAACCAAGCGAA-3′; 5′-AGCTCCAGGTCGCTGACA-3′BslI11rs17191305′-GGCCACCCCTACTGAGTCAC-3′; 5′-TCAGGGCTTGCTCCTCTTTC-3′5′-AAGCTCTCTAGACAGAGATAGGCAGGG-3′; 5′-AGCTCTCTAGACGGAGATAGGCAGG-3′12rs11303715′-AATTTCATAGCTGACTACTTTGAGACGA-3′; 5′-GGCCTGTCTCTGCCCCA-3′5′-TGCTCCAAGCCCGGTGTCATGTA-3′; 5′-CAGTGCTCCAAGCCTGGTGTCATGT-3′13rs17191335′-AAGCCTGCCTTCCTCAACTG-3′; 5′-AGCTATGAAATTCTGTGGAATCTGC-3′BanII14rs17191345′-CACTCTAGGTCTCCCAGGAGCT-3′; 5′-GACTGTTCTCTTATCTCAGTTCTCTTCAG-3′5′-AGAGATGTCCAAGGCTTCTCTTGGGTT-3′; 5′-AGAGATGTCCAAGGTTTCTCTTGGGTTG-3′15ss465664385′-CCTCCTCTGCACCATGGCT-3′; 5′-GGGCTTTTAGGCCACAAGAAA-3′5′-ACCATGGCCAGAGAGTGGT-3′; 5′-ACCATGACCAGAGAGTGGT-3′16ss465664395′-TCAGACTTTGTAGAATTTGTATAATGTCG-3′; 5′-AGGGAATGATAAATTATCCACTAGATCA-3′MnlI17rs16344985′-CAGACACTTAGAAAGGACAGAATTCC-3′; 5′-TGATAAAGCTAAATTGGGACCAAAC-3′HincII18rs16345075′-TCTTGCTGGAGTATTCCCTATGA-3′; 5′-GCCACAACCTGCTCTTGC-3′HphI19rs16345145′-GCCTTCTGCTCTCCAGCG-3′; 5′-TTCTTAAGAAAATAGGAACTTGAATGTTAT-3′HinfI20rs17191465′-CATGTTAGGTGGGAATGGATATTT-3′; 5′-GGTGGATGGGATCCTTCCT-3′BstUI21rs17191535′-CAAGGGTTTTAACACCCTTATGAAC-3′; 5′-CCAAGCAGGCCTACAAGCTT-3′5′-TTTCCTTAACTGTGAAACT-3′; 5′-TTTCCTTAACAGTGAAACT-3′ Open table in a new tab Allele frequencies and genotype frequencies were calculated and tests for Hardy-Weinberg equilibrium were performed using SAS Genetics software (SAS Institute). Unphased diploid genotype data were partially phased by use of PHASE,26Stephens M Donnelly P A comparison of Bayesian methods for haplotype reconstruction from population genotype data.Am J Hum Genet. 2003; 73: 1162-1169Abstract Full Text Full Text PDF PubMed Scopus (2942) Google Scholar and the output from PHASE was input into HAPLOVIEW27Barrett JC Fry B Maller J Daly MJ Haploview: analysis and visualization of LD and haplotype maps.Bioinformatics. 2005; 21: 263-265Crossref PubMed Scopus (11437) Google Scholar for pairwise D′ and r2 calculation, haplotype estimation, and haplotype-block construction. Comparisons of allele, genotype, and haplotype frequencies between the seronegative HREU individuals and the HIV-1–infected seroconvertors were done with Fisher's exact test or logistic regression by use of SAS. Cox proportional hazards regression and Kaplan-Meier survival statistics assessed rates of disease progression among seroconvertors. Regression and survival analyses were performed twice—once by using each SNP or haplotype alone and once by including three SNPs from CCL5 (RANTES) as covariates: RANTES-3′ 222, RANTES-In1.1 (rs2280789), and RANTES-(−403) (rs2107538).28McDermott DH Beecroft MJ Kleeberger CA Al-Sharif FM Ollier WE Zimmerman PA Boatin BA Leitman SF Detels R Hajeer AH Murphy PM Chemokine RANTES promoter polymorphism affects risk of both HIV infection and disease progression in the Multicenter AIDS Cohort Study.AIDS. 2000; 14: 2671-2678Crossref PubMed Scopus (182) Google Scholar, 29An P Nelson GW Wang L Donfield S Goedert JJ Phair J Vlahov D Buchbinder S Farrar WL Modi W O'Brien SJ Winkler CA Modulating influence on HIV/AIDS by interacting RANTES gene variants.Proc Natl Acad Sci USA. 2002; 99: 10002-10007Crossref PubMed Scopus (173) Google Scholar Participants were stratified by sex (results are shown for males only) and age at HIV-1 seroconversion: 40 years. The results stratified by age were included in the model and appear in table 3.Table 3Survival Analyses of Progression to Three AIDS End Points for CCL18, CCL3, and CCL4 SNPs and One HaplotypeAIDS-93AIDS-87DeathGroup and SNP or HaplotypeNERH (95% CI)PNERH (95% CI)PNERH (95% CI)PAAs in all cohorts (n=290):……………………… rs7120401241.01 (.60–1.52).9657.86 (.49–1.52).6331.37 (.61–.50).45 rs20150861251.09 (.75–1.57).6659.95 (.56–1.61).84321.12 (.55–2.28).76 rs20150701251.11 (.55–2.22).7759.79 (.28–2.23).6534.71 (.17–3.06).65 rs7120431251.18 (.72–1.94).5591.09 (.53–2.25).8134.75 (.26–2.15).59 rs8544721281.11 (.68–1.82).68611.03 (.50–2.12).9335.73 (.25–2.07).55 rs17192201301.26 (.75–2.11).39631.18 (.55–2.53).6836.98 (.34–2.81).97 rs1634481129.06 (.70–2.01).52631.10 (.51–2.38).836.92 (.32–2.65).88 rs1719127131.96 (.62–1.51).8762.84 (.43–1.62).6351.18 (.53–2.64).68 rs89511311.34 (.85–2.11).2611.26 (.66–2.43).48351.69 (.75–3.81).21 ss46566437132.97 (.58–1.61).962.77 (.36–1.65).535.77 (.27–2.19).62 rs17191301251.3 (.89–1.94).18571.27 (.73–2.20).4321.29 (.62–2.68).49 rs11303711311.34 (.91–1.94).14621.21 (.69–2.11).535.90 (.41–1.95).78 rs1719133132.91 (.52–1.59).74621.09 (.51–2.32).82351.16 (.45–3.02).76 rs17191341251.25 (.83–1.88).2956.96 (.51–1.82).9132.79 (.32–1.96).62 ss46566438129.90 (.53–1.53).6960.85 (.40–1.81).6735.80 (.28–2.28).68 ss46566439130.92 (.54–1.56).7560.65 (.28–1.52).3235.91 (.32–2.59).86 rs16344981291.02 (.62–1.69).93601.13 (.56–2.26).74341.29 (.53–3.16).58 rs16345071301.53 (1.01–2.34).05*601.33 (.71–2.50).3735.64 (.24–1.69).36 rs17191441291.52 (.91–2.52).11601.34 (.61–2.95).4734.66 (.19–2.31).52 rs17191461262.37 (1.11–5.06).03*592.72 (.99–7.49).05*341.39 (.30–6.37).67 rs17191531291.55 (.96–2.48).07621.60 (.81–3.13.1735.71 (.24–2.13).54 Haplotype [block 3-.071]1231.37 (.77–2.34).21601.19 (.55–2.47).3833.84 (.33–2.26).51 rs17191461042.38 (1.12–5.04).02*462.72 (.97–7.56).06222.17 (.47–9.97).32EAs in all cohorts (n=661):……………………… rs712040406.96 (.75–1.23).75293.95 (.71–1.28).74250.93 (.66–1.29).65 rs2015086413.96 (.76–1.21).72299.98 (.75–1.29).89256.97 (.72–1.31).85 rs2015070406.90 (.69–1.17).43293.80 (.58–1.12).19250.82 (.57–1.17).27 rs7120434111.09 (.89–1.32).42981.16 (.92–1.46).222551.08 (.84–1.39).55 rs8544724081.11 (.91–1.35).32941.21 (.96–1.53).112501.12 (.87–1.44).38 rs17192204101.04 (.85–1.27).712981.09 (.86–1.37).48253.97 (.75–1.24).79 rs16344813961.06 (.87–1.30).552861.14 (.90–1.44).29240.99 (.77–1.29).96 rs1719127aResults not reported for seven SNPs with near-zero MAFs in EAs.……………………… rs89514091.15 (.95–1.40).152981.30 (1.03–1.63).03*2621.06 (.83–1.36).64 ss46566437……………………… rs17191304001.24 (1.01–1.51).04*2891.39 (1.10–1.76).01*2501.25 (.97–1.60).09 rs11303714141.17 (.97–1.43).113021.33 (1.06–1.67).01*2631.15 (.90–1.47).26 rs1719133……………………… rs17191344181.18 (.98–1.44).093041.34 (1.07–1.68).01*2641.17 (.92–1.49).21 ss46566438……………………… ss46566439……………………… rs1634498……………………… rs16345074051.14 (.93–1.38).212971.23 (.98–1.54).082571.07 (.83–1.37).61 rs17191443921.20 (.98–1.47).072871.32 (1.05–1.67).02*2521.09 (.85–1.41).49 rs1719146……………………… rs17191534091.19 (.98–1.45).082971.33 (1.06–1.68).01*2531.20 (.94–1.54).15EAs in MACS cohort (n=403):……………………… rs7120432481.14 (.89–1.47).31831.38 (1.02–1.85).04*1591.18 (.86–1.62).31 rs8544722481.15 (.89–1.48).281811.41 (1.05–1.91).02*1561.20 (.87–1.65).27 rs16344812461.08 (.83–1.39).571791.27 (.94–1.71).121531.01 (.73–1.39).95 rs89512431.21 (.94–1.57).141821.43 (1.06–1.91).02*1621.12 (.82–1.53).46 rs17191302411.19 (.92–1.54).181791.40 (1.04–1.88).02*1581.22 (.89–1.68).21 rs11303712451.22 (.95–1.57).131841.41 (1.06–1.89).02*1621.15 (.85–1.57).37 rs17191342501.22 (.95–1.57).111871.42 (1.07–1.90).02*1641.19 (.87–1.62).28 rs16345072381.19 (.92–1.54).181801.32 (.99–1.78).061571.12 (.82–1.54).47 rs17191442281.19 (.92–1.55).191721.42 (1.05–1.91).02*1531.13 (.82–1.56).44 rs17191532471.22 (.95–1.57).121831.45 (1.08–1.94).01*1581.26 (.92–1.73).15Note.—End points were assessed using Cox proportional hazards regression under a dominant genetic model (results include CCL5 covariates listed in the "Material and Methods" section). n = number of seroconvertors; NE = number of events. P values ≤.05 are indicated by an asterisk (*).a Results not reported for seven SNPs with near-zero MAFs in EAs. Open table in a new tab Note.— End points were assessed using Cox proportional hazards regression under a dominant genetic model (results include CCL5 covariates listed in the "Material and Methods" section). n = number of seroconvertors; NE = number of events. P values ≤.05 are indicated by an asterisk (*). Twenty-one SNPs covering a 47,637-bp region containing CCL18, CCL3, and CCL4 (fig. 1) were genotyped in up to 3,158 participants. Three SNPs occurred within coding regions as follows: (1) ss46566437 at +1342G→T changes amino acid position 79 (E79D) in exon 3 of the CCL3 gene, (2) rs1130371 at +868C→T is a silent substitution (P60P) in exon 2 of the CCL3 gene, and (3) rs1719146 at +663C→T is a silent substitution (T39T) in exon 2 of the CCL4 gene. All other SNPs were noncoding and were found in promoter regions, introns, and intergenic areas (fig. 1 and table 1). Of the 21 SNPs, 7 had minor-allele frequencies (MAFs) 0.03 in AAs and were retained in analyses of AAs (table 1). Pairwise D′ values for AAs and EAs are shown (fig. 1A and 1B). In AAs, the three SNPs at both (5′ and 3′) ends of the region had much lower D′ values than did the 15 SNPs in the middle. Similarly, in EAs, the three SNPs at the 5′ end had lower D′ values than the remaining 11 SNPs. In addition, 16 (7.6%) of 211 pairwise r2 values exceeded 0.70 in AAs, whereas 30 (33%) of 91 pairwise r2 values exceeded 0.70 in EAs. These higher r2 values in EAs are reflected by the limited range in MAFs (0.218–0.304) for all but the three SNPs at the 5′ end, whereas MAFs in AAs showed a greater range across the entire region (0.032–0.497) (table 1). Haplotype-block analysis identified four blocks in AAs, which were <1 kb, 1 kb, 3 kb, and 7 kb in size, and three blocks in EAs, which were 6 kb, <1 kb, and 28 kb in size (fig. 1C and 1D). Diversity within each block was limited; only block 3 in AAs had more than two common haplotypes with frequencies exceeding 5%. In addition, multiallelic D′ values between blocks ranged from 0.81 to 0.93, indicating extensive disequilibrium between blocks in both AAs and EAs. Although there are recombinant haplotypes in AA blocks 3 and 4 and in EA

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