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Basic and Translational Science

Polymorphisms of the Kappa Opioid Receptor and Prodynorphin Genes

HIV Risk and HIV Natural History

Proudnikov, Dmitri PhD*; Randesi, Matthew BS*; Levran, Orna PhD*; Yuferov, Vadim PhD*; Crystal, Howard MD; Ho, Ann PhD*; Ott, Jurg PhD‡,§; Kreek, Mary J. MD*

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: May 1, 2013 - Volume 63 - Issue 1 - p 17-26
doi: 10.1097/QAI.0b013e318285cd0c



Opioids and opioid receptors have been implicated in aspects of the immune response to HIV infection, in part through modulation of expression of cytokines and chemokines and their receptors.1,2 Several polymorphisms in specific genes were found to be involved in mechanisms of HIV transmission and pathophysiology of HIV infection, including variants of CC chemokine receptors CCR5 and CCR2 that are directly involved in the mechanisms of HIV-1 penetration into the target cell3,4 and polymorphisms of the human leukocyte antigen cluster associated with different rates of HIV progression.5–8 We hypothesize that the gene polymorphisms of the opioid receptors that have been shown to be involved in neurophysiological functions and their ligands may also influence the pathophysiology of HIV. HIV has been a focus of our studies since the early 1980s.9–12

Opioid receptor genes have been implicated in the general regulation of specific aspects of immune response. Serum concentrations of the proinflammatory cytokines interleukin (IL) 6, tumor necrosis factor-alpha (TNF-alpha), and interferon-gamma were found to be significantly lower, and the general health score was significantly higher in subjects bearing the allele G of the functional mu opioid receptor (OPRM1) variant 118A>G, suggesting that carriers of the G variant may suppress proinflammatory cytokine secretion from peripheral immune cells.13

Heterologous cross-deactivation of opioid and chemokine receptors has been shown in both in vivo and in vitro experiments.1,14–18 Activation of one of the G protein–coupled receptors results in phosphorylation of the C-terminus of another G protein–coupled receptor by protein kinases A and C, leading to decoupling of the latter receptor from G proteins and loss of sensitivity to stimulation.19,20 Activation of the OPRM1 and the delta opioid receptors (OPRD1) induce deactivation of CCR1, CCR2, CCR5, CXCR1, and CXCR2, but not CXCR4. In turn, ligands of CCR1, CCR2, CCR5, CCR7, and CX3CR1 deactivate OPRM1 and OPRD1.16–18 Beta-endorphin, an endogenous agonist for both OPRM1 and OPRD1 with higher affinity for OPRM1, significantly enhances IL-2 production in IL-1–stimulated EL-4 and LBRM33-1A5 lymphoid cell lines; naloxone completely abolished this enhancing effect.21 In contrast, the kappa opioid receptor (OPRK1)-selective synthetic full agonist U50 488H (trans-3,4-dichloro-N-methyl-N[2-(1-pyrolidinyl)cyclohexyl]benzeneaceamide methanesulfonate) inhibits the expression of the proinflammatory cytokines IL-1, IL-2, IL-6, and TNF-alpha.1 U50 488H inhibits the expression of CXCR4 on human CD4+ T cells, resulting in a reduction of viral entry.22,23 Treatment of microglial cell culture with U50 488H results in dose-dependent inhibition of expression of the monocytotropic HIV-1 SF162 strain.24

In this study, we looked for an association of 17 selected variants of OPRK1 and 11 variants of prodynorphin (PDYN) with HIV status in subjects from the Women's Interagency HIV Study (WIHS) sites.25–28 Next, we tested for the influence of specific genotypes of OPRK1 or PDYN on the change of viral load (VL) and CD4 count across 2 periods: between admission to WIHS and the start of highly active antiretroviral therapy (HAART) and also between the start of HAART and the most recent WIHS visit for which data were available. Our findings indicate potential involvement of the specific variants of OPRK1 and PDYN in the pathophysiology of HIV infection.


Study Subjects: Recruitment and Diagnostic Procedures

African American, White, and Hispanic unrelated women were recruited by the WIHS. As of 2002, 3766 subjects were recruited, primarily in 2 cohorts—first, in 1994–2000 (the majority of cohort 1 was recruited in 1995–1996) and second in 2001–2002 (available at:; accessed on June 15, 2012). In our study, only subjects who signed an extended updated informed consent to participate (from April 1, 2006, to September 30, 2007, 1506 subjects in total) were included, thus excluding those who died before 2006. Subjects completed our Family Origin Questionnaire on 3 generations. Limited clinical information on the subjects was provided by WIHS, including HIV status, VL, and CD4 counts. During each clinic visit (every 6 months), subjects provided specimens and were physically examined and interviewed on history of illnesses, substance abuse, current medications, and medication adherence.

We excluded 497 subjects for different reasons (Table 1): mixed ethnicity (421 subjects), seroconversion after admission to WIHS (6 subjects), subjects whose DNA concentration in specimen provided was insufficient (45 subjects), and subjects whose CD4 count was below 10 cells per milliliter (22 subjects), thus leaving 1009 subjects for analysis including 682 HIV+ subjects and 327 healthy controls. This cohort overlaps by 98% of the cohort examined in our association study of OPRM1 variants with response to HIV treatment.29 The subset of these HIV+ subjects who had complete data across 3 study points R, S, and T (R, entrance to WIHS study, S, initiation of HAART, and T, most recent WIHS visit for which clinical data were available) was used for comparison of change of VL and CD4 count at study intervals X (from entry to WIHS to start of HAART) and Y (start of treatment until most recent visit, Table 2). A different subset of HIV+ subjects who had complete data across 2 of 3 study points (R to S or S to T) was used for analysis of the influence of genotype on change of VL or CD4 count across intervals X or Y. Study point S was chosen to be in the range from 6 months before start of HAART up to 1 month after initiation of HAART. Lengths of the intervals X and Y varied widely among individuals, and this was taken into account in the regression analysis (see Statistical Analysis below).

Demography of the Subjects Involved in This Study and Reasons for Exclusions
Definition of Study Points (Study Design)

OPRK1 and PDYN Genotyping

The structures of OPRK1 and PDYN are shown in Figures 1A–H. Genetic markers were chosen based on the reported genotype frequency (>0.05 in African Americans or Whites, HapMap data), location in the gene (preference was given to variants from the coding region and promoter), and previous reports on association with various medical conditions. Some of the variants chosen for this study had been reported (HapMap) to be in linkage disequilibrium (LD) in 2, but not all 3, ethnic groups of our study.

Structure of OPRK1 and PDYN genes. A, Location of the polymorphisms of the OPRK1 gene selected for these studies in chromosome 8; B, messenger RNA (mRNA) of the OPRK1 gene; C, protein structure of the OPRK1 gene; D, the list of the polymorphisms in the OPRK1 gene used in this study; E, location of the polymorphisms of the PDYN gene selected for these studies in chromosome 20; F, mRNA of the PDYN gene; G, protein structure of the PDYN gene; H, the list of the polymorphisms in the PDYN gene used in this study; TM1–7, transmembrane domains; 5′-UTR and 3′-UTR, untranslated regions; VEON, vertebrate endogenous opioid neuropeptide (21–68 amino acids); BN, beta neoendorphin (175–183 amino acids); DYN, dynorphin (207–223 amino acids); LEUM, leumorphin (226–254 amino acids); RIM, rimorphin (226–238 amino acids); LEU-ENK, leu-enkephalin (226–230 amino acids). Nucleotide positions in the gene are assigned using a start point (+1) translation initiation site ATG.

DNA from peripheral blood mononuclear cell pellets obtained from the WIHS repository was isolated using the PureGene DNA purification kit (Gentra Systems, Minneapolis, MN) and treated with RNase A, yielding about 3.5 μg per subject. For genotyping, in each well of a 384 optical plate (ABI; Applied Biosystems, Foster City, CA), 1 µL of DNA (3 ng/µL) was mixed with 4 µL of the solution containing Universal PCR Mastermix (ABI), water, and custom-synthesized oligonucleotide primers and probes specific for each polymorphism (ABI; see Table S1, Supplemental Digital Content, Polymerase chain reaction cycling was performed according to an ABI protocol. Genotype analysis was done on the ABI Prism 7900 sequence detection system using SDS 2.2 software (ABI). For each polymorphism, the results of TaqMan genotyping were confirmed by Sanger sequencing of 22 randomly selected samples using the same primers.

Genotyping of the PDYN 68-base pair (bp) repeat (rs35286251) was done by amplification of the area surrounding this variant in 2 replicates: first, using 5′-FAM–labeled forward and unlabeled reverse primers and then using labeled reverse and unlabeled forward primers (sequences are in Table S1, Supplemental Digital Content, The amplification was performed using a step-down protocol; fragments were analyzed using an ABI 3730xl instrument and GeneMapper 4.0 software (ABI) as described earlier.30 For calibration and confirmation, 20 homozygous samples were sequenced by the Sanger method.

Statistical Analysis

As the CD4 counts and VL were strongly skewed (long right hand tail), they were natural log transformed for analysis. For each variant, Hardy–Weinberg equilibrium (HWE) tests were determined in HIV− controls with likelihood ratio χ2, separately for each ethnicity (84 tests). Differences in genotype frequencies among ethnicities were determined using χ2 tests in controls.

To assess the change in VL and CD4 counts before and after initiation of HAART, analysis of variance with repeated measures was used for each ethnicity in the subset of HIV+ subjects with values at each of 3 study points: R, S, and T (Table 2).

All but one (rs35286251) of the genetic markers were single nucleotide polymorphisms (SNPs). For these markers, genotypes AA, AB, and BB were coded 0, 1, and 2, respectively. This coding scheme is proportional to the number of B alleles in an individual and will reflect the allelic effect of an SNP, based on its genotype. Thus, working with these codes is not affected by deviations from HWE. For the 68-bp repeat for each individual, the 2 alleles (numbers of repeats) were added together to form this individual's genotype. In a second approach for the 68-bp repeat, allele lengths 1 and 2 repeats were considered S (short), allele lengths greater than 2 repeats were considered L (long), and genotypes were assigned as follows: SS = 1, SL = 2, and LL = 3 as described.31,32 All analyses were carried out separately for each of the 3 ethnicities and each marker.

Initially, a standard case–control association analysis was performed to see whether any one of the markers was associated with HIV status. For each marker, logistic regression was carried out with case/control as the dependent variable and marker genotype code as the independent (predictor) variable. Thus, results for each marker were associated with 1 degree of freedom.

For the second type of analysis, the aim was to see whether the rate of increase or decrease of VL or CD4 count over intervals X and Y was associated with a marker genotype. This analysis (guided by J.O.) involved HIV+ subjects only. For each marker, each ethnicity, and each of the 2 intervals X and Y, a multiple regression analysis was carried out with natural log-transformed VL as the dependent variable and X (or Y) and marker genotype as independent (predictor) variables. Thus, in the analysis for a given SNP, 2 predictor variables, X (or Y), allowed for different lengths of these time intervals, whereas genotype measured the potential effect of the given SNP on VL (or CD4 count) for constant time interval length. In these analyses, the statistical software package (SYSTAT, identified some individuals as being outliers or having large leverage (impact), that is, exerting an unusually large effect on the result. Whenever the resulting P value for effect of genotype was 0.10 or less, analyses were repeated with these individuals excluded. If necessary, this procedure was repeated (when new "large leverage" individuals were identified), so that all results with P <0.10 were statistically reliable in the sense that no individuals identified as outliers or having large leverage contributed to the final result.

Analysis of the LD of the variants studied was performed separately for each ethnicity using Haploview 4.2 (, accessed on March 15, 2011).


Analysis of Overall Change of VL and CD4 Counts at Different Study Points (Data Set 1, Table 3)

When examining overall changes of the VL and CD4 count, we used only those subjects who had clinical measurements at all 3 study points. We found a marked decline of the VL from admission to WIHS until the start of HAART (interval X) in all 3 ethnic groups (P < 0.0001; see Figure S1, Supplemental Digital Content,, probably because of azidothymidine therapy. Administration of azidothymidine alone or in combination with other modified nucleotides has been shown to reduce VL by 50%–90%.33–35 A significant decline in VL from start of HAART until the most recent visit (interval Y) was found in African Americans and Whites (P < 0.0001), but not in Hispanics. A significant increase of CD4 count after initiation of HAART was found in African Americans only (P < 0.0001). However, the number of subjects with CD4 less than 500 at the last WIHS visit was considerably reduced in all ethnicities compared with this number at entry to WIHS or initiation of HAART.

Demography of HIV+ Subjects Used for Clinical Data Analysis of VL or CD4 Count Change When Data on All 3 Study Points (R, S, and T) Were Available

Ethnic Differences in Genotype Frequencies, HWE Test, Exclusion of Low-Frequency Variants

Allele and genotype frequencies of the polymorphisms studied are shown in Table S2 (see Supplemental Digital Content, In the control group, allele frequencies were significantly different among the 3 ethnicities for 25 out of 28 genetic markers studied (see Table S2, Supplemental Digital Content,, with the exception of OPRK1 variants −298G>A (rs16918955) and coding 36G>T (rs1051660, Pro12Pro) and PDYN 1421T>C (rs10485703). Therefore, all tests were performed separately in each ethnicity. Polymorphisms with frequency less than 0.05 in both case and control groups were excluded from analysis (see Table S2, Supplemental Digital Content,, highlighted in yellow). This includes variants of OPRK1 −72C>T (rs9282806) in Hispanics, and 459C>T (Ser153Ser, rs7815824) and IVS3+3773C>G (rs16918884) in Whites. A significant deviation from HWE in controls was found for the variants of OPRK1 IVS2+7886A>G (rs12548098) and IVS3+3773C>G (rs16918884) in African Americans (P < 0.0001). Raw data for these polymorphisms were reviewed and reevaluated. No technical problems were found.

Multiple Regression Analysis of Association of Genotypes of OPRK1 and PDYN With VL and CD4 Count Change Before Initiation of HAART (Data Set 2, Table 4)

A total of 324 multiple regression analyses were carried out [3 ethnicities × 2 outcomes (VL, CD4) × 2 intervals (X, Y) × 28 markers] after exclusion of 12 tests (see above). Of these 324 analyses, 225 identified individuals with large leverage, ranging between 1 (114 tests) and 8 (1 test). Often it was the same individual who showed large leverage for different markers. The average number of individuals with large leverage among these 225 tests was 1.9.

Demography of the HIV+ Subjects Used for Statistical Genetics (Logistic Regression) Analysis of Association of the OPRK1 and PDYN Polymorphisms With Change of VL or CD4 Count When Data on 2 of 3 (R and S or S and T) Study Points Were Available

Using multiple regression analysis in the group of HIV+ subjects only, before HAART, we found a significant effect of the number of T alleles of IVS3+189C>T (rs6035222) in PDYN on the VL change (greater decline, P = 0.0014, Fig. 2A, Table 5) in African Americans. No variants of OPRK1 showed an effect on VL change before HAART.

Influence of the genotypes of OPRK1 and PDYN variants on change of ln of VL from admission to WIHS to initiation of HAART (A) and from initiation of HAART to the most recent visit (B–D). The left y axis shows ln values of VL (used in statistical analysis); the right y axis shows the actual (untransformed) values of VL. Clinical improvements were found in carriers of the minor allele of the PDYN IVS3+189C>T in African Americans and OPRK1 IVS2+2225G>A in Whites. Clinical deterioration was found in carriers of the minor allele of OPRK1 IVS2+10658G>T and IVS2+10963A>G in Whites.
Significant Results of an Association of the OPRK1 and the PDYN Variants With VL and CD4 Count Change and With HIV Status; Regression Analysis of the Genotypes of OPRK1 and PDYN in HIV+ Subjects Only With VL and CD4 Count Change Over X* (Data Set 2, Table 4)

We found a significant effect of the number of A alleles of the −1205G>A (rs3808627) in OPRK1 on CD4 count change (greater decline, P = 0.0224, Fig. 3A, Table 5) before HAART in Whites. Subjects bearing genotype AG of IVS2+7886A>G (rs12548098) in OPRK1 showed a lesser decline of CD4 count before HAART also in Whites (P = 0.0095, Fig. 3C). No subjects with the IVS2+7886GG genotype were observed in Whites. Subjects bearing genotype CT of −72C>T (rs9282806) in OPRK1 showed a lesser decline of CD4 count before HAART in African Americans (P = 0.0404, Fig. 3B). Four outliers were removed in the analysis of −72C>T in African Americans, including the only individual bearing the −72TT genotype; 2 outliers were removed in analysis of the influence of −1205G>A and IVS2+7886A>G on CD4 count change in Whites. No variants of PDYN showed an effect on CD4 count change before HAART.

Influence of the genotypes of the OPRK1 variants on change of CD4 count from admission to WIHS to initiation of HAART (A–C) and from initiation of HAART to the most recent visit (D). The left y axis shows ln CD4 count (used in statistical analysis); the right y axis shows the actual (untransformed) values of CD4 count. Clinical improvements were found in carriers of the minor allele of the OPRK1 −72C>T and IVS2+7886A>G in Whites. Clinical deterioration was found in carriers of the minor allele of OPRK1 −1205G>A in Whites and OPRK1 IVS2+2225G>A in Hispanics.

Multiple Regression Analysis of Association of Genotypes of OPRK1 and PDYN With VL and CD4 Count Change After Initiation of HAART (Data Set 2, Table 4)

After initiation of HAART, a significant effect of the number of A alleles of the IVS2+2225G>A (rs6985606) of the OPRK1 on VL change (greater decline) was found in Whites (P = 0.0033, Fig. 2B, Table 6). Also in Whites, a lesser decline of VL was found in individuals bearing genotypes TT and GT compared with GG genotype of IVS2+10658G>T (rs1365098, P = 0.0376, Fig. 2C) and in individuals bearing genotypes GG and AG compared with AA genotype of IVS2+10963A>G (rs997917, P = 0.0376, Fig. 2D). Genotypes of the polymorphism IVS2+10658G>T completely predict genotypes of IVS2+10963A>G.

Significant Results of an Association of the OPRK1 and the PDYN Variants With VL and CD4 Count Change and With HIV Status; Regression Analysis of the Genotypes of OPRK1 and PDYN in HIV+ Subjects Only With VL and CD4 Count Change Over Y* (Data Set 2, Table 4)

In Hispanics, individuals with genotypes AA and GA of IVS2+2225G>A showed a lesser increase of CD4 count after initiation of HAART (P = 0.0401, Fig. 3D, Table 6). Two individuals were removed from this analysis as outliers. No variants of PDYN showed an effect on VL or CD4 count change after initiation of HAART.

Logistic Regression Analysis of OPRK1 and PDYN Variants and HIV Status (Included Data Are Shown in Table 1)

Using logistic regression, in all controls and cases, we found that in African Americans, frequencies of the minor allele A of the OPRK1 promoter variant −1205G>A (rs3808627) and allele A of the possible PDYN promoter variant −11128G>A (rs1997794) are lower in the HIV+ group than in controls, indicating possible protection from infection (Table 7).

Significant Results of an Association of the OPRK1 and the PDYN Variants With VL and CD4 Count Change and With HIV Status; Logistic Regression Analysis of the Polymorphisms of the OPRK1 and PDYN and HIV Status (Data Set From Table 1)

In this initial study, all findings in multiple regression analyses or logistic regression analyses were only pointwise significant and did not withstand multiple test correction. Further studies are needed to validate these genetic findings and the functionality of the variants.

Analysis of LD

LD analysis was performed in the group of HIV− controls only, separately for each ethnic group (see Figure S2, Supplemental Digital Content, In the OPRK1 gene, very high LD (r2 > 0.95) was found for the variant pair rs997917/rs1365098 in all 3 ethnicities; pairs rs3802281/rs3802282 and rs963549/rs702764 in Whites and African Americans; pair rs16918875/rs16918955 in Whites; and pair rs3802281/rs963549 in African Americans and Hispanics. Several pairs of OPRK1 variants, including rs3802282/rs702764, rs3802281/rs702764, and rs3802282/rs963549, were found to be in high LD (r2 > 0.75) in all 3 ethnicities.

In the PDYN gene, in all 3 ethnicities, very high LD (r2 > 0.95) was observed among variants rs2235749, rs910079, and rs910080 and also between rs10854244 and rs6045935.


Before the initiation of HAART, we found a significant effect of the number of T alleles of intronic PDYN IVS3+189C>T on the slope of VL decline in African Americans. This polymorphism has been found to be in association with alcohol dependence.36 No other variants of PDYN were found to have a significant effect on either VL or CD4 count change before or after HAART. However, a promoter allele PDYN −11128A (rs1997794) was found to be in association with HIV status (protection) also in African Americans. This variant might be involved in the regulation of promoter activity and might influence gene expression through formation of a noncanonical activator protein-1 binding site.37 This variant has been found to be in association with opioid dependence in Chinese women38 and with alcohol dependence in Whites.36 In tests of cognitive function, carriers of the minor allele of this variant and also variant 1508T>C (rs910080) had higher episodic memory scores than homozygote carriers of the major allele.39 Decline of cognitive function is one of the challenges of HIV+ patients. We did not find this polymorphism 1508T>C to be in association with either VL or CD4 count change in our study.

The 68-bp PDYN promoter repeat (rs35286251) is involved in the regulation of gene expression.40–42 Three or more copies of this variant (or "long" allele vs. 1 or 2 copies or "short" allele) have been found to be associated with opioid dependence in Chinese43 and with cocaine dependence and cocaine/alcohol codependence in African Americans.31,32 Epidemiologic studies have shown that drug abuse may exacerbate HIV disease progression.44 However, we did not find an effect of this polymorphism on change of VL or CD4 count either before or after HAART, using the long–short allele paradigm or using the sum of repeats from 2 alleles in each individual.

Some of the OPRK1 variants that were found to interact with VL or CD4 count change in this study have been found to be in association with alcohol dependence in Whites,36 including IVS2+2225G>A (rs6985606), IVS2+7886A>G (rs12548098), and IVS2+10963A>G (rs997917). Other studies indicate an association of haplotype GGCTTCT of the OPRK1, consisting of −3731C>T (rs12675595), 36G>T, IVS2+2225G>A, IVS2+10963A>G, 843A>G, 1176G>A, and 4139A>G (rs7820807), with alcohol dependence also in Whites.45

We found an association of the OPRK1 −1205G>A of the possible promoter region with HIV status in African Americans. Studies have shown that the OPRK1 is potentially involved in the modulation of stress responsivity and depression.46,47 Stress, depressed mood, and dysthymic disorders have been shown to be associated with unsafe sex practices48 and, therefore, HIV infection. As a result of self-medication for depression, abuse of drugs, in turn, may lead to infection with HIV through sharing of needles or by using unsafe sex practices under the influence of drugs.9–12

No association of the synonymous OPRK1 36G>T variant with HIV status, or change of VL or CD4 count, was found in our study. This variant has been previously found to be in association with vulnerability to develop opiate addiction.49,50

Previous studies indicate possible involvement of OPRK1 in activation or inhibition of proinflammatory cytokines IL-1, IL-2, IL-6, and TNF-alpha that are involved in inflammatory response to HIV infection1 and also in possible regulation of expression of CXCR4 responsible for HIV entry into cells.22,23OPRK1 polymorphisms analyzed in this study may potentially alter either expression of the OPRK1 gene or its messenger RNA splicing, thus altering cytokine activation or CXCR4 expression, which may result in observed alteration of VL or CD4 change.

Polymorphisms of the OPRK1 gene have also been studied for association with other medical conditions, including nausea and vomiting in cancer patients receiving opioids and pressure pain sensitivity in women, but no significant findings were made. Contradictory results were found in association of PDYN gene variants with temporal lobe epilepsy and schizophrenia; no association of PDYN polymorphisms with atopic dermatitis was found.

The large number of African Americans in this study allows interpreting associations found in this ethnicity with greater confidence. A limitation of this study is a relatively small number of Hispanic and White subjects.

Our findings may be considered evidence of the involvement of OPRK1 and PDYN variants and also the OPRK1 and PDYN systems in response to HIV treatment.


The authors thank Susan Russo for editorial assistance. The authors acknowledge the National Institutes of Health–National Institute of Allergy and Infectious Diseases, which supported the WIHS and principal investigators of these studies (see the detailed acknowledgements provided below). The authors also acknowledge Susan Holman for facilitation and Judith Cook for help in our studies. The authors thank the women participating in the WIHS for their time, cooperation, and support. Data in the manuscript were collected by the WIHS Collaborative Study Group with centers (Principal Investigators) at New York City/Bronx Consortium (Kathryn Anastos); Brooklyn, NY (Howard Minkoff); Washington, DC, Metropolitan Consortium (Mary Young); The Connie Wofsy Study Consortium of Northern California (Ruth Greenblatt); Los Angeles County/Southern California Consortium (Alexandra Levine); Chicago Consortium (Mardge Cohen); and Data Coordinating Center (Stephen Gange).


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polymorphism; SNP; viral load; CD4 count; WIHS; OPRK1; PDYN

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