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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Trends Genet. Author manuscript; available in PMC Oct 8, 2013.
Published in final edited form as:
PMCID: PMC3792714

Host genes associated with HIV/AIDS: advances in gene discovery


Twenty five years after the discovery of HIV as the cause of AIDS, there is still no effective vaccine and no cure for this disease. HIV susceptibility shows a substantial degree of individual heterogeneity, much of which can be conferred by host genetic variation. In an effort to discover host factors required for HIV replication, identify critical pathogenic pathways, and reveal the full armament of host defenses, there has been a shift from candidate gene studies to unbiased genome wide genetic and functional studies. However, the number of securely identified host factors involved in HIV disease remains small, explaining only ~15–20% of the observed heterogeneity – most of which is attributable to HLA. Multidisciplinary approaches integrating genetic epidemiology to systems biology will be required to fully understand viral-host interactions to effectively combat HIV/AIDS.

Combating HIV/AIDS through host genetics

Identification of genes that affect susceptibility and resistance to HIV-1 is key to unravelling HIV-host interactions for drug and vaccine development. In 1996, a 32-base-pair deletion (CCR5 Δ32) was discovered that provided near complete protection against HIV infection in homozygotes [1, 2]. This was the first convincing evidence that transmitted strains of HIV preferentially use the CCR5 co-receptor for cell entry and led to the development of new class of anti-HIV drugs inhibiting entry of HIV into the cell [1]. Recently, an HIV-1-positive patient was cured after the transplantation of CCR5 Δ32/Δ32 stem cells [3]. This isolated case illustrates the potential of translational genetic research to combat HIV/AIDS. Unlike HIV which can develop resistance through escape mutations, host targets are genetically stable. The search for host genetic restriction factors has intensified and expanded as a practical cure for HIV infection or an effective vaccine has failed to materialize 25 years after the discovery of HIV.

Here we review the insights gained into genetic variation in the host genome from a decade of candidate gene analysis and also from recent advances based on high throughput genome-wide scans utilizing single nucleotide polymorphism (SNP) arrays and small interfering RNA (siRNA) approaches.

Epidemiologic evidence of genetic restriction of HIV/AIDS

There is considerable heterogeneity in viral control and progression rates (Figure 1), which not fully explained by environmental or viral factors. A subset of exceptional individuals deviate from the expected response to HIV exposure: (i) Exposed uninfected (EU) individuals show resistance to HIV acquisition even after multiple, high risk exposures (reviewed in [1, 4]); (ii) Long- term non-progressors (LTNP) maintain stable CD4 levels and low viral load (VL) for ten or more years; (iii) Fast progressors who cannot control viremia and develop AIDS within three years of infection [1]; and (iv) Elite controllers (EC), representing just 1% of HIV-infected persons, control HIV replication to <50 copies/mL [5]. Extensive studies have found that many EC are infected with replication-competent virus [6], pointing to a differences in host genetic background in HIV control. People with extreme phenotypes are highly informative in genetic association studies because they can be enriched for risk or protective alleles (Figure 2 and Box 1).

Box 1

Approaches to identify host genetic factors affecting HIV-1 acquisition and progression

  1. HIV-1 acquisition and transmission: The identification of host genetic factors influencing HIV infection is confounding by non-genetic factors such as route of transmission, risk behavior, concurrent infection with sexually transmitted diseases, and infectiousness (viral load) of the transmitting partner. This has hampered the identification of genetic factors predicting transmission and acquisition. (Figure 2):
  2. Disease progression (Figure 1): With a seroconverter (i.e., known time of HIV infection) cohort, the rate of progression to specific AIDS endpoints can be assessed with Kaplan-Meier survival analysis and Cox proportional hazards models using time to CD4<200 or <350, AIDS-defining condition or death as endpoints. When the date of infection is unknown, KM survival and Cox models are not robust. General linear or mixed effects models can be used to assess the effects of genotype on CD4+ T cell trajectory if longitudinal data is available.
  3. Control of HIV replication: Viral set point during the asymptomatic phase is a predictor of HIV progression and a commonly used outcome variable (Figure 1). Other studies have used the general linear or mixed effects models to assess the HIV RNA trajectory.
  4. Extreme phenotypes: Extremes of the distribution in terms of AIDS-free survival, CD4+ cell, or control of viremia can be enriched for genetic variation associated with the extreme phenotype and require fewer individuals for gene detection. However, caution is required in comparing the extreme tails of the distribution to each other since, for example, factors promoting rapid progression can be quite different than those involved associated with long term AIDS-free survival (analogous to comparing infant mortality to centenarians). Different design strategies are to compare the genotype distribution in the extreme group to a normal population or to a middle group showing median or average progression (Table S1 online).
  5. The major cause of failure to replicate is that the original association was a false positive or had a smaller than originally reported effect size—the winner’s curse.
  6. Candidate gene studies were used to identify CCR5, chemokine ligands, HLA, and KIRS in HIV/AIDS, though they are known for high rate of false positives. Replication in independent, well-powered populations is the gold standard of validation. On the other hand, the curse of GWAS is high rate of false negatives. The stringent threshold for statistical significance (5 or 1 ×10−8) required to correct for multiple comparisons in GWAS avoid false positive associations, but comes at the expense of false negatives. Separating noise from true signals in genome-wide studies will continue to be a challenge, but hopefully this barrier will be breached through international collaborations to increase power.

The term winner’s curse is also used in a related but distinct sense in science, in particular genome-wide association studies. In studies involving many tests on one sample of the full population, the consequent stringent standards for significance make it likely that the first person to report a significant test (the winner) will also report an effect size much larger than is likely to be seen in subsequent replication studies.

Figure 1
A typical natural course of HIV infection. (i) Individual infected with HIV-1; (ii) Acute phase lasting for 6-12 weeks with flu-like symptoms, peak viral load and drop in CD4+ T cells; (iii) Chronic asymptomatic phase last on average for 7-10 years; following ...
Figure 2
Study groups and phenotypes in HIV-1 genetic association analysis. Case-control and cohort studies are used to evaluate genetic impact on HIV-1 infection or disease progression by assessing an AIDS-predicting phenotype (setpoint viral load), time from ...

Study groups for HIV host genetic investigation

HIV-1 natural history cohorts established during 1980s enrolled subjects from the major HIV risk groups at the time: intravenous drug users, persons with hemophilia, and men who have sex with men (MSM) (Figure 2, Table S1 in the online supplementary material). Owing to the early demographics of HIV infection in the USA and Europe, these studies are over-represented by men of European descent infected with subtype B virus—not at all representative of the current global epidemic where more than half the HIV infections occur in sub-Saharan Africa with HIV subtype C. However, these studies remain of value because they are prospective and represent the natural history of HIV infection before the advent of effective HIV therapy and include HIV seroconverters (with dates of last negative and first positive HIV test). Table S1 lists the major HIV studies that have been used in genetic association studies.

Host genes associated with HIV/AIDS

Using a combination of candidate gene analysis (CGA) and unbiased genomewide association scans (GWAS), genetic variation has been identified that explain in part the observed variation in HIV-1/AIDS. These genes mainly fall into three classes: innate and adaptive immunity; HIV dependency factors (HDF) required by HIV for replication; and intrinsic anti-viral restriction factors. Genes identified by CGA are listed in Table 1.

Table 1
Published host genetic factors of HIV/AIDS

The palace guards: HLA and KIR

HLA class I alleles were the first host genetic factors identified to affect AIDS. HLA class I genes encode cell surface molecules that differentially present viral antigenic epitopes to CD8+ T cytotoxic lymphocytes (CTL). Specific HLA Class I alleles, by differential HIV epitope binding, influence the effectiveness of immune response thus affecting HIV progression (reviewed in Refs [7, 8]). HLA homozygosity for 1, 2, or 3 class I loci were positively correlated to disease progression with individuals homozygous at HLA-A, -B, and -C showing the shortest AIDS-free survival [9]. The HLA-B*35 Px allele is dominantly associated with more rapid progression to AIDS, presumably because of a limited repertoire of HIV epitope recognition [10]. However, the HLA-B*57 and B*27 alleles have been consistently associated with delayed disease progression [1, 8]. Studies of the effects of HLA-B alleles show that carriage of a protective HLA-B allele by either the child or the mother was associated with delayed progression to AIDS [11]; however, sharing of HLA alleles between the mother and child or maternal HLA class I homozygosity increased the risk of mother-to-child HIV transmission [11, 12].

Killing of infected cells by CD8+ CTL, following CTL recognition of HIV epitopes presented by HLA Class I receptors, is essential for immune control of HIV; mutation of the presented epitopes allows HIV to escape this control. Plausibly, HLA Class I alleles are susceptible or protective to the extent that they make this immune escape easier or more difficult. HLA Class I homozygosity decreases the number of HLA alleles, decreasing the number of epitopes presented and the extent of mutation needed to escape antigen presentation. However, HLA-B*27 is notably nonselective in antigen presentation, making it harder for HIV epitopes to lose binding affinity by mutation. HLA-B*57 is protective because the viral mutation required for the primary epitope to escape significantly decreases viral fitness. The specific mutational processes involved in HIV escape from HLA-B*27 and HLA-B*57 antigen presentation have been observed in vivo [13]. HLA-B*5701 is also associated with severe hypersensitivity to abacavIr, a protease inhibitor used in antiretroviral therapy. Genetic testing for HLA-B*57 is required before prescribing Abacavir [14].

The intersection between innate and acquired immunity is manifested by interactive effects of HLA and the killer immunoglobulinlike receptors (KIR) on HIV disease; this complex subject has been reviewed by Carrington et al.[7]. An adaptive strategy shown by HIV and other viruses is to downregulate HLA Class I expression, thus blocking presentation of viral epitopes and CTL killing of the infected cells. One function of natural killer (NK) cells is to counter this strategy by eliminating cells which fail to display correct levels of Class I receptors, and one function of the NK cells’ KIR is to determine whether the potential target cells carry the proper set of HLA receptors. In a study of ~1000 HIV seroconverters the activating KIR3DS1 receptor increased susceptibility to AIDS progression in the absence of its presumed ligand, a subset of the HLA-Bw4 group of alleles, but was strongly protective in the presence of one or more of these HLA-B alleles [7, 8].

Modulation of the immune response to HIV-1 infection is critical in controlling HIV replication and in HIV-1 pathogenesis. A gene expression study in HIV-uninfected and -infected patients showed similar profiles in expression levels from CD8+ cells from both uninfected persons and HIV-infected individuals with control of viremia, but a distinctly different profile in early infection that persisted in chronic progressors, characterized by up-regulation of interferon-inducible genes [15]. Polymorphism in regulatory and coding regions of immune regulatory genes have been associated HIV acquisition and HIV progression (e.g. interferon regulatory factor 1 (IRF-1) [16], IL10 [1], interferon gamma (IFNG)[1], IL8 receptor (CXCR1)[17]. Linking transcriptional profiles to underlying genetic variation might identify key players in determining normal from aberrant interferon responses following HIV infection.

Keepers of the gate: the chemokine-receptor nexus and HIV-cell entry

The chemokine receptors and their ligands have been extensively investigated since its discovery in 1996 as having a key role in HIV cell entry. HIV-1 enters cells by binding to CD4 and one of two major cell surface coreceptors: CCR5 used by transmitted forms of HIV-1 strains (R5) or CXCR4 used by X4 strains that arise in about 50% of individuals late during infection (Figure 3). The chemokines CCL5 (RANTES), CCL3 (Mip1α) and CCL4 (Mip1β) are ligands of CCR5 and competitively inhibit R5 HIV cell entry. A 32 base pair deletion (CCR5 Δ32) results in a truncated CCR5 protein that is not expressed on the cell surface [2, 18]. Only 1% of northern Europeans carry the CCR5 32Δ homozygous genotype—these individuals enjoy near complete protection against HIV infection [18]. Although CCR5 Δ32 heterozygosity does not prevent infection, it does slow progression in both untreated [2] and treated—this protective effect is particularly strong in persons on antiretrovirial therapy (ART) [19]. Genetic variation in the promoter region and infrequent nonsynonymous mutations in CCR5 also modify progression rates [20-22]. The CCR5 Δ32 to date is the only genetic mutation that completely blocks HIV infection in humans.

Figure 3
Host factors that interact with HIV

A compelling candidate gene is the Duffy Antigen Receptor for Chemokines (DARC) that binds HIV to the surfaces of erythrocytes and also binds pro-inflammatory cytokines and chemokines. A null mutation (FY -46T→C) that is nearly fixed in subSaharan Africa but is absent elsewhere provides resistance to Plasmodium vivax and P. knowlesi malaria. A provocative study reported that the DARC null genotype was associated with a 40% increase in HIV acquisition and might account for up to 11% of the excess burden of HIV in Africa [23]. Subsequent studies have failed to replicate these results, probably due to inadequate control of population substructure in the original report [24-27].

Chemokines that bind HIV-1 coreceptors potently inhibit HIV-1 replication in vitro and have been shown in multiple genetic association studies to modify HIV infection and pathogenesis. Gene expression of CCL5, a CCR5 ligand, is modulated by interacting regulatory SNPs [28, 29]. Down-regulation of CCL5 levels by these regulatory SNPs are associated with HIV acquisition, progression to AIDS, and higher viral load in both Asian and European populations [28][30]. [31] [32] [33] [34]. These detrimental effects continue even after antiviral treatment [19]. An up-regulating allele (−28G) is associated with favorable outcomes in Asian populations [29, 32, 35]. Supporting the benefit of higher CCL5 levels in resistance to HIV acquisition, CCL5 levels in the genital tracts of EU commercial sex women were found to be 10-fold greater than that of un-exposed uninfected controls [36], attesting to the protective effects of abundant CCR5 ligand levels that compete with HIV for binding sites.

Copy number variation (CNV) is predicted to have a major role in human diseases. CCRL3L1, a ligand for CCR5, is encoded by 1 to 7 duplicated genes. It was reported that lower copy number of CCL3L1 is risk factor for HIV acquisition and AIDS [37] due to lower levels of CCL3L1 available to bind CCR5. Recently, three well-powered, independent studies failed to replicate these results using improved methods to quantify gene copy number [38-40].

The sentry guards: intrinsic HIV-1 restriction factors

The mammalian APOBEC3 protein family has emerged as a key mediator of intrinsic restriction to retroviruses including HIV and HBV (Reviewed in Chiu and Greene [41]). APOBEC3 proteins, a class of cytidine deaminase enzymes, are incorporated into virions. APOBEC3G edit newly synthesized viral DNA by deaminating dC to dU resulting in lethal G-to-A hypermutations. The anti-viral activity of APOBEC3 can also be deaminase-independent, through interference with viral transcription or integration. HIV has clearly out-maneuvered its host—HIV-1 encoded viral infectivity factor (Vif) targets APOBEC3G and 3F for proteasomal degradation through an ubiquitination pathway using the host cellular proteins Cullin5, elongins B and C, and Rbx1 [42] (Figure 3). Seven APOBEC3 family genes (A, B, C, DE, F, G and H) are located in tandem array on chromosome 22q12-q13.2 that spans 150 kb. Their anti-HIV activity and sensitivity to HIV-vif mediated degradation is variable (Table 2).

Table 2
Anti-HIV-1 Profile of APOBEC3 proteins

The role of APOBEC3G genetic variants on HIV-1 infection and progression has been extensively investigated [43-45]. The H186 R allele is associated with CD4 T cell decline and AIDS in homozygotes in Africa Americans [44], with high HIV-1 viral load in Africans with acute infection [46]. Multiple APOBEC3G haplotypes comprising promoter and intronic SNPs influence HIV progression [44]. An intronic SNP C40693T was found to increase risk to HIV-1 infection [43]. Because APOBEC3G is restricted by HIV-vif, it is unlikely that APOBEC3G variation will have a strong influence unless it increases APOBEC3G resistance to HIV-vif mediated degradation.

The impact of the APOBEC3G expression levels on HIV-1 infection and severity of HIV disease has been investigated. Most studies reported a positive correlation between expression levels and favorable outcomes [45, 47-49]. Higher APOBEC3G expression in PBMC and cervical tissues was observed in HIV-exposed seronegative women; their PBMC were also resistant to infection with R5 HIV strains [47]. APOBEC3G mRNA levels range from high to low in non-progressors > exposed uninfected > Progressors, respectively, and were inversely correlated with viral load and positively correlated with CD4+ T cell levels [50]. Taken together, the evidence indicate that APOBEC3 genetic variation and expression levels modify HIV acquisition and disease progression in vivo suggesting that delivery of HIV-vif resistant APOBEC3 protein to HIV-1 susceptible cells could potently restrict HIV replication.

A complete deletion of the ABOBEC3B gene is common with allele frequencies ranging from greater than 27% (Asians) to 4% or less in Africans [51]. APOBEC3B is resistant to HIV-vif mediated degradation and shows moderate to strong anti-HIV activity. A recent study of the effect of the gene deletion on HIV-1/AIDS suggested that the null genotype was associated with greater risk of HIV acquisition, higher viral load, and shorter AIDS-free survival; no effect on HIV/AIDS was noted for heterozygotes [52]. This study warrants further investigation in other populations to confirm the association.

The interaction of HIV-1 Vif and the host factor Cullin5 (CUL5) is critical in disarming APOBEC3 anti-HIV activity. CUL5 SNPs are strongly associated with CD4 T cell loss, one which differentially binds nuclear proteins suggesting altered regulation of CUL5 transcription[53]. A significant interaction was also observed between CUL5 SNPs and APOBEC3G-186R [53]. Cullin5 and APOBEC3G collectively contribute to HIV disease in vivo and provide support for the notion that increasing cellular levels of APOBEC3 protein by interfering with HIV-1 vif-cul5 mediated degradation by drugs targeting this pathway would have therapeutic benefit [53].


TRIM5α play a critical role in the primate anti-viral defense system (Figure 3). Trim5α in the Rhesus monkey completely blocks HIV-1 infection whereas human TRIM5α mediates a low level inhibition to HIV-1 replication [54]. The mechanism of HIV-1 restriction is not fully understood. Proposed models include binding of a multimer of TRIM5α to the incoming viral capsid that leads to premature uncoating of the capsid or by interaction between TRIM5α and the capsid with other undefined cofactors (e.g., CypA) [55]. Using an in vitro assay system, cells were transduced with different TRIM5 missense polymorphisms; R136Q enhanced HIV restriction while H43Y, V112F, and G110E relaxed restriction on HIV replication[56-59] or had no effect (G249D, H419Y) [56-58] (Table 3).

Table 3
In vitro and in vivo effects of TRIM5 variantsa

Because TRIM5 is a compelling candidate gene numerous associations have been done to determine if naturally occurring polymorphisms affects HIV acquisition or progression [56, 57, 59-61]. The results of these studies have been inconsistent, due to the low population frequencies of the missense mutations or small numbers of subjects. One study [61] found a rare haplotype (1.0%) carrying a 5′ untranslated region allele (−2G) and 136Q was significantly higher in HIV-infected persons compared to EU, but this was not replicated in a second study [56]. Notably, the ancestral haplotype carrying more wildtype alleles for 12 SNPs was associated with HIV resistance [56]. SNP 43Y was associated with reduced susceptibility to HIV-1 infection in Asians, a result compatible with the in vitro studies(Table 2) [57]

Even a moderate over expression of wild-type human TRIM5 provides substantial restriction to infection for HIV-1 [62]. It is conceivable that the alleles regulating TRIM5α protein levels can have a major effect on HIV acquisition or HIV replication. Although TRIM5 missense polymorphisms explain little of the variation in European descent populations tested to date, TRIM5 expression might be important in HIV acquisition and should be investigated in diverse human populations. A South African study showed an inverse correlation between Human TRIM5 expression levels in PBMC and susceptibility to HIV infection; HIV-infected persons had lower levels of TRIM5 expression in PBMC compared from EU [63].

Aiding the enemy: cyclophilin facilitates infection

Unlike TRIM5α, Cyclophilin A (CypA) encoded by PPIA is a cellular protein that increases viral infectivity by facilitating proper uncoating of the viral capsid (CA) (Figure 3) [55]. The genetic effects of PPIA stem from regulatory SNPs as no coding SNPs have been found. The promoter SNP1604G affects binding to transcription factor [64], increases HIV-1 infectivity in CD4 T cells [65], and was associated with the faster progression in Swiss and USA cohorts [65] and higher viral RNA levels in drug users in the Amsterdam Cohort [66]. Targeting CA–CypA interaction comprises another anti-HIV approach that is under development [67].

Genome-wide association scans (GWAS)

Although candidate gene studies have been used to identify several AIDS restriction genes (Table 1), GWAS have identified novel SNPs not previously implicated in HIV/AIDS. Four GWAS have been published to date on European or European descent individuals (Table 4) [68-71] . The Limou and Le Clerc studies used selected individuals at the tails of the distribution for AIDS-free survival—very rapid progressors and long term survivors[68, 69]. The Fellay study included participants with longitudinal data and used viral set-point or progression to CD4<350 as outcomes [71] and the Dalmasso GWAS assessed SNP association with plasma HIV-RNA and PBMC HIV-DNA levels during the primary infection [70]. Although the study designs were quite different, the HLA region emerged as the major influence on HIV/AIDS.

Table 4
Summary of GWAS results in association with HIV-1/AIDSa

Few of SNP associations in GWAS studies reached a genome-wide significance of P <5×10−8, but three of the four studies found strong association with rs2395029 (which is near HLA complex P5 (HCP5), a gene localized within the MHC class I region, but is not structurally related to MHC class I genes) [68, 70, 71], rs9264942 (associated with expression levels of HLA-C) and two found associations with ZNDR1 (Zinc ribbon domain-containing protein 1, a DNA-dependent RNA polymerase)[68, 71]; these results clearly implicate chromosome 6p21.33 HLA region as having a major role in control of HIV. HCP5 is near absolute linkage disequilibrum (LD) with HLA-B*57 but the HLA-C SNP does not seem to be tracking through LD other HLA alleles. Although HLA-B*57 is a well-known HIV restriction gene, HLA-C variation was not previously known to have a role in HIV/AIDS. HCP-5/HLA-B*57 and HLA-C explain 14% of the variance in HIV setpoint—a major predictor of AIDS. Forty-four of the top 100 SNPs strongest association in the Fellay [71] study were in the 6p21 HLA region attesting to the importance of HLA.

Other SNP associations with progression rates or viral load were observed in the various studies but were short of genome-wide significance and not replicated among the four GWAS (Table 4). Among the top 100 SNPs in the Fellay study [71] were several biologically plausible genes, including DICER1 (generating miRNA and siRNA, interacting with HIV tat), TRIM39 (same family as anti-HIV factor TRIM5), IF (which regulates the complement cascade), and PROX1 (a negative regulator of IFN-gamma expression in T cells). The role of these SNPs is not yet determined—if these associations are replicated, they will provide new insights into HIV pathogenesis as none of these genes fall in the HLA region or have been previously implicated in HIV/AIDS. Indeed, a multistage GWAS performed in > 700 US seroconverters has recently replicated the strong protective role of PROX1 in AIDS progression [72].

HIV/AIDS GWAS using different study designs and phenotypes have been remarkably consistent in identifying variant alleles in the HLA region as major determinants of HIV viral load and progression. However, they have been less rewarding in revealing new or unexpected alleles or replicating previously published candidate gene associations. This is likely due to limitations inherent to the method. Because of the large number of comparisons and because there is no a priori hypothesis, stringent corrections for multiple tests are required to avoid false positive (type 1) errors—but this comes at the cost of increasing false negative (type 2) errors. The current standard for genome wide significance in GWAS is <5 ×10−8 for p<0.05 or 1 ×10−8 for p<0.01. This stringency provides assurance that implicated SNPs are probable true positives but small to moderate effect alleles, low frequency alleles, and/or recessive alleles are unlikely to be detected simply because of a lack of power due to sample size or to a loss of power due to genotyping or phenotyping errors. With sample sizes less than 1000 in published HIV/AIDS GWAS, only common alleles with moderate to large affect sizes would reach genome wide significance. The published GWAS scans suggest that larger effect alleles other than HLA controlling viral replication are less likely. Future GWAS will most likely identify multiple small effect polymorphisms that explain only a portion of the observed variance but might provide needed insight into biological pathways and provide useful insight for drug or vaccine development. It is also likely that host genetics will explain only a portion of the observed variance in HIV replication and pathogenesis with the remainder being explained by HIV and environmental factors.

The problem of identifying true positive associations among the sea of false negatives is daunting but not hopeless. Meta-analysis using combined phenotype-genotype data from multiple studies should identify new associations that fail to reach genome wide significance in any single study. Collaborative initiatives among investigators with DNA and clinical data are also critical in achieving adequate power and to validate associations through replication. As shown in Table S1, there are numerous case-control and longitudinal HIV/AIDS study groups with a combined enrollment of nearly 20 000 participants. With careful integration of phenotype data plus stringent assessment and correction of population substructure, it should be possible to identify and validate genetic predictors of HIV transmission and acquisition (not yet explored by GWAS); control of viral replication, kinetics of progression, response and adverse events to antiretroviral therapies, and specific AIDS-defining outcomes. In this era of highly affective antiretroviral treatment (HAART) leading to longer AIDS-free survival, there are increasing reports of non-AIDS-defining events (NADE) that include renal disease, diabetes, cardiovascular diseases, and metabolic syndromes leading to morbidity and mortality in HAART–treated cohorts. Predictors of NADEs are also amenable to discovery by GWAS, but, with the exception of HIV-associated nephropathy (HIVAN), have not yet been explored by genome-wide genetic studies.

Admixture mapping identifies MYH9 as a major susceptibility gene for HIV-associated nephropathy

African descent individuals have long been recognized to having a strikingly increased risk for HIV-associated nephropathy; we have previously estimated at 20-fold compared to European descent individuals [73]. Studies of adults and children in the pre-ART era suggest that approximately 10% African Americans developed HIVAN, a leading cause of end stage kidney disease in African Americans. The predilection of African descent individuals to develop HIVAN as well as other major forms of chronic and end stage renal disease suggested that a renal susceptibility locus would be more frequent on an African-origin chromosome and less frequent in non-African-origin chromosomes. Kopp et al. used a genome-wide ancestry admixture mapping strategy to identify a region of excess African ancestry (92%) on chromosome 22 compared to either the genome-wide average of 81% African ancestry or compared to the controls at the same chromosome 22 region [74]. Fine mapping identified MYH9, encoding non-muscle myosin IIA, to be strongly associated with HIV-associated nephropathy (HIVAN) in African Americans (odds ratio 5-7 range, p<10−8, attributable risk =100%). It is notable that in the Kopp et al. study, every individual with HIVAN carried at least one risk haplotype and the number of homozygotes for the risk haplotype was 76% in the HIVAN cases compared to 36% in the controls. The risk alleles are extremely common in African descent individuals (≈60%) and infrequent in non-African descent individuals (<4%) thus explaining a major USA and global health disparity [74]. The prevelance of MYH9-related kidney disease in sub-Saharan Africa is unknown but might be substantial in a setting of untreated HIV-1 infection.

Genome-wide siRNA functional scan

siRNA and small hairpin (sh)RNA knock-down of gene transcription providing a powerful tool to identify genes required for HIV-1 replication [75]. Three studies have used siRNA to knock down one by one ~20 000 genes and then measured the ability of the cells to support transient HIV infection (Table 5). The fourth study [76] used shRNA to chronically silence each of 54 509 mRNAs in Jurket T cell clones (Table 5). Following infection, HIV-1 infected clones that survived represented silenced mRNA required for HIV-replication since active HIV replication leads to Jurket cell death. Notably, although each identified >250 genes as HIV dependency factors, only three genes (MED6, mediator complex subunit 6 that involves in RNA polymerase II transcription, MED7, a cofactor required for Sp1 transcriptional activation and RELA, a component of NF-kappa B complex) overlapped among the four studies and 40 overlapped between 2 out of 4 studies; however, similar pathways were identified by each (Table 6) [75-78].

Table 5
Design of HIV genome-wide siRNA and shRNA screensa
Table 6
Major cellular pathways identified in genomic-wide screens

Differences in gene expression among the different cell lines, HIV strain, timing of infection to assay end-point, as well as off-target gene silencing might account the low level of replication among the RNAi studies. It is also possible that HIV is promiscuous in co-opting host proteins for its life cycle. The results of these studies are hypothesis generating and require further validation; however, they have the potential to identify novel host targets for anti-HIV drug design that, unlike viral genes, cannot develop escape mutations and might not be required for cell survival.

Other high-throughput approaches on functional genomic cDNA screening [79], mRNA profiling [80], proteomic [81], and microRNA (miRNA) [82] have also been applied to unravel the host factors in HIV pathogenesis (Table 5). Notably type III RNAses Dicer and Drosha, PCAF (a histone acetylase associated with p300/CBP nuclear proteins) in the miRNA -silencing machinery was implicated in HIV-1/AIDS [82].

Concluding remarks

After more than two decades of genetic association studies using both candidate and more recently genome-wide association analysis, only a fraction of the variation (≈15-20%) in HIV progression [1] and control of viral set-point [71] has been explained by genetic polymorphisms. The most important predictors are common variation in the chemokine-chemokine nexus involved in cell entry, HLA class I alleles involved in host defenses, and MYH9 for HIVAN.

Figure 4 shows the overlap between different approaches for gene identification. At first glance the lack of replication among the methods is unsettling; however, these methods are each complementary (Table 6, Box 2). The GWAS studies enable comparisons the strength of association across the entire genome and to access the role of both intracellular and extracellular factors in vivo—and to provide quantification of attributable and relative risks important to clinical and public health policies. Functional and positional candidate gene studies have been remarkable successful at identifying rare and common genetic variation associated with HIV/AIDS through direct gene interrogation. Goldstein [83] has argued for a ‘shift from genome scans of ever larger samples to studies of rarer variants of larger effect’. With the advent of next generation sequencing and the careful selection of highly informative individuals from the extremes of the phenotype distribution (e.g. HIV set-point or rate of CD4+ T cell decline), it should be possible to identify rare variants in genes and pathways involved HIV pathogenesis or HIV replication. It should be pointed out that all the GWAS to date have had small samples sizes and were underpowered to detect small effects or infrequent alleles. International consortium pooling resources for large GWAS should identify additional genetic variation associated with HIV/AIDS.

Box 2

Strength and weakness of the approaches to identify host genetic factors

  1. Candidate gene association study
    • Strength
      • Cost-effective method for direct genetic interrogation
      • Prior evidence of biological relevance
      • Hypothesis-driven test
      • Provides estimates of attributable and relative risks
    • Weakness
      • Subject to false positive bias (winner’s curse)
      • Limited to what is already known
  2. GWAS (genome-wide association study)
    • Strength
      • Unbiased, Identifies previously undetected variation
      • Provides estimates of attributable and relative risks
      • Stringent standards for genome-wide significance reduce false positive associations
      • Robust for common alleles (>5%), which tend to have small to moderate effect sizes
    • Weakness
      • High noise to signal ratio: statistically impossible to separate false from true negatives
      • Expensive and requires large sample sizes
      • Does not identify rare mutations
  3. Genome-wide RNA interference (RNAi) screen: Systematically for genes controlling specific biological processes in vivo by silencing each of individual endogenous mRNAs with double-stranded (ds) RNA.
    • Strength
      • Genetic variation not required for gene detection
      • Unbiased
    • Weakness
      • Off-target silencing leading to low replication among studies
      • Cannot identify extracellular factors
      • No quantification of population variation or risk
  4. Whole-genome re-sequencing: Next generation, high throughput sequencing
    • Strength
      • Unbiased detection of all genome or transcriptome sequence variation
    • Weakness
      • Bioinformatics complex
      • Massively parallel coverage to avoid false detection of rare variation
Figure 4
Replication among GWAS, silencing RNA screen, and candidate gene studies

The over-arching goals to prevent new HIV infections and cure established infections require an interdisciplinary, systems-based approach (Figure 5). Integrating knowledge gained from large scale genomic, transcriptomic, proteomic studies using computational modeling should lead to a comprehensive view of host-virus interactions and to the identification of vulnerable pathways that can be exploited for preventive or therapeutic interventions.

Figure 5
Interdisciplinary approaches to identify host factors affecting HIV-1/AIDS

Supplementary Material



We are in debt to Dr. George Nelson for many discussions and critical reading of the manuscript. This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This Research was supported [in part] by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.


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1. O’Brien SJ, Nelson GW. Human genes that limit AIDS. Nature genetics. 2004;36:565–574. [PubMed]
2. Dean M, et al. Genetic restriction of HIV-1 infection and progression to AIDS by a deletion allele of the CKR5 structural gene. Hemophilia Growth and Development Study, Multicenter AIDS Cohort Study, Multicenter Hemophilia Cohort Study, San Francisco City Cohort, ALIVE Study. Science. 1996;273:1856–1862. [PubMed]
3. Hutter G, et al. Long-term control of HIV by CCR5 Delta32/Delta32 stem-cell transplantation. N Engl J Med. 2009;360:692–698. [PubMed]
4. Shacklett BL. Understanding the “lucky few”: the conundrum of HIV-exposed, seronegative individuals. Current HIV/AIDS reports. 2006;3:26–31. [PubMed]
5. Walker BD. Elite control of HIV Infection: implications for vaccines and treatment. Top HIV Med. 2007;15:134–136. [PubMed]
6. Miura T, et al. Genetic characterization of human immunodeficiency virus type 1 in elite controllers: lack of gross genetic defects or common amino acid changes. J Virol. 2008;82:8422–8430. [PMC free article] [PubMed]
7. Carrington M, et al. KIR-HLA intercourse in HIV disease. Trends in microbiology. 2008;16:620–627. [PMC free article] [PubMed]
8. Carrington M, O’Brien SJ. The influence of HLA genotype on AIDS. Annual review of medicine. 2003;54:535–551. [PubMed]
9. Carrington M, et al. HLA and HIV-1: heterozygote advantage and B*35-Cw*04 disadvantage. Science. 1999;283:1748–1752. [PubMed]
10. Gao X, et al. Effect of a single amino acid change in MHC class I molecules on the rate of progression to AIDS. N Engl J Med. 2001;344:1668–1675. [PubMed]
11. Thobakgale CF, et al. Impact of HLA in mother and child on disease progression of pediatric human immunodeficiency virus type 1 infection. J Virol. 2009;83:10234–10244. [PMC free article] [PubMed]
12. Mackelprang RD, et al. Maternal HLA homozygosity and mother-child HLA concordance increase the risk of vertical transmission of HIV-1. J Infect Dis. 2008;197:1156–1161. [PMC free article] [PubMed]
13. Boutwell CL, et al. Reduced viral replication capacity of human immunodeficiency virus type 1 subtype C caused by cytotoxic-T-lymphocyte escape mutations in HLA-B57 epitopes of capsid protein. J Virol. 2009;83:2460–2468. [PMC free article] [PubMed]
14. Colombo S, et al. The HCP5 single-nucleotide polymorphism: a simple screening tool for prediction of hypersensitivity reaction to abacavir. J Infect Dis. 2008;198:864–867. [PubMed]
15. Hyrcza MD, et al. Distinct transcriptional profiles in ex vivo CD4+ and CD8+ T cells are established early in human immunodeficiency virus type 1 infection and are characterized by a chronic interferon response as well as extensive transcriptional changes in CD8+ T cells. J Virol. 2007;81:3477–3486. [PMC free article] [PubMed]
16. Ball TB, et al. Polymorphisms in IRF-1 associated with resistance to HIV-1 infection in highly exposed uninfected Kenyan sex workers. AIDS. 2007;21:1091–1101. [PubMed]
17. Vasilescu A, et al. A haplotype of the human CXCR1 gene protective against rapid disease progression in HIV-1+ patients. Proc Natl Acad Sci U S A. 2007;104:3354–3359. [PMC free article] [PubMed]
18. Ioannidis JP, et al. Effects of CCR5-Delta32, CCR2-64I, and SDF-1 3′A alleles on HIV-1 disease progression: An international meta-analysis of individual-patient data. Annals of internal medicine. 2001;135:782–795. [PubMed]
19. Hendrickson SL, et al. Host genetic influences on highly active antiretroviral therapy efficacy and AIDS-free survival. J Acquir Immune Defic Syndr. 2008;48:263–271. [PubMed]
20. McDermott DH, et al. CCR5 promoter polymorphism and HIV-1 disease progression. Multicenter AIDS Cohort Study (MACS) Lancet. 1998;352:866–870. [PubMed]
21. Martin MP, et al. Genetic acceleration of AIDS progression by a promoter variant of CCR5. Science. 1998;282:1907–1911. [PubMed]
22. An P, et al. Influence of CCR5 promoter haplotypes on AIDS progression in African-Americans. AIDS. 2000;14:2117–2122. [PubMed]
23. He W, et al. Duffy antigen receptor for chemokines mediates trans-infection of HIV-1 from red blood cells to target cells and affects HIV-AIDS susceptibility. Cell host & microbe. 2008;4:52–62. [PMC free article] [PubMed]
24. Horne KC, et al. Duffy antigen polymorphisms do not alter progression of HIV in African Americans in the MAC S cohort. Cell host & microbe. 2009;5:415–417. author reply 418-419. [PubMed]
25. Julg B, et al. Lack of Duffy antigen receptor for chemokines: no influence on HIV disease progression in an African treatment-naive population. Cell host & microbe. 2009;5:413–415. author reply 418-419. [PMC free article] [PubMed]
26. Walley NM, et al. The Duffy antigen receptor for chemokines null promoter variant does not influence HIV-1 acquisition or disease progression. Cell host & microbe. 2009;5:408–410. author reply 418-409. [PMC free article] [PubMed]
27. Winkler CA, et al. Expression of Duffy antigen receptor for chemokines (DARC) has no effect on HIV-1 acquisition or progression to AIDS in African Americans. Cell host & microbe. 2009;5:411–413. author reply 418-419. [PubMed]
28. An P, et al. Modulating influence on HIV/AIDS by interacting RANTES gene variants. Proc Natl Acad Sci U S A. 2002;99:10002–10007. [PMC free article] [PubMed]
29. Liu H, et al. Polymorphism in RANTES chemokine promoter affects HIV-1 disease progression. Proc Natl Acad Sci U S A. 1999;96:4581–4585. [PMC free article] [PubMed]
30. Duggal P, et al. The effect of RANTES chemokine genetic variants on early HIV-1 plasma RNA among African American injection drug users. J Acquir Immune Defic Syndr. 2005;38:584–589. [PubMed]
31. Ahlenstiel G, et al. Distribution and effects of polymorphic RANTES gene alleles in HIV/HCV coinfection -- a prospective cross-sectional study. World J Gastroenterol. 2005;11:7631–7638. [PubMed]
32. Wichukchinda N, et al. Protective effects of IL4-589T and RANTES-28G on HIV-1 disease progression in infected Thai females. AIDS. 2006;20:189–196. [PubMed]
33. Liu XL, et al. [Preliminary study on the association of chemokine RANTES gene polymorphisms with HIV-1 infection in Chinese Han population] Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi. 2003;24:971–975. [PubMed]
34. Rathore A, et al. Association of RANTES -403 G/A, -28 C/G and In1.1 T/C polymorphism with HIV-1 transmission and progression among North Indians. Journal of medical virology. 2008;80:1133–1141. [PubMed]
35. Koizumi Y, et al. RANTES −28G delays and DC-SIGN - 139C enhances AIDS progression in HIV type 1-infected Japanese hemophiliacs. AIDS research and human retroviruses. 2007;23:713–719. [PubMed]
36. Iqbal SM, et al. Elevated T cell counts and RANTES expression in the genital mucosa of HIV-1-resistant Kenyan commercial sex workers. J Infect Dis. 2005;192:728–738. [PubMed]
37. Gonzalez E, et al. The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science. 2005;307:1434–1440. [PubMed]
38. Urban TJ, et al. CCL3L1 and HIV/AIDS susceptibility. Nat Med. 2009;15:1110–1112. [PMC free article] [PubMed]
39. Field SF, et al. Experimental aspects of copy number variant assays at CCL3L1. Nat Med. 2009;15:1115–1117. [PMC free article] [PubMed]
40. Bhattacharya T, et al. Reply to: “CCL3L1 and HIV/AIDS susceptibility” Nat Med. 2009;15:1112–1115. [PubMed]
41. Chiu YL, Greene WC. The APOBEC3 cytidine deaminases: an innate defensive network opposing exogenous retroviruses and endogenous retroelements. Annual review of immunology. 2008;26:317–353. [PubMed]
42. Yu X, et al. Induction of APOBEC3G ubiquitination and degradation by an HIV-1 Vif-Cul5-SCF complex. Science. 2003;302:1056–1060. [PubMed]
43. Valcke HS, et al. APOBEC3G genetic variants and their association with risk of HIV infection in highly exposed Caucasians. AIDS. 2006;20:1984–1986. [PubMed]
44. An P, et al. APOBEC3G genetic variants and their influence on the progression to AIDS. J Virol. 2004;78:11070–11076. [PMC free article] [PubMed]
45. Do H, et al. Exhaustive genotyping of the CEM15 (APOBEC3G) gene and absence of association with AIDS progression in a French cohort. J Infect Dis. 2005;191:159–163. [PubMed]
46. Reddy K, et al. APOBEC3G expression is dysregulated in primary HIV-1 infection and polymorphic variants influence CD4+ T-cell counts and plasma viral load. AIDS. 2010;24:195–204. [PMC free article] [PubMed]
47. Biasin M, et al. Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like 3G: a possible role in the resistance to HIV of HIV-exposed seronegative individuals. J Infect Dis. 2007;195:960–964. [PubMed]
48. Jin X, et al. APOBEC3G levels predict rates of progression to AIDS. Retrovirology. 2007;4:20. [PMC free article] [PubMed]
49. Pace C, et al. Population level analysis of human immunodeficiency virus type 1 hypermutation and its relationship with APOBEC3G and vif genetic variation. J Virol. 2006;80:9259–9269. [PMC free article] [PubMed]
50. Jin X, et al. APOBEC3G/CEM15 (hA3G) mRNA levels associate inversely with human immunodeficiency virus viremia. J Virol. 2005;79:11513–11516. [PMC free article] [PubMed]
51. Kidd JM, et al. Population stratification of a common APOBEC gene deletion polymorphism. PLoS genetics. 2007;3:e63. [PMC free article] [PubMed]
52. An P, et al. APOBEC3B Deletion and Risk of HIV-1 Acquisition. J Infect Dis. 2009;200:1054–1058. [PMC free article] [PubMed]
53. An P, et al. Polymorphisms of CUL5 are associated with CD4+ T cell loss in HIV-1 infected individuals. PLoS genetics. 2007;3:e19. [PMC free article] [PubMed]
54. Stremlau M, et al. The cytoplasmic body component TRIM5alpha restricts HIV-1 infection in Old World monkeys. Nature. 2004;427:848–853. [PubMed]
55. Sokolskaja E, Luban J. Cyclophilin, TRIM5, and innate immunity to HIV-1. Current opinion in microbiology. 2006;9:404–408. [PubMed]
56. Javanbakht H, et al. Effects of human TRIM5alpha polymorphisms on antiretroviral function and susceptibility to human immunodeficiency virus infection. Virology. 2006;354:15–27. [PubMed]
57. Nakajima T, et al. Impact of novel TRIM5alpha variants, Gly110Arg and G176del, on the anti-HIV-1 activity and the susceptibility to HIV-1 infection. AIDS. 2009;23:2091–2100. [PubMed]
58. Sawyer SL, et al. High-frequency persistence of an impaired allele of the retroviral defense gene TRIM5alpha in humans. Curr Biol. 2006;16:95–100. [PubMed]
59. Goldschmidt V, et al. Role of common human TRIM5alpha variants in HIV-1 disease progression. Retrovirology. 2006;3:54. [PMC free article] [PubMed]
60. van Manen D, et al. The effect of Trim5 polymorphisms on the clinical course of HIV-1 infection. PLoS pathogens. 2008;4:e18. [PMC free article] [PubMed]
61. Speelmon EC, et al. Genetic association of the antiviral restriction factor TRIM5alpha with human immunodeficiency virus type 1 infection. J Virol. 2006;80:2463–2471. [PMC free article] [PubMed]
62. Kaumanns P, et al. Human TRIM5alpha mediated restriction of different HIV-1 subtypes and Lv2 sensitive and insensitive HIV-2 variants. Retrovirology. 2006;3:79. [PMC free article] [PubMed]
63. Sewram S, et al. Human TRIM5alpha expression levels and reduced susceptibility to HIV-1 infection. J Infect Dis. 2009;199:1657–1663. [PMC free article] [PubMed]
64. An P, et al. Regulatory polymorphisms in the cyclophilin A gene, PPIA, accelerate progression to AIDS. PLoS pathogens. 2007;3:e88. [PMC free article] [PubMed]
65. Bleiber G, et al. Use of a combined ex vivo/in vivo population approach for screening of human genes involved in the human immunodeficiency virus type 1 life cycle for variants influencing disease progression. J Virol. 2005;79:12674–12680. [PMC free article] [PubMed]
66. Rits MA, et al. Polymorphisms in the regulatory region of the Cyclophilin A gene influence the susceptibility for HIV-1 infection. PloS one. 2008;3:e3975. [PMC free article] [PubMed]
67. Li J, et al. Discovery of dual inhibitors targeting both HIV-1 capsid and human cyclophilin A to inhibit the assembly and uncoating of the viral capsid. Bioorganic & medicinal chemistry. 2009;17:3177–3188. [PubMed]
68. Limou S, et al. Genomewide association study of an AIDS-nonprogression cohort emphasizes the role played by HLA genes (ANRS Genomewide Association Study 02) J Infect Dis. 2009;199:419–426. [PubMed]
69. Le Clerc S, et al. Genomewide association study of a rapid progression cohort identifies new susceptibility alleles for AIDS (ANRS Genomewide Association Study 03) J Infect Dis. 2009;200:1194–1201. [PubMed]
70. Dalmasso C, et al. Distinct genetic loci control plasma HIV-RNA and cellular HIV-DNA levels in HIV-1 infection: the ANRS Genome Wide Association 01 study. PloS one. 2008;3:e3907. [PMC free article] [PubMed]
71. Fellay J, et al. A whole-genome association study of major determinants for host control of HIV-1. Science. 2007;317:944–947. [PMC free article] [PubMed]
72. Herbeck JT, et al. Multi-Stage Genome-Wide Association Study of HIV-1 Disease Progression to Clinical AIDS. J Infect Dis. In press.
73. Kopp JB, Winkler C. HIV-associated nephropathy in African Americans. Kidney international. 2003:S43–49. [PubMed]
74. Kopp JB, et al. MYH9 is a major-effect risk gene for focal segmental glomerulosclerosis. Nature genetics. 2008;40:1175–1184. [PMC free article] [PubMed]
75. Brass AL, et al. Identification of host proteins required for HIV infection through a functional genomic screen. Science. 2008;319:921–926. [PubMed]
76. Yeung ML, et al. A genome-wide short hairpin RNA screening of jurkat T-cells for human proteins contributing to productive HIV-1 replication. J Biol Chem. 2009;284:19463–19473. [PMC free article] [PubMed]
77. Konig R, et al. Global analysis of host-pathogen interactions that regulate early-stage HIV-1 replication. Cell. 2008;135:49–60. [PMC free article] [PubMed]
78. Zhou H, et al. Genome-scale RNAi screen for host factors required for HIV replication. Cell host & microbe. 2008;4:495–504. [PubMed]
79. Nguyen DG, et al. Identification of novel therapeutic targets for HIV infection through functional genomic cDNA screening. Virology. 2007;362:16–25. [PubMed]
80. Imbeault M, et al. Microarray study reveals that HIV-1 induces rapid type-I interferon-dependent p53 mRNA up-regulation in human primary CD4+ T cells. Retrovirology. 2009;6:5. [PMC free article] [PubMed]
81. Chan EY, et al. Dynamic host energetics and cytoskeletal proteomes in human immunodeficiency virus type 1-infected human primary CD4 cells: analysis by multiplexed label-free mass spectrometry. J Virol. 2009;83:9283–9295. [PMC free article] [PubMed]
82. Nathans R, et al. Cellular microRNA and P bodies modulate host-HIV-1 interactions. Mol Cell. 2009;34:696–709. [PMC free article] [PubMed]
83. Goldstein DB. Common genetic variation and human traits. N Engl J Med. 2009;360:1696–1698. [PubMed]
84. Fauci AS, et al. Immunopathogenic mechanisms of HIV infection. Annals of internal medicine. 1996;124:654–663. [PubMed]
85. Bushman FD, et al. Host cell factors in HIV replication: meta-analysis of genome-wide studies. PLoS pathogens. 2009;5:e1000437. [PMC free article] [PubMed]
86. Bashirova AA, et al. Consistent effects of TSG101 genetic variability on multiple outcomes of exposure to human immunodeficiency virus type 1. J Virol. 2006;80:6757–6763. [PMC free article] [PubMed]
87. Catano G, et al. HIV-1 disease-influencing effects associated with ZNRD1, HCP5 and HLA-C alleles are attributable mainly to either HLA-A10 or HLA-B*57 alleles. PloS one. 2008;3:e3636. [PMC free article] [PubMed]
88. Deeks SG, Walker BD. Human immunodeficiency virus controllers: mechanisms of durable virus control in the absence of antiretroviral therapy. Immunity. 2007;27:406–416. [PubMed]
89. Loeuillet C, et al. In vitro whole-genome analysis identifies a susceptibility locus for HIV-1. PLoS biology. 2008;6:e32. [PMC free article] [PubMed]
90. van Manen D, et al. Association of HLA-C and HCP5 gene regions with the clinical course of HIV-1 infection. AIDS. 2009;23:19–28. [PubMed]
91. Lewden C, et al. Comparison of Early CD4 T-Cell Count in HIV-1 Seroconverters in Cote d’Ivoire and France: The ANRS PRIMO-CI and SEROCO Cohorts. J Acquir Immune Defic Syndr. 2009 [PubMed]
92. Oh DY, et al. CCR5Delta32 genotypes in a German HIV-1 seroconverter cohort and report of HIV-1 infection in a CCR5Delta32 homozygous individual. PloS one. 2008;3:e2747. [PMC free article] [PubMed]
93. van Loggerenberg F, et al. Establishing a cohort at high risk of HIV infection in South Africa: challenges and experiences of the CAPRISA 002 acute infection study. PloS one. 2008;3:e1954. [PMC free article] [PubMed]
94. Land AM, et al. Human immunodeficiency virus (HIV) type 1 proviral hypermutation correlates with CD4 count in HIV-infected women from Kenya. J Virol. 2008;82:8172–8182. [PMC free article] [PubMed]
95. Tang J, et al. Human leukocyte antigen class I genotypes in relation to heterosexual HIV type 1 transmission within discordant couples. J Immunol. 2008;181:2626–2635. [PMC free article] [PubMed]
96. Singh KK, et al. Associations of chemokine receptor polymorphisms With HIV-1 mother-to-child transmission in sub-Saharan Africa: possible modulation of genetic effects by antiretrovirals. J Acquir Immune Defic Syndr. 2008;49:259–265. [PMC free article] [PubMed]
97. Smith MW, et al. Contrasting genetic influence of CCR2 and CCR5 variants on HIV-1 infection and disease progression. Hemophilia Growth and Development Study (HGDS), Multicenter AIDS Cohort Study (MACS), Multicenter Hemophilia Cohort Study (MHCS), San Francisco City Cohort (SFCC), ALIVE Study. Science. 1997;277:959–965. [PubMed]
98. Duggal P, et al. Genetic influence of CXCR6 chemokine receptor alleles on PCP-mediated AIDS progression among African Americans. Genes and immunity. 2003;4:245–250. [PubMed]
99. Garcia-Moruja C, et al. Molecular phenotype of CXCL12beta 3′UTR G801A polymorphism (rs1801157) associated to HIV-1 disease progression. Current HIV research. 2009;7:384–389. [PubMed]
100. Winkler C, et al. Genetic restriction of AIDS pathogenesis by an SDF-1 chemokine gene variant. ALIVE Study, Hemophilia Growth and Development Study (HGDS), Multicenter AIDS Cohort Study (MACS), Multicenter Hemophilia Cohort Study (MHCS), San Francisco City Cohort (SFCC) Science. 1998;279:389–393. [PubMed]
101. He W, et al. Reply to: “Experimental aspects of copy number variant assays at CCL3L1” Nat Med. 2009;15:1117–1120. [PubMed]
102. Martin MP, et al. Association of DC-SIGN promoter polymorphism with increased risk for parenteral, but not mucosal, acquisition of human immunodeficiency virus type 1 infection. J Virol. 2004;78:14053–14056. [PMC free article] [PubMed]
103. Shin HD, et al. Genetic restriction of HIV-1 pathogenesis to AIDS by promoter alleles of IL10. Proc Natl Acad Sci U S A. 2000;97:14467–14472. [PMC free article] [PubMed]
104. An P, et al. A tumor necrosis factor-alpha-inducible promoter variant of interferon-gamma accelerates CD4+ T cell depletion in human immunodeficiency virus-1-infected individuals. J Infect Dis. 2003;188:228–231. [PubMed]
105. Kanari Y, et al. Genotypes at chromosome 22q12-13 are associated with HIV-1-exposed but uninfected status in Italians. AIDS. 2005;19:1015–1024. [PubMed]
106. Koning FA, et al. Defining APOBEC3 expression patterns in human tissues and hematopoietic cell subsets. J Virol. 2009;83:9474–9485. [PMC free article] [PubMed]
107. Harari A, et al. Polymorphisms and splice variants influence the antiretroviral activity of human APOBEC3H. J Virol. 2009;83:295–303. [PMC free article] [PubMed]
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