The impact of host genetic variation on infection with HIV-1

Abstract

The outcome after infection with the human immunodeficiency virus type 1 (HIV-1) is a complex phenotype determined by interactions among the pathogen, the human host and the surrounding environment. An impact of host genetic variation on HIV-1 susceptibility was identified early in the pandemic, with a major role attributed to the genes encoding class I human leukocyte antigens (HLA) and the chemokine receptor CCR5. Studies using genome-wide data sets have underscored the strength of these associations relative to variants located throughout the rest of the genome. However, the extent to which additional polymorphisms influence HIV-1 disease progression, and how much of the variability in outcome can be attributed to host genetics, remain largely unclear. Here we discuss findings concerning the functional impact of associated variants, outline methods for quantifying the host genetic component and examine how available genome-wide data sets may be leveraged to discover gene variants that affect the outcome of HIV-1 infection.

Figure 1

Opportunities and obstacles for human genetic studies of HIV-1 outcomes. (a) Genetic studies of pre-infection phenotypes have focused on susceptibility by comparing samples from HIV-1–infected people to the general population or to cohorts of high-risk exposed-uninfected individuals. Studies of disease progression in infected groups have commonly used set-point viral load, rate of CD4+ T cell decline and time to AIDS or death as markers of progression. Other phenotypes with potential impact on susceptibility to infection or severity of disease—including degree of immune activation, magnitude of acute viremia and the size of the latent reservoir—may also be informative. (b) A commonly proposed model of genetic architecture of disease traits in which variant frequency is inversely correlated with variant impact. Common variants (>1% frequency) of moderate to low effect size can be detected in large patient samples by GWAS using genotyping arrays. Rare variants (<1%) can be detected by sequencing studies that provide base-pair–level resolution. (c) Curves showing the sample size required for 80% power to detect common (5%, 10% and 30%) and rare (1% and 0.5%) genetic variants across a range of effect sizes (odds ratio) at genome-wide significance (P < 5 × 10⁻8). Curves were modeled on case-control studies assuming an equally distributed sample (n cases = n controls) and a trait prevalence of 5% (the approximate population proportion of viremic controllers). The diamonds indicate the properties of the well-described effects of HLA-B*57:01 (5% population frequency) and CCR5Δ32 heterozygosity (10% population frequency) with odds ratios as reported in HIV-1 controllers. The dashed line is set at a sample size of 2,500, the largest published GWAS of HIV-1 progression phenotypes to date. Variants affecting HIV-1 progression with properties falling above the dashed line would not have been detected by current studies.

Figure 2

Approaches to supporting a direct functional impact of HLA-C expression on HIV-1 disease outcome. (a) Higher expression of HLA-C associates with protection against HIV-1 in African and European Americans despite distinct HLA-C allele frequencies and linkage disequilibrium patterns with HLA-B in these two groups. (b) MIR148A insertion and deletion polymorphism associates with different expression levels, affecting the extent to which HLA-C is downregulated and degree of HIV-1 control in individuals carrying an intact miR-148a binding site in the HLA-C 3’ UTR. The MIR148A variant has no effect on HIV-1 control in those carrying HLA-C alleles that escape miR-148a regulation. (c) Higher expression of HLA-C on HIV-1–infected targets results in a greater frequency of CTL responses to specific HLA-C–restricted peptides.

Figure 3

HIV-1–dependency genes identified by genome-wide short interfering RNA (siRNA) screens are relatively conserved. (a) Selection as measured by the ratio of nonsynonymous substitutions per nonsynonymous site (d_N) to synonymous substitutions per synonymous site (d_S) across primate species for 99 high-confidence HIV-1 dependency factors (HDFs) (dashed line) and a background set of 16,618 genes (solid line). HDFs are relatively depleted of nonsynonymous variation (Wilcoxon rank sum P = 0.01), suggesting conservation across species. (b) Example plot summarizing human variation across the HDF PARP9 in a set of ~6,300 individuals from the NHLBI ESP. The protein background (gray box) is plotted with vertical bars indicating amino acid location of protein-modifying variants (blue, missense; red, stop-gain (nonsense); yellow, frameshift). Amino acid locations of stop-gain and frameship variants are given below the protein background Top, bars indicate the frequency of each plotted variant (green, rare variants; purple, common variants). This variation could be exploited to test the importance of the encoding gene in HIV-1 replication ex vivo. Protein plot was generated by the GuavaH web tool. MAF, minor allele frequency.