Effect of TNF-α genetic variants and CCR5Δ32 on the vulnerability to HIV-1 infection and disease progression in Caucasian Spaniards

<p>Abstract</p> <p>Background</p> <p>Tumor necrosis factor alpha (TNF-α) is thought to be involved in the various immunogenetic events that influence HIV-1 infection.</p> <p>Methods</p> <p>We aimed to determine whether carriage of the <it>TNF-α-238G>A, -308G>A </it>and <it>-863 C>A </it>gene promoter single nucleotide polymorphisms (SNP) and the <it>CCR5Δ32 </it>variant allele influence the risk of HIV-1 infection and disease progression in Caucasian Spaniards. The study group consisted of 423 individuals. Of these, 239 were uninfected (36 heavily exposed but uninfected [EU] and 203 healthy controls [HC]) and 184 were HIV-1-infected (109 typical progressors [TP] and 75 long-term nonprogressors [LTNP] of over 16 years' duration). <it>TNF-α </it>SNP and the <it>CCR5Δ32 </it>allele were assessed using PCR-RFLP and automatic sequencing analysis methods on white blood cell DNA. Genotype and allele frequencies were compared using the χ 2 test and the Fisher exact test. Haplotypes were compared by logistic regression analysis.</p> <p>Results</p> <p>The distribution of <it>TNF-α-238G>A, -308G>A </it>and <it>-863 C>A </it>genetic variants was non-significantly different in HIV-1-infected patients compared with uninfected individuals: <it>-238G>A</it>, p = 0.7 and p = 0.3; <it>-308G>A</it>, p = 0.05 and p = 0.07; <it>-863 C>A</it>, p = 0.7 and p = 0.4, for genotype and allele comparisons, respectively. Haplotype analyses, however, indicated that carriers of the haplotype H3 were significantly more common among uninfected subjects (p = 0.04). Among the infected patients, the distribution of the three <it>TNF-α </it>genetic variants assessed was non-significantly different between TP and LTNP: <it>-238G>A</it>, p = 0.35 and p = 0.7; <it>-308G>A</it>, p = 0.7 and p = 0.6: <it>-863 C>A</it>, p = 0.2 and p = 0.2, for genotype and allele comparisons, respectively. Haplotype analyses also indicated non-significant associations. Subanalyses in the LTNP subset indicated that the <it>TNF-α-238A </it>variant allele was significantly overrepresented in patients who spontaneously controlled plasma viremia compared with those who had a detectable plasma viral load (genotype comparisons, p = 0.02; allele comparisons, p = 0.03). The <it>CCR5Δ32 </it>distribution was non-significantly different in HIV-1-infected patients with respect to the uninfected population (p = 0.15 and p = 0.2 for genotype and allele comparisons, respectively) and in LTNP vs TP (p = 0.4 and p = 0.5 for genotype and allele comparisons, respectively).</p> <p>Conclusions</p> <p>In our cohort of Caucasian Spaniards, <it>TNF-α </it>genetic variants could be involved in the vulnerability to HIV-1 infection. <it>TNF-α </it>genetic variants were unrelated to disease progression in infected subjects. The <it>-238G>A </it>SNP may modulate the control of viremia in LTNP. Carriage of the <it>CCR5Δ32 </it>variant allele had no effect on the risk of infection and disease progression.</p

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p &lt; 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p &lt; 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes