Testing for risk and protective trends in genetic analyses of HIV acquisition

Abstract

Host genetics studies of HIV-1 acquisition are critically important for the identification of new targets for drug and vaccine development. Analyses of such studies typically focus on pairwise comparisons of three different groups: HIV-1 positive individuals, HIV-1 high-risk seronegative individuals, and population controls. Because there is a clear expectation of how gene frequencies of risk or protective alleles would be ordered in the three groups, we are able to construct a statistical framework that offers a consistent increase in power over a wide-range of the magnitude of risk/protective effects. In this paper, we develop tests that constrain the alternative hypothesis to appropriately reflect risk or protective trends jointly across the three groups and show that they lead to a substantial increase in power over the naive pairwise approach. We develop both likelihood-ratio and score statistics that test for genetic effects across the three groups while constraining the alternative hypothesis to reflect biologically motivated trends of risk or protection. The asymptotic distribution of both statistics (likelihood ratio and score) is derived. We investigate the performance of our approach via extensive simulation studies using a biologically motivated model of HIV-1 acquisition, and find that our proposed approach leads to an increase in power of roughly 10-28%. We illustrate our approach with an analysis of the effect of the CCR5Δ32 mutation on HIV acquisition.

Keywords: CCR5; Constrained alternative; HIV-1, Score statistic; High-risk seronegative.

Fig. 1.

Power comparisons of various control sample sizes. For small control samples, the HIV-resistant comparison is the dominant pairwise test, but the three-way comparison is most powerful. As the control sample size increases, the control–resistant test gains in power. In all cases, the three-way analysis is more powerful than the pairwise comparisons with the Bonferroni correction.

Fig. 2.

Power comparisons of various control sample sizes, with a resistant control group. For small control samples, the HIV-resistant comparison is the dominant pairwise test, but the three-way comparison is most powerful. As the control sample size increases, the HIV-control test gains in power. This is in direct contrast to the results obtained using a highly susceptible control group.