Genome-wide association study reveals genetic variants associated with HIV-1C infection in a Botswana study population

Although there have been many studies of gene variant association with different stages of HIV/AIDS progression in United States and European cohorts, few gene-association studies have assessed genic determinants in sub-Saharan African populations, which have the highest density of HIV infections worldwide. We carried out genome-wide association studies on 766 study participants at risk for HIV-1 subtype C (HIV-1C) infection in Botswana. Three gene associations (AP3B1, PTPRA, and NEO1) were shown to have significant association with HIV-1C acquisition. Each gene association was replicated within Botswana or in the United States–African American or United States–European American AIDS cohorts or in both. Each associated gene has a prior reported influence on HIV/AIDS pathogenesis. Thirteen previously discovered AIDS restriction genes were further replicated in the Botswana cohorts, extending our confidence in these prior AIDS restriction gene reports. This work presents an early step toward the identification of genetic variants associated with and affecting HIV acquisition or AIDS progression in the understudied HIV-1C afflicted Botswana population.

A novel probabilistic generator for large-scale gene association networks

Motivation Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators—such as GeneNetWeaver—are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. Results We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used “scale-free” model is insufficient for replicating these structures. Availability This generator is implemented in the R package “SeqNet” and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).