Acquired immunodeficiency syndrome (AIDS) is one of the most deadly diseases worldwide. AIDS was first reported in 1981, with its disease causing virus discovered and isolated two years later (Gottlieb et al., 1981; Barre-Sinoussi et al., 1983; Gallo et al., 1983). Since then, the three decades of research has seen huge progress on many aspects, especially on lengthening the life span of HIV infected patients. Yet today, there are still more than 34 million people living with HIV/AIDS, (http://www.amfar.org/About_HIV_and_AIDS/Facts_and_Stats/Statistics__Worldwide/) and we are no where close to thoroughly understanding the pathogenesis of this virus and to finding an ultimate cure for the disease. Despite the enormous amount of studies, the enigma of HIV infection and how it progresses to AIDS remains elusive.
The progression of HIV infection varies greatly among individuals. Since HIV uses the cellular machinery to replicate, many researchers have been focusing on identifying the host factors that determine the resistance to HIV progression. Numerous genome wide association studies (GWAS) have been conducted to unveil these determining genetic variation and to infer causal genes, but there has been little success. Most GWA studies agree on the significant roles of HLA genes (mainly HLA-B and HLA-C), which are challenging candidates due to their complexity.
In this study, I adopt a novel computational method to identify candidate genes by leveraging the information in GWAS and expression quantitative trait loci (eQTL) data. The combination of GWAS and eQTL reveal several new genes, including MED28, CD151, A4GALT, and ANAPC2, that have never been implicated in previous GWA studies. Substantial literature evidence support the potential roles of these genes. Hypergeometric test between HIV interactome data (Jager et al., 2011), RNAi screens (Brass et al., 2008; Konig et al., 2008; Zhou et al., 2008; Yeung et al., 2009) and my result shows significant overlap.