Abstract

The screening of common genetic polymorphisms among candidate genes for AIDS pathology in HIV exposed cohort populations has led to the description of 20 AIDS restriction genes (ARGs), variants that affect susceptibility to HIV infection or to AIDS progression. The combination of high-throughput genotyping platforms and the recent HapMap annotation of some 3 million human SNP variants has been developed for and applied to gene discovery in complex and multi-factorial diseases. Here, we explore novel computational approaches to ARG discovery which consider interacting analytical models, various genetic influences, and SNP-haplotype/LD structure in AIDS cohort populations to determine if these ARGs could have been discovered using an unbiased genome-wide association approach. The procedures were evaluated by tracking the performance of haplotypes and SNPs within ARG regions to detect genetic association in the same AIDS cohort populations in which the ARGs were originally discovered. The methodology captures the signals of multiple non-independent AIDS-genetic association tests of different disease stages and uses association signal strength (odds ratio or relative hazard), statistical significance (p-values), gene influence, internal replication, and haplotype structure together as a multi-facetted approach to identifying important genetic associations within a deluge of genotyping/test data. The complementary approaches perform rather well and predict the detection of a variety of undiscovered ARGs that affect different stages of HIV/AIDS pathogenesis using genome-wide association analyses.