Viruses can be considered 'parasites' because they cannot survive outside of a host. The progression rate to AIDS caused by human immunodeficiency virus type-1 (HIV-1) is therefore a consequence of HIV-host cell interactions. In this article, we present an innovative statistical model for detecting the effects of genetic interactions on HIV-1 dynamics triggered by different quantitative trait loci (QTL) from the HIV and human genomes. Our model integrates the principles of functional mapping for longitudinal traits and of linkage disequilibrium analysis for high-resolution mapping of QTL within the maximum likelihood context and is implemented with the EM algorithm. We performed Monte Carlo simulation studies to investigate the impacts of different heritability levels and sample sizes on the power to detect interacting QTL. Our model allows for the tests of a number of clinically meaningful hypotheses and provides a powerful tool for unravelling the genetic architecture of HIV-1 dynamics and therefore AIDS progression rate.
All Science Journal Classification (ASJC) codes
- Statistics and Probability