  HIV-1C is the most prevalent subtype of HIV-1 and accounts for over half of
HIV-1 infections worldwide. Host genetic influence of HIV infection has been
previously studied in HIV-1B, but little attention has been paid to the more
prevalent subtype C. To understand the role of host genetics in HIV-1C disease
progression, we perform a study to assess the association between
longitudinally collected measures of disease and more than 100,000 genetic
markers located on chromosome 6. The most common approach to analyzing
longitudinal data in this context is linear mixed effects models, which may be
overly simplistic in this case. On the other hand, existing non-parametric
methods may suffer from low power due to high degrees of freedom (DF) and may
be computationally infeasible at the large scale. We propose a functional
principal variance component (FPVC) testing framework which captures the
nonlinearity in the CD4 and viral load with potentially low DF and is fast
enough to carry out thousands or millions of times. The FPVC testing unfolds in
two stages. In the first stage, we summarize the markers of disease progression
according to their major patterns of variation via functional principal
components analysis (FPCA). In the second stage, we employ a simple working
model and variance component testing to examine the association between the
summaries of disease progression and a set of single nucleotide polymorphisms.
We supplement this analysis with simulation results which indicate that FPVC
testing can offer large power gains over the standard linear mixed effects
model.
