# Functional principal variance component testing for a genetic association study of HIV progression

## TL;DR

This paper introduces a novel functional principal variance component testing framework to analyze the association between genetic markers and HIV progression, capturing nonlinear disease dynamics more effectively than traditional models.

## Contribution

The study develops and validates a new FPVC testing method that improves power and computational efficiency in large-scale genetic association studies of HIV progression.

## Key findings

- FPVC testing shows higher power than linear mixed effects models.
- The method effectively captures nonlinear disease progression patterns.
- Simulation results demonstrate large power gains in detecting genetic associations.

## Abstract

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.