FUNCTIONAL PRINCIPAL VARIANCE COMPONENT TESTING FOR A GENETIC ASSOCIATION STUDY OF HIV PROGRESSION

Area of Science:

• Genetics and Genomics
• Immunology
• Epidemiology

Background:

• Human Immunodeficiency Virus type 1 subtype C (HIV-1C) is the most common globally, yet host genetic factors influencing its disease progression are understudied.
• Previous genetic association studies primarily focused on HIV-1B, leaving a gap in understanding genetic influences on the prevalent HIV-1C.
• Standard statistical models for longitudinal data may oversimplify the complex, nonlinear patterns of disease progression markers like CD4 counts and viral load.

Purpose of the Study:

• To investigate the association between host genetic markers on chromosome 6 and the progression of HIV-1C disease.
• To develop and validate a novel statistical framework capable of analyzing complex longitudinal disease data and genome-wide genetic markers.
• To address limitations of existing methods in handling nonlinear disease trajectories and large-scale genetic data for HIV-1C.

Main Methods:

• Proposed a two-stage functional principal variance component (FPVC) testing framework.
• Stage 1: Functional principal components analysis (FPCA) to summarize major variation patterns in longitudinal CD4 and viral load data.
• Stage 2: Variance component testing to assess the association between summarized disease progression and single nucleotide polymorphisms (SNPs).

Main Results:

• The FPVC framework effectively captures nonlinear disease progression with low degrees of freedom.
• Simulations demonstrate that FPVC testing provides significant power gains compared to standard linear mixed effects models.
• The method is computationally efficient, suitable for genome-wide association studies.

Conclusions:

• The FPVC testing framework is a powerful and efficient tool for analyzing host genetic associations with HIV-1C disease progression.
• This approach offers improved statistical power for detecting genetic influences on complex, longitudinal disease trajectories.
• The findings pave the way for a deeper understanding of genetic determinants in HIV-1C infection and potential therapeutic targets.