Characterizing associations among multiple single-nucleotide polymorphisms (SNPs) within and across genes, and measures of disease progression or disease status will potentially offer new insight into disease etiology and disease progression. However, several analytical challenges arise due to the existence of multiple potentially informative genetic loci, as well as environmental and demographic factors, and the generally uncharacterized and complex relationships among them. Latent variable modeling offers a natural framework for data arising from these population-based association studies to uncover simultaneous effects of multiple biomarkers. In the first chapter, we describe applications and performance of two such latent variable methods, namely structural equation models (SEMs) and mixed effects models (MEMs), and highlight their theoretical overlap. The relative advantages of each paradigm are investigated through simulation studies and an application to data arising from a study of anti-retroviral-associated dyslipidemia in HIV-1 infected individuals is provided for illustration. In the second chapter, we address a prediction-based classification (PBC) method that allows the use of repeatedly measured biomarkers for CD 4 + T cell outcome prediction through first-stage of fitting MEMs and subsequent classification based on clinical relevant thresholds ( CD 4+ T cell count 200 or 350cells/mm 3 ). Then we apply this PBC approach to a prospective cohort of HIV-1 infected subjects (n=3357) monitored upon anti-retroviral therapy initiation in 7 clinical sites with distinct geographical and socio-economic settings.