Statistical methods for analyzing deep sequencing data in HIV-1 prevention trial sieve analyses

Abstract

Understanding how vaccines perform against different pathogen genotypes is crucial for developing effective prevention strategies, particularly for highly genetically diverse pathogens like human immunodeficiency virus-1 (HIV-1). Sieve analysis is a statistical framework used to determine whether a vaccine selectively prevents infection by certain genotypes while allowing breakthrough of other genotypes that evade immune responses. Traditionally, these analyses are conducted with a single sequence available per individual acquiring the pathogen. However, modern deep sequencing technology can provide detailed characterization of within-host viral diversity by capturing up to hundreds of pathogen sequences per person. In this dissertation, we develop statistical methods for conducting sieve analyses using deep sequencing data. Our objective is to define and study estimands that summarize within-host viral diversity and enable comparisons across treatment groups. When viral variants are characterized by a feature of interest, the within-host viral population induces a distribution over the possible values of that feature. Because only a finite number of sequences is observed for each person, the data provide a sample from this unobserved individual-level distribution. One key issue is that the number of observed sequences, called sequencing depth, can vary across individuals and may be low for some. Throughout, we consider how low and variable sequencing depth affects estimation and inference and propose methods to address these effects. We show that incorporating deep sequencing data into sieve analyses has the potential to reveal patterns missed by single-sequence analyses by providing a more complete characterization of each individual’s viral population.