Viral Genetic Linkage Analysis in the Presence of Missing Data

Abstract

Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering can be related to the potential of such individuals to contribute to the spread of the virus, either directly through transmission to their partners or indirectly through further spread of HIV from those partners. Assessment of the extent to which individual (incident or prevalent) viruses are clustered within a community will be biased if only a subset of subjects are observed, especially if that subset is not representative of the entire HIV infected population. To address this concern, we develop a multiple imputation framework in which missing sequences are imputed based on a model for the diversification of viral genomes. The imputation method decreases the bias in clustering that arises from informative missingness. Data from a household survey conducted in a village in Botswana are used to illustrate these methods. We demonstrate that the multiple imputation approach reduces bias in the overall proportion of clustering due to the presence of missing observations.

Fig 1. Schematic of the sequence-imputation method.

Fig 2. Decrease in overall proportion of clustering with deletion of sequences.

Clustering at each number of deletions is averaged over 100 different random deletions of the same number of sequences.

Fig 3. Demonstrating the corrective effect of a representative imputation.

Deleting 100 sequences leads to noticeably lower clustering. Imputation substantially improves estimates, and results in slightly larger error bars.

Fig 4. Estimating the true proportion of clustering in the Mochudi population.

Treating the observed Mochudi data (N = 371) as a biased sample from the population, and imputing the imputed-population dataset (N = 504) based on Botswana and Mochudi-specific age and gender breakdowns to create a database with the same demographic structure as Botswana as a whole. Point estimates and error bars for the 0.10 and 0.15 thresholds of clustering.