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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
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Optimizing ancestral trait reconstruction of large HIV Subtype C datasets through multiple-trait subsampling.
Xingguang Li, Nídia S Trovão1, Joel O Wertheim2
1Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, 31 Center Dr, Bethesda, MA 20892, USA.
Virus Evolution
|December 4, 2023
Summary
Subsampling human immunodeficiency virus Type 1 Subtype C data improves phylodynamic reconstructions by addressing sampling bias. Including all available traits, even if poorly recorded, optimizes datasets for evolutionary and spatio-temporal analyses.
Area of Science:
- * Computational biology and bioinformatics
- * Epidemiology and public health
- * Viral evolution and phylodynamics
Background:
- * Large datasets and sampling bias pose challenges for accurate phylodynamic reconstructions.
- * Heterogeneous data sources and convenience sampling can exacerbate bias in viral datasets.
- * Understanding human immunodeficiency virus Type 1 Subtype C dynamics requires robust analytical methods.
Purpose of the Study:
- * To evaluate the impact of unbalanced sampling on phylodynamic reconstructions of human immunodeficiency virus Type 1 Subtype C.
- * To assess the effectiveness of a comprehensive subsampling strategy in mitigating sampling bias.
- * To determine optimal dataset characteristics for reconstructing viral spatial and risk group dynamics.
Main Methods:
- * Utilized a comprehensive subsampling strategy on human immunodeficiency virus Type 1 Subtype C data.
- * Analyzed sampling distribution across collection date, location, and risk group.
- * Employed phylogenetic comparative methods for ancestral trait reconstruction.
Main Results:
- * Subsampling by all available traits, especially with multigene datasets, yields the most suitable data for ancestral trait reconstruction.
- * Sampling bias is significantly inflated when trait information is unavailable or of poor quality, as seen with the risk group trait.
- * Including poorly recorded traits in subsampling optimizes dataset representativeness compared to their exclusion.
Conclusions:
- * Deliberately including all available traits, even if imperfectly recorded, enhances dataset representativeness for phylodynamic analysis.
- * Subsampling approaches are crucial for optimizing datasets, reducing computational burden, and improving the accuracy of phylodynamic reconstructions.
- * This strategy benefits researchers studying the evolutionary and spatio-temporal patterns of infectious diseases.
