Optimizing ancestral trait reconstruction of large HIV Subtype C datasets through multiple-trait subsampling

Abstract

Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence of unbalanced sampled distribution by collection date, location, and risk group of human immunodeficiency virus Type 1 Subtype C using a comprehensive subsampling strategy and assess their impact on the reconstruction of the viral spatial and risk group dynamics using phylogenetic comparative methods. Our study shows that a most suitable dataset for ancestral trait reconstruction can be obtained through subsampling by all available traits, particularly using multigene datasets. We also demonstrate that sampling bias is inflated when considerable information for a given trait is unavailable or of poor quality, as we observed for the trait risk group. In conclusion, we suggest that, even if traits are not well recorded, including them deliberately optimizes the representativeness of the original dataset rather than completely excluding them. Therefore, we advise the inclusion of as many traits as possible with the aid of subsampling approaches in order to optimize the dataset for phylodynamic analysis while reducing the computational burden. This will benefit research communities investigating the evolutionary and spatio-temporal patterns of infectious diseases.

The emergence and circulation of human immunodeficiency virus (HIV)-1 subtype C

“…We were also able to capture the highest number of transmission events when analysing locrisk626, indicating that subsampling by date, country and risk group might produce the best dataset for reconstructing the transmission dynamics of HIV-1 subtype C while curbing potential sampling biases, which is in line with previous findings [25, 35, 36].…”

“…Further subsampling was performed with Sequence sampling tool for phylogenetics (SAMPI) [24] to obtain a homogenous collection of samples per country, risk group and date (date, country and risk group; date and country; and date and risk group), as described elsewhere [25]. This resulted in full genome datasets of 626 sequences subsampled based on sampling location, risk group and collection date (locrisk626); 562 sequences subsampled based on sampling location and collection date (loc562); and 393 sequences subsampled based on risk group and collection date (risk393).…”

Quantifying the genomic determinants of fitness inE. coliST131 using phylodynamics

“…In addition to fitness, pathogen phylogenies can be strongly shaped by sampling biases [64][65][66]. The major risk here is that oversampling of certain genotypes or lineages could produce a higher branching rate that would be difficult to distinguish from higher pathogen fitness [67][68][69].…”