Back

Understanding Plasmodium vivax recurrent infections using an amplicon deep sequencing assay, PvAmpSeq, identity-by-descent and model-based classification

Rosado, J.; Han, J.; Obadia, T.; Munro, J.; Traore, Z.; Schoffer, K.; Brewster, J.; Bourke, C.; Vinetz, J. M.; White, M.; Bahlo, M.; Gamboa, D.; Mueller, I.; Ruybal-Pesantez, S.

2025-05-26 infectious diseases
10.1101/2025.05.26.25327775 medRxiv
Show abstract

Plasmodium vivax infections are characterised by recurrent bouts of blood-stage parasitaemia. Understanding the genetic relatedness of recurrences can help distinguish whether these are caused by relapse, reinfection, or recrudescence, which is critical to understand treatment efficacy and transmission dynamics. We developed PvAmpseq, an amplicon sequencing assay targeting 11 SNP-rich regions of the P. vivax genome. PvAmpSeq was applied to field isolates from a clinical trial in the Solomon Islands and a longitudinal observational cohort in Peru, and statistical models were applied for genetic classification of recurrences. In the Solomon Islands trial, where participants received antimalarials at baseline, half of the recurrent infections were caused by parasites with >50% relatedness to the baseline infection (identity-by-descent), with statistical models providing further classification as probable relapses and recrudescences, although with wide uncertainty. In the Peruvian cohort, half of the recurrent infections were caused by parasites with >22% relatedness to the baseline infection. PvAmpSeq provides high-resolution genotyping to characterise P. vivax recurrences, offering insights into transmission and treatment outcomes. We also discuss the nuances and limitations of available statistical methods for the classification of P. vivax genotyping data. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/25327775v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@f888eaorg.highwire.dtl.DTLVardef@e4e327org.highwire.dtl.DTLVardef@fd2549org.highwire.dtl.DTLVardef@19aff01_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Rosado, J. (2026) https://BioRender.com/p66i686

Published in iScience (predicted rank #20) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.